This year at 麻豆原创 Sapphire, we shared our vision for the Autonomous Enterprise — the next evolution of how businesses run, where AI agents execute critical workflows so people can focus on innovation, customer value, and business growth.

This vision comes to life through a reimagined Joule Work, evolving from an AI assistant into the central workspace for enterprise AI. We also introduced the 麻豆原创 Autonomous Suite, bringing AI agents and assistants across core business functions to execute complex workflows with human oversight. With 麻豆原创 Business AI Platform, customers and partners can build, manage, and govern AI agents. And by expanding Industry AI, we’re delivering AI grounded in deep business context and domain expertise to solve industry-specific challenges.

Customers are already benefiting. IT division, Bosch Digital, integrated 麻豆原创 Joule for Developers directly into their coding workflows. Developers saw a 20% increase in productivity using Joule to automate routine coding tasks and optimize code. Joule also generates test cases, speeding up unit testing by 15% to 20% and freeing senior developers for high-value tasks. , the country鈥檚 leading airport operator, defines safety thresholds, service levels, and playbooks, and its agent, Smart Network for Operative Winter (SNOW), executes them. The SNOW agent is a winter operations system that integrates real-time weather, runway, and operations/maintenance data to automatically orchestrate work at Patagonian airports. The agent has improved runway safety, cut direct costs by 16%, and reduced administrative effort by 90%.

built a tool using  so its clients can better handle international tax rules by developing and managing their own custom AI agents and solutions. This way, PwC鈥檚 clients can focus on strategy while AI handles tax. PwC鈥檚 tool helped one pharmaceutical company handle VAT on international transfers 60% more efficiently.

Another customer, , a global fashion retailer, used an AI agent, built on 麻豆原创 Joule, to cut HR process cycle times by 40% to 60%. The agent helps employees quickly handle HR transactions, such as leave requests and payroll queries, through natural language conversations. Reducing time spent on administrative tasks allows HR teams to focus on strategic talent management. These are just some of the customers getting value. There are many more.

Now let鈥檚 dive into the releases from Q2 2026.

Please note that this article covers only AI offerings released from April 1, 2026, to June 30, 2026.

Joule

Joule Work
麻豆原创 Early Adopter Care program (registrations closed)

redefines how people interact with and execute end-to-end business processes. As the user engagement component of the Joule solution, it moves the user experience beyond fragmented, transactional interfaces toward a unified, intelligent way of working across 麻豆原创 and non-麻豆原创 systems. Its dynamic workspace adapts to users’ intent, helping them focus on outcomes rather than spending time finding information. And because it can delegate execution to AI, users will no longer need to coordinate work across multiple application interfaces manually.

Joule Work will allow users to express in natural language what they want to accomplish, triggering to coordinate teams of Joule Agents that will surface the right insights and automate routine work across business domains and systems to achieve the goal. This happens in intent-driven, adaptive workspaces built in real time that keep teams focused on driving decisions and impact. Joule Work can help reduce manual handoffs, shorten cycle times, and enable teams to turn decisions into actions faster. A key function of Joule Work is to connect users with Joule Assistants, which are like smart teammates organized by function. These assistants use context to intuit people鈥檚 intent and act by coordinating the appropriate Joule Agents across the business. Joule Assistants understand organizations deeply and can automate complex tasks within and across functions, freeing employees to address more strategic work.

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Joule Work mobile app
General availability

Employees who use 麻豆原创 on the go can rely on the Joule Work mobile app to interact with 麻豆原创 applications in natural language on their smartphones or tablets. Joule is integrated directly into the app, so a simple chat can surface the latest figures, help complete approvals or maintenance tasks, and support work across areas such as sales, HR, and supply chain processes without having to navigate multiple mobile apps. On iPhone and iPad, users can even start by saying 鈥淗ey Siri, ask Joule in Joule Work,鈥� then speak their question, which is passed straight to Joule for a response. This gives organizations a single, mobile-enabled entry point to Joule capabilities and lets employees gain insights and act on tasks across their 麻豆原创 solutions using everyday language.

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Voice for Joule
麻豆原创 Early Adopter Care program (registrations closed)

A new partnership with LiveKit delivers intelligent voice for Joule, extending the experience beyond the keyboard and into settings where work happens. This partnership helps advance 麻豆原创’s vision for the Autonomous Enterprise. With LiveKit, 麻豆原创 customers can use real-time voice capabilities in Joule and access reliable, always-on conversational AI. This brings voice AI to a full range of roles, devices, and environments, putting Joule within reach of employees whose work is done away from a keyboard.

Enhancements for Joule
Multi-system support for 麻豆原创 S/4HANA Cloud Editions

Joule now supports connecting multiple 麻豆原创 S/4HANA Cloud Private Edition systems or clients and multiple 麻豆原创 S/4HANA Cloud Public Edition systems within a single Joule formation.

Work seamlessly across different 麻豆原创 S/4HANA environments through one unified Joule interface, increasing flexibility and efficiency for organizations operating multiple systems. Administrators enable this feature by configuring system-specific destinations with naming conventions, including additional systems in the Joule formation via System Landscape, and mapping system identifiers in the Joule Admin Center.

Developers can build custom capabilities that leverage data and functionality from multiple back-end systems. Business users access and execute processes across all connected systems naturally within their workflow.

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Autonomous SCM

麻豆原创 Digital Manufacturing, AI-assisted production engineering
General availability

Production engineers can analyze error logs to identify root causes and generate resolution instructions for production processes using 麻豆原创 Digital Manufacturing. The feature also enables engineers to extend production processes via script tasks generated based on natural language input.

Organizations can reduce error analysis time for production process errors by 20%, reduce error analysis time for connectivity errors by 20%, and cut the time to handle a production process or connectivity error from 4.5 to 3.6 hours — while improving operating time from 92% to 92.92%.

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麻豆原创 Digital Manufacturing, AI-assisted description enhancement
General availability

Quality managers can generate clearer and more structured initial descriptions of complex issues using 麻豆原创 Digital Manufacturing. By reducing bias and subjective language, a more balanced and factual representation of the problem at hand is created. Users can also refine and rephrase initial rough descriptions, facilitating more effective follow-up and thorough investigation, and translate descriptions into different languages.

This offers organizations an up to five percent improvement in the efficiency of quality engineers during issue handling and resolution, and an up to 10% reduction in errors during problem handling.

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Autonomous Finance

Project Billing Price Verification Agent
Beta release

Billing specialists can quickly identify mismatches between agreed prices and billing amounts using the Project Billing Price Verification Agent in the manage project billing application of 麻豆原创 S/4HANA Cloud Public Edition.

The agent identifies the relevant contracts and statements of work for the related customer project, extracts key pricing data, and compares them with the values in the project billing request. It highlights discrepancies, provides context, and suggests corrective actions.

Organizations can reduce time spent resolving price discrepancies by 75%, cut revenue leakage from undetected incorrect billing by 75%, and improve cash flow while reducing days sales outstanding by fewer billing cycle delays.

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麻豆原创 S/4HANA Cloud Public Edition, AI-assisted localized business data management
General availability

Accounts receivable managers can access and explore localized financial and logistics data through Joule using natural language, without leaving their daily workflows. The capability enables users to run complex reports using natural language instead of manual selection screens, and instantly filter, navigate, and explore results with AI-supported context awareness. This way, finance teams can reduce the amount of training effort required and increase productivity and confidence across the organization.

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Autonomous Spend

Expense Automation Agent
General availability

Expense Automation Agent helps employees who submit business trip expenses by creating a first draft of their expense reports. It automatically collects and adds transactions, fills in relevant fields using contextual information and past behavior, and lets employees quickly review and adjust before submission. Customers can reduce manual data entry, shorten report completion time by up to 30%, and allow employees to focus more on their core work.

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麻豆原创 Ariba Contracts, AI-assisted contract creation from documents and prompts
General availability

Contract managers who create and manage large volumes of agreements can use AI鈥慳ssisted contract creation from prompts to start new contracts more efficiently. The feature lets users enter a simple natural-language prompt directly in the contract creation flow, then proposes contract header fields in seconds for review and confirmation before finalization. Organizations can reduce the effort required to initiate contracts, provide a guided in鈥慶ontext experience, and build a scalable foundation for future AI capabilities while maintaining clear human oversight of each contract.

麻豆原创 Fieldglass, AI-assisted SOW worker role recommendations
General availability

Procurement specialists who manage statements of work can now define suitable worker roles more quickly. This feature applies generative AI to the SOW context, including scope, outcomes, and timelines, to propose relevant roles that users can review and refine. Organizations benefit from faster, more consistent SOW authoring, improved fit鈥憈o鈥憇cope, and clearer, better-governed worker role definitions.

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麻豆原创 Ariba Invoicing, AI-assisted multi-model data extraction
General availability

Procurement and accounts payable teams working in 麻豆原创 Ariba Invoicing can rely on multi鈥憁odel data extraction to capture invoice information more accurately. The feature leverages the latest large language models in the content extraction service to interpret and extract key invoice data, enabling a smoother capture process. Organizations gain a more reliable and efficient invoice processing experience, with improved data quality that helps reduce manual corrections and downstream errors.

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Concur Travel, AI-assisted policy rule generator
麻豆原创 Early Adopter Care

Travel program administrators who manage Concur Travel policies can set up and adjust travel rules more efficiently with the policy rule generator. By pasting existing policy text into an AI-based rule generator, they can automatically produce multiple rule classes and rules in a single step, then apply them via a guided wizard. Organizations save time on policy implementation, reduce configuration errors, and promote more consistent, compliant travel policies across their programs.

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Autonomous CX

Order Reliability Agent
Beta release

The Order Reliability Agent helps customer service and order management teams stay on top of order issues consistently. The agent continuously monitors orders in 麻豆原创 Order Management Services, detects risks such as failures or delays, and either takes automated corrective action or presents clear recommendations and root-cause insights for staff to review. Companies can cut the time spent analyzing and handling exceptional orders by around half. The agent can also reduce customer churn related to fulfillment problems by about 20%, helping create a more reliable order experience.

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麻豆原创 Revenue Growth Management, AI-assisted trade promotion creation
General availability

Key account managers who plan trade promotions in 麻豆原创 Revenue Growth Management can set up promotions more quickly. When they enter a promotion name in the relevant account context, the system proposes key details such as dates, promotion type, duration, and sell鈥慽n timing based on master data, historical promotions, and past user edits. Organizations can shorten promotion setup time by up to 75% and reduce data鈥慹ntry errors and rework by around 30%, improving both efficiency and consistency in promotion planning.

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麻豆原创 Revenue Growth Management, AI-assisted deal sheet generation
General availability

Key account managers can quickly turn promotion data into retailer-ready deal sheets. Starting from a single promotion, the feature fills in system-of-record fields, applies appropriate PDF or Excel templates, and checks that required information is present before the document is created. This helps organizations produce consistent, audit-ready deal sheets in seconds, reduce formatting and data-entry errors, and give account teams more time to focus on customer negotiations rather than document preparation.

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Joule with 麻豆原创 Order Management Services
Beta release

Operations managers and order management teams using 麻豆原创 Order Management Services can rely on Joule to handle everyday operational questions and tasks through simple natural language. By enabling conversational access to key data and actions across areas such as order processing, orchestration, sourcing, availability, returns, and flows, Joule provides real-time, role-aware guidance directly in the flow of work. Organizations benefit from faster access to relevant transactions and insights, can act earlier to prevent issues from escalating, and support smarter, more timely decisions that save both time and operational cost.

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麻豆原创 Engagement Cloud, email campaign duplication
麻豆原创 Early Adopter Care program

Marketing teams can duplicate existing email campaigns to speed up everyday execution. When a marketer copies a previous campaign, email campaign duplication carries over layout, branding, and technical settings, so they only need to update content such as copy or offers. This helps organizations reduce campaign setup time, keep branding and formatting consistent, and limit repetitive configuration work and related errors across channels.

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麻豆原创 Business AI Platform

Build

Joule Studio
麻豆原创 Early Adopter Care

The new Joule Studio gives product teams, architects, and developers a single place to extend, build, and integrate AI experiences from business intent through to production-ready solutions. It starts from the outcomes you want to achieve, uses your own processes and data for context, and connects out of the box across your application landscape. At the same time, it can generate product requirements and technical specifications from your company-specific context, apply eval-based, data-driven guardrails to AI coding assistants under 麻豆原创-managed enterprise controls, and remain open so you can work with third-party or 麻豆原创 models in the development environment that fits your needs.

There will be a migration path from the original Joule Studio to the new version to help customers transition without disruption.

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麻豆原创 S/4HANA custom code migration agent
General availability

ABAP developers and migration teams moving from 麻豆原创 ECC to 麻豆原创 S/4HANA can use the 麻豆原创 S/4HANA custom code migration agent to automate the complex, time-consuming process of migrating custom ABAP code from 麻豆原创 ECC to 麻豆原创 S/4HANA. Delivered as an agentic AI capability in 麻豆原创 Joule for Developers, ABAP AI, the agent runs 麻豆原创 S/4HANA readiness checks via ABAP test cockpit across entire custom code packages, interprets the findings, categorizes issues, and applies a mix of deterministic quick fixes and AI-based code changes with confidence scores, while recording all updates in transport requests for full traceability. High-confidence fixes are applied automatically, and lower-confidence proposals are added as comments for developer review, so teams retain control over final code quality while spending far less time on object-by-object analysis, freeing capacity for higher-value design decisions and overall migration governance.

and .

Contextualize and Reason

Generative AI hub, enhancements

The generative AI hub in the 麻豆原创 AI Core infrastructure integrates with hyperscaler-agnostic operations to improve accuracy and support enterprise-wide adoption of business AI.

Batch API enabling processing of high鈥憊olume foundational model (FM) workloads
Developers and platform teams working with 麻豆原创 AI Core can use the batch API to process high-volume foundational model workloads more efficiently. By submitting large collections of non-urgent AI requests as a single input file, they can run jobs asynchronously in the background. At the same time, 麻豆原创 AI Core writes results to an object store, ensuring real-time, fast, and responsive user experiences. This improves scalability for high-volume processing, simplifies the developer experience across different models and providers, and ensures fair, predictable throughput so that large jobs do not block smaller ones.

Inference observability service: centralized logging and feedback for generative AI workloads
Teams running generative AI workloads on 麻豆原创 AI Core can use the inference observability service to monitor, analyze, and systematically improve model outputs. The service centrally records prompts, responses, and key context with controlled overhead, lets developers and users rate the quality of each response, and attaches lightweight feedback. It also supports labels and filters so interactions can be easily discovered and exported as datasets for fine鈥憈uning, prompt engineering, and benchmarking. Organizations gain standardized transparency into prompt and response quality, reduce costs and effort by replacing ad hoc logging with a unified, compliant feedback channel, and accelerate iterative improvement using structured inference data stored in S3 or metadata-only mode, and managed via REST APIs for labels, feedback, and record retrieval.

Speech-to-speech
The availability of speech鈥憈o鈥憇peech (S2S) recognition helps agent and app developers build natural, end鈥憈o鈥慹nd voice experiences into 麻豆原创 applications.

New models available
New models are supported, including Gemini 3.1 Flash Lite, Claude Opus 4.7, GPT Realtime and Mistral Small, GPT 5.4, GPT 5.4-nano, and GPT 5.3-Codex.

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麻豆原创 Joule for Consultants, enhancements

Custom knowledge grounding connects 麻豆原创 Joule for Consultants to a separate grounding service that indexes organizational content via 麻豆原创 AI Core. It enables Joule to securely index and reference an organization鈥檚 methodologies, policies, templates, and delivery standards alongside 麻豆原创鈥慶urated knowledge. By grounding responses in company鈥憇pecific documentation, consultants can receive more accurate guidance that aligns with established ways of working across projects and engagements.

Expert workspace introduces personalized 鈥渆xperts鈥� that help tailor guidance for specific projects, domains, or workstreams. Context is retained across conversations, so users can switch between initiatives while preserving project-specific knowledge.

and .

麻豆原创 Document AI enhancements

Model selection
This new feature allows you to choose the large language model (LLM) used for document processing. The Default LLM reflects the best-performing model at any given time, while additional models such as Gemini 2.5 Flash and GPT-5 are also available. The list of supported models is updated frequently to ensure access to the latest advancements.

New standard document types
麻豆原创 Document AI workspace and OData V4 APIs now support three additional standard document types: learning certificate, order confirmation, and traffic violation notice. This expands the range of business documents that can be processed out of the box, reducing the need for custom configurations.

Configuration of document-level confidence ranges
麻豆原创 Document AI now supports configurable document-level confidence thresholds, making it easier to assess extraction quality at a glance. Custom confidence ranges 鈥� low, medium, and high 鈥� can be defined on the configurations tab of a schema version. When documents are processed, the overall confidence score is displayed in the document header with color-coding: red for low, orange for medium, and green for high confidence.

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麻豆原创 Domain Models

are AI models trained on 麻豆原创 domain knowledge, including code, data, metadata, business processes, architectural knowledge, and documentation. When customers initiate a query or want to create code, the models are designed to provide results firmly grounded in the 麻豆原创 context instead of relying on generic internet knowledge. Combined with context graphs and agents, the models bring deep 麻豆原创 knowledge to Joule, Joule Studio, Joule Agents, and 麻豆原创 applications.

麻豆原创 Domain Models will help:

• Create custom extensions in 麻豆原创 S/4HANA Cloud Public Edition and 麻豆原创 Ariba: Developers in Joule Studio can use specialized models for 麻豆原创 S/4HANA and 麻豆原创 Ariba to understand and generate clean core-compliant code from natural language.
• Query information in 麻豆原创 S/4HANA Cloud Public Edition and 麻豆原创 Ariba: Customers can use natural-language prompts in Joule to access customer data that is grounded in the underlying data models and the business context.

These capabilities will help create clean core extensions while preserving 麻豆原创 standards and governance. 麻豆原创 Domain Models are running under the hood of Joule and Joule Studio and are not directly exposed to customers.

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Govern

麻豆原创 AI Agent Hub enhancements

麻豆原创 AI Agent Hub gives organizations a single control pane for all AI agents, LLMs, and MCP servers across the enterprise. Featuring automated AI asset discovery across major platforms, including Microsoft, Google, AWS, and now ServiceNow and 麻豆原创 AI Core, alongside structured governance assessments and a verification badge that integrates directly with runtime solutions to control which agents and MCP servers are approved for use.

Looking ahead, we will expand into runtime observability and governance, identity and access control, agent-in-process mining, and workforce impact mapping to make the AI Agent Hub the central command center for AI governance at scale.

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Process Consulting Agent
General availability

Process owners, analysts, and operational leaders can turn process data into clear, practical insights without needing specialist analytics skills with the Process Consulting Agent. Users can ask questions in natural language, and the agent retrieves and analyzes relevant process information through a multi鈥慳gent system, returning structured findings along with suggested next steps. Organizations can cut the time spent searching complex data per artifact by up to 90% and reduce the effort to analyze, design, model, and monitor processes by around five percent, helping teams move more quickly from insight to action.

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Enterprise Content Research Agent
General availability

Enterprise architects and portfolio managers can quickly find and understand architectural information through the Enterprise Content Research Agent in 麻豆原创 LeanIX. By querying inventory data and related documentation across sources such as 麻豆原创 LeanIX, Confluence, and SharePoint, the agent highlights missing fields, supports gap analysis, and helps keep records complete and consistent, while leveraging MCP Server tools as needed. This reduces the time spent on informational searches and navigation, simplifies data management tasks, and supports stronger governance over architecture data across the landscape.

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WalkMe solutions, AI knowledge indexing
General availability

Digital adoption and enablement teams can use AI knowledge indexing to make internal documentation easily available to WalkMe鈥檚 AI services in a controlled way. The feature processes connected knowledge sources, such as web pages and files, extracts text content, and converts it into secure vector embeddings, enabling WalkMe鈥檚 contextual AI assistance to ground guidance in company policies, wikis, and procedures rather than generic models. Organizations can improve real-time compliance outcomes and see a 21% increase in procurement policy adherence, while giving employees faster, policy-aligned answers directly in their workflows.

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WalkMe solutions, AI knowledge referencing
General availability

Digital adoption teams and application owners receive app guidance that aligns with their company鈥檚 policies and standards with WalkMe鈥檚 AI knowledge referencing. When certain conditions are met, such as editing a specific field or completing a form, the feature retrieves relevant content from connected internal documentation so tools like AI SmartTips or chat can compare user input with best practices and provide tailored feedback. This allows organizations to anchor AI assistance in trusted company information and help employees access the right policy or governance details when needed.

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WalkMe solutions, pinned AI
General availability

Operations, HR, finance, and other business teams now benefit from on-screen assistance exactly where they enter data or complete tasks, using WalkMe鈥檚 pinned AI. By attaching AI smart tips to input fields and AI Launchers to specific elements, the feature provides contextual guidance in place, grounded in company knowledge sources, so users can continue their work without switching applications. Organizations can improve data quality across key forms and workflows, reduce errors and rework, and see measurable gains such as a 41% improvement in data quality.

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WalkMe solutions, on-demand AI
麻豆原创 Early Adopter Care program

Employees working across line-of-business applications can turn to WalkMe鈥檚 on-demand AI for quick answers or step-by-step support without leaving their current screen. Through a conversational in-app menu that travels with users across applications, they can ask questions, retrieve company knowledge, and trigger automations or Smart Walk-Thrus, keeping guidance and execution closely connected. Organizations benefit from faster access to trusted information and a measurable impact on quality, including up to a 41% reduction in time spent correcting ERP-related business tasks.

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麻豆原创 LeanIX solutions, AI-assisted enterprise architecture decision management
General availability

Enterprise architects and architecture review boards get faster, more consistent decisions with enterprise architecture decision management in 麻豆原创 LeanIX solutions. By providing context, such as transformation diagrams or landscape changes, they can ask the AI to generate a draft architecture decision entry that includes the relevant background, decision, and implications for stakeholders to review and approve. This reduces manual data extraction and authoring effort, streamlines collaboration on approvals, and helps ensure architecture decisions are documented and concluded in a timely, traceable way.

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麻豆原创 LeanIX solutions, AI-assisted fact sheet calculations
General availability

Enterprise architects and workspace admins using AI-assisted fact sheet field calculations in 麻豆原创 LeanIX solutions can quickly turn plain-language business rules into working calculations. When they describe the rule they need, the feature generates readable, commented code that is aware of their fact sheet types, fields, relations, and enums. Hence, calculations align with the actual workspace configuration. This helps teams move from a business question to a usable metric in minutes, increase self-service configuration, reduce reliance on JavaScript skills, and speed up the delivery and maintenance of calculated fields that downstream reports and views depend on.

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麻豆原创 LeanIX solutions, AI-assisted automation creation
General availability

Enterprise architects and workspace admins can transform plain-English workflow descriptions into working automations with AI-assisted automation creation in 麻豆原创 LeanIX solutions. When they describe the review, update, or governance flow they need, the feature generates the appropriate triggers, conditions, and actions with field mappings aligned to the current workspace configuration. This lets teams build and scale automations themselves, increasing EA productivity, reducing reliance on technical experts, and making it easier to keep key processes such as onboarding workflows and lifecycle checkpoints consistently automated.

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麻豆原创 Cloud ALM, AI-assisted document summary
General availability

Document summary helps project teams and engineers understand long documents more quickly. Within the documents application, users can trigger an AI-generated summary, review and edit it in a separate window, and then apply it as a persistent summary section in the document. This shortens the time spent manually reading and extracting key points, supports faster comprehension of complex engineering content, and enables quicker decisions without leaving the document workflow.

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麻豆原创 Signavio solutions, AI-assisted context analyzer, text-to-event matching, and sentiment analysis
General availability

The context analyzer helps process owners and analysts match free text with process objects, such as sales orders or purchase requisitions, to the corresponding process events in event logs. The feature links free-text records such as survey responses, feedback, comments, and tickets to the corresponding process events, so qualitative experience data appears alongside operational logs. This enriches process mining with unstructured text, reduces manual mapping work, and improves process analysis accuracy by around 30%, helping teams pinpoint bottlenecks and experience issues more effectively.

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Jonathan von Rueden is chief AI officer for 麻豆原创 SE.

*Disclaimer: This article provides estimated benefits. All calculations are estimates based on 麻豆原创 customer case studies, 麻豆原创 benchmarks, and other research. Actual benefits may vary and may be affected by additional factors not considered by this article. The information is provided 鈥渁s is鈥� without warranty of any kind, expressor implied, and in no event shall 麻豆原创 be liable for any damages whatsoever in relation with the use of this article. See Legal Notice on for use terms, disclaimers, disclosures, or restrictions related to this material.

As customer journeys become more connected and complex, gaps in execution can lead to inconsistent experiences, slower response times, and missed opportunities. The next frontier of customer experience is not generating more insights, but turning insight into coordinated action at scale.

This latest release of the solution portfolio helps organizations strengthen that foundation by connecting workflows across marketing, commerce, sales, and service鈥攅nabling more consistent, scalable execution across every customer interaction.

Explore the highlights of the Q2 2026 release. For full sub-solution details, see our recaps for the , , , , and solutions.

Turning customer intent into action

Customer interactions are becoming more conversational, connected, and immediate across channels and touchpoints. At the same time, organizations need faster access to information and simpler ways to take action鈥攚hether engaging customers, managing campaigns, or responding to changing business needs.

• Conversational AI shopping through a model context protocol (MCP) server: Enable secure integration between and AI agents that can guide or act on behalf of customers. AI assistants can query real-time product information, provide inventory updates, manage shopping carts, and complete transactions directly within chat or voice interfaces鈥攃reating more intelligent, conversational buying experiences beyond the traditional storefront. 

• Joule in 麻豆原创 Engagement Cloud (麻豆原创 Early Adopter Care): Bring 麻豆原创’s conversational AI directly into campaign workflows. Teams can ask product or campaign questions in natural language and get accurate answers without searching across multiple systems. They can also duplicate successful campaigns without starting from scratch, freeing more time for strategic thinking, creativity, and customer engagement.

• Rich communication services (RCS) in 麻豆原创 Engagement Cloud: Engage customers with rich, interactive messages supported by Google and featuring media, carousels, and action buttons within native mobile messaging experiences. Branded, verified messages help build trust and guide customers smoothly from discovery to purchase without requiring an additional application.

Scaling personalized engagement

Recognizing customer intent is only the beginning. As engagement channels expand, marketing teams need to respond quickly while delivering relevant, personalized experiences at scale. This requires frictionless campaign execution, timely insights, and the ability to tailor every interaction to each customer’s needs and preferences.

• AI-assisted content composer (pilot): Generate high-quality, on-brand campaign content in . Using Gemini models informed by audience, product, and campaign context, teams can quickly create and refine content variations so they can launch personalized campaigns faster and spend less time on manual content creation.

• Embedded audience builder: Enable marketers to access and activate rich data from directly within 麻豆原创 Engagement Cloud. With this capability, they can build advanced segments themselves without switching systems or waiting on data analysts. The precision and relevancy of omnichannel campaigns can be improved by combining behavioral, transactional, account, and profile data with operational data across the business.

Enabling consistent sales execution at scale

Success depends on turning insight into disciplined, repeatable actions that drive predictable revenue outcomes. As sales environments grow more complex, even small inconsistencies in data, priorities, or execution can undermine forecasts and cause opportunities to slip away. Acting with greater consistency and confidence calls for stronger data integrity, aligned behaviors, and clearer guidance.

• Agentic opportunity summary overview: Give sales teams the tools they need to quickly assess deal health. This capability in aggregates engagement signals, activity levels, and progress indicators into a real-time view, allowing teams to identify risks early, prioritize effectively, and maintain deal momentum.
• : Optimize sales velocity and help ensure the right product placement with retail execution enabled by intelligent, AI-enhanced processes that maximize revenue. Teams can improve visit planning and execution, harness insights to improve sales performance, and optimize interactions. For consumer products companies, this helps drive shelf availability, promotion compliance, and merchandising effectiveness across retail locations. Field teams gain greater visibility into store-level execution, enabling more consistent brand presence and stronger sell-through performance.

• 麻豆原创 Incentive Management: Improve sales team effectiveness by using the solution, which is part of solutions. It helps drive profitable behaviors that increase revenue and support business growth while providing real-time performance insights, dispute management, and motivating rewards. Teams can use flexible tools to streamline incentive compensation and quickly design, test, and launch sales plans. AI-supported recommendations are also available to guide organizations in optimizing plans, maximizing outcomes, and uncovering actionable insights.

• Consumer Products Intelligence (麻豆原创 Early Adopter Care program): Enable consumer product companies to turn the enormous amount of sales and trade data they generate into better decisions. It uses analytics and AI to help improve trade spend performance, increase sales revenue and margins, and reduce manual effort.
   

Standardizing service execution across the enterprise

Service teams are increasingly expected to deliver faster, more reliable support while managing growing complexity across channels and requests. Achieving this objective requires simplifying how services are accessed and helping ensure consistent processes across the organization.

• Self-service catalog: Allow employees to quickly find what they need without understanding backend processes. Through this guided, intuitive catalog for requests are automatically routed with the right context, reducing delays and improving resolution times.

• Content package framework: Leverage the framework for 麻豆原创 Enterprise Service Management to deliver rapid, scalable value across lines of business. Prebuilt, reusable configurations for case types, workflows, and catalogs help organizations deploy services more quickly while simplifying implementation across the business. With this approach, organizations can eliminate complexity, empower partners, and speed adoption. Content packages for HR service delivery will be coming soon. 
• Email editor in 麻豆原创 Service Cloud and 麻豆原创 Enterprise Service Management: Compose, edit, and manage customer communications more efficiently while maintaining high-quality service interactions. The modern, user-friendly email editor is built for an AI-first world.

• Creation of sales objects from customer hub: Let agents fully manage leads, opportunities, appointments, and sales orders directly from the service agent workspace of 麻豆原创 Service Cloud. This capability helps turn each customer interaction into an opportunity to deliver more value.

Accelerating connected order management

Turning insight into action requires connected systems that can adapt as the business evolves. As organizations expand order channels, fulfillment networks, and technology landscapes, they need integration and order management that can keep pace so teams can respond faster to change.

• Flow connector: Enables smooth data flow between the solution, other 麻豆原创 solutions, and third-party products. This predefined capability allows business users to configure custom business flows and integrations with minimal IT involvement, creating connected order management processes across the enterprise.

Execution at scale: the next customer experience advantage

As AI becomes embedded in daily operations, the differentiator shifts from insight generation to execution.

Our recently announced strategic partnerships with and Google Cloud help extend this execution-first approach by connecting AI-powered service, commerce, and engagement experiences directly to operational systems and business data. As a result, organizations can move from isolated interactions and insights to coordinated actions that drive faster resolutions, better customer experiences, and greater business impact.

Learn more about 麻豆原创 CX in Q2鈥�2026 

Read the 麻豆原创 Help documentation to get started with these new capabilities:

Balaji Balasubramanian is president and chief product officer for 麻豆原创 Customer Experience and Consumer Industries at 麻豆原创.

But what if more or better software is not necessarily the answer? Each solution solves a real problem for a specific team. But collectively, they create a consequence no one planned for: every new tool builds its own data world, without a common language or context across them.

In a 2026 study by Oxford Economics, . Twenty-nine percent remain highly fragmented. Organizations with siloed CX tech are more likely to face an inability to connect customer needs to actionable data insights. Fifty-eight percent reported this challenge, compared to 47% among those with harmonized environments.

More than what tools businesses choose to add to their CX landscape, how they connect and interact with each other becomes even more important.

A unified data strategy sounds straightforward in principle. In practice, most businesses find that the obstacle is not ambition but rather execution. Every integration decision made without a clear data architecture becomes a future campaign mired in manual reconciliation, a customer journey that breaks at the handoff, or a personalization promise the disparate data sources cannot support. Implementation without a validated strategy creates new fragmentation inside the solution meant to eliminate the old. And without a structured way to pressure-test decisions before any commitment is made, even well-resourced organizations find themselves repeating the same cycle: invest, integrate, fragment, repeat.

The real barrier is not budget or technology

A Forrester study of more than 1,000 senior executives found that , ahead of budget, technology maturity, and talent. The same study found that 56% of respondents struggle with poor data quality; 55% face persistent data silos. This is not for lack of investment in technology, but because the connections between systems were not designed or maintained effectively.

What bridges that gap is not another platform, it is the expertise to think through data connectivity decisions before they are made and the ongoing guidance to ensure those decisions compound into measurable gains over time. The for solutions provides that guidance along every step of the journey.

Define what success actually looks like

The most common reason data unification projects fall short of expectations is not technical failure, it is a failure to define and measure what success looks like for the entire business before the work begins.

The value management session from the Advanced Success Plan for 麻豆原创 Customer Experience establishes that definition at the outset: What does a fully unified data strategy actually enable? It means running the next marketing campaign without manual data reconciliation, and presenting an AI readiness road map without caveats. Stakeholders will know, at every checkpoint, whether the investment is moving the business forward, not just moving the project forward.

Design the strategy before building the integrations

A robust data strategy starts with good design. Product guidance from the Advanced Success Plan for 麻豆原创 Customer Experience covers available out-of-the-box integrations, common usage scenarios, and pitfalls and how to avoid them鈥攁ll delivered in a live remote session by an 麻豆原创 expert who can answer questions in real time. The data strategy can be conceptualized and pressure-tested before any budget or technical commitments are made.

Validate every critical decision with expert guidance

With the technical assistance and functional assistance from the Advanced Success Plan for 麻豆原创 Customer Experience, businesses have continuous access to expert guidance at every critical decision point. Beyond resolving immediate questions, the ongoing access also shares insight on how 麻豆原创 thinks through problems, strengthening in-house expertise with every interaction. The result is an organization that makes better decisions not just now but for the future.

Measure whether the strategy is delivering

Adoption and innovation checkpoints conducted on a quarterly or semi-annual basis bring the measurement back to where it started: the business outcomes defined at the outset. Do campaigns run without manual reconciliation? Is the AI use case performing against its stated goals? A clear throughline from the value management success KPIs to the adoption and innovation checkpoints proves the benefits of the investment.

Where cycles and silos break

The cycle of invest, integrate, fragment, repeat is not inevitable. It is the predictable result of making technical decisions without an anchoring business imperative, and integration decisions without strategic expert guidance. Organizations that break the cycle do not necessarily have better technology than their competitors鈥攖hey have better judgment about how to use it.

The Advanced Success Plan for 麻豆原创 Customer Experience exists for exactly that reason: to put proactive and prescriptive guidance at every decision point where that judgment matters most. When data strategy is designed before it is built, validated before it is committed, and measured against real business outcomes, the technology investment already made starts working harder.

The stack was never the problem. When the thinking behind the technology finally matches its ambition, the personalized, seamless experience becomes a reality.

Tara Tracey, global product owner for the Advanced Success Plan for 麻豆原创 Customer Experience.
Ella De Torres, product manager for the Advanced Success Plan for 麻豆原创 Customer Experience.

While cash is losing importance, contactless payments and mobile wallets are becoming dominant, with many providers enabling customers to make payments directly from their smartphones. These shifts put constant pressure on retailers to update their systems. The demand for mobile, contactless, and secure payment solutions keeps growing.

Market trends determine success

As a globally operating company, 麻豆原创 keeps a close eye on the latest trends, looking not just to adapt its solutions but to rethink them. 麻豆原创 Customer Checkout, the intelligent integrated point of sale (POS) solution for retail, merchandising, and catering, relies on strong partnerships to stay ahead and keep pace with those trends. Speed and the right collaborations make the difference.

“The POS market is in constant flux. Competitors are alert and customers arrive every day with new demands and ideas,” said Harald Tebbe, development lead for 麻豆原创 Customer Checkout, speaking to the scale of change in the market. “Ultimately, market trends decide who stays successful and who doesn’t. Even though payment processes aren’t our direct business, our focus is on offering customers a wide range of payment options to make the end customer’s shopping experience as simple and convenient as possible. Payment infrastructure is a critical component of that experience. Our trusted technology partnerships are what make it possible, which is why we’re continuously looking for new collaborations.”

End-to-end solution through 麻豆原创 Customer Checkout and Viva.com

As the first tech bank in Europe for businesses and being active in 29 countries, Viva.com offers customers an integrated omnichannel payments, banking, and financing platform.

Viva.com’s leading Tap on Any Device payment technology takes centre stage. It turns any Android device鈥攆rom fixed and mobile payment terminals to self-checkout tills and handheld devices鈥攐r iPhone into a secure card terminal, optimizing the customer experience and helping customers pay wherever they are. Tap on Any Device by Viva.com supports over 40 payment methods, DCC, surcharge, offline payments, while the tech bank delivers real-time settlement 365 days a year and a cashback scheme reducing transaction fees to zero percent.

Since 麻豆原创 Customer Checkout was built to be hardware-independent, works across a variety of devices, and integrates with different payment terminals, the partnership creates an ideal end-to-end solution for customers in retail, consumer goods, food service, and other sectors. Open API interfaces in 麻豆原创 Customer Checkout and Viva.com’s Terminal Integration Hub made the integration straightforward and quick to implement.

鈥淲ith Viva.com, 麻豆原创 Customer Checkout customers in 29 European countries get integrated payments and banking in one platform: From omnichannel payments and our leading Tap on Any Device technology to fast, seamless access to tailored financing options -no need to switch providers,” Harry Xenophontos, Chief Business Officer at Viva.com, said. “That’s the unique proposition we’ve built with 麻豆原创.”

Secure data communication for customers

Integrating 麻豆原创 Customer Checkout with Viva.com requires a dedicated plug-in, which simplifies the connection between the POS system and the payment terminal by ensuring data is transferred via the HTTPS protocol, keeping communication secure. This matters in a period where both innovation and the protection of sensitive data are front of mind.

Customers also benefit from a particularly fast setup process and the freedom to choose from a wide range of devices to use as payment terminals. With three possible integration options, the system offers flexibility so merchants can find the right fit for their specific needs.

The joint project supports the development and delivery of forward-looking POS solutions that work well for both merchants and end customers.

Elena Vavitsa is a senior solution specialist for 麻豆原创 Customer Checkout.

Top image courtesy of Viva.com

The Advanced Success Plan version for 麻豆原创 Customer Experience solutions is designed precisely for this moment. It offers utilities customers a structured, continuously guided approach to maximizing value from 麻豆原创 Customer Experience (麻豆原创 CX) investments, from first adoption through to AI-enabled optimization.

A sector in motion: why customer experience has become strategic for utilities

Four converging forces are making customer experience a boardroom priority for utilities and increasing the demand for structured, expert-led adoption support.

• Energy transition and service complexity: Renewables, EV charging, solar feed-in tariffs, and demand response programs are adding new service dimensions. Customers expect their energy provider to manage this complexity seamlessly.
• Deregulation and customer switching: In liberalized energy markets, customers can, and do, switch providers. The cost of acquisition consistently exceeds the cost of retention. Superior service experience is a measurable retention lever.
• Smart metering and data volume: Smart meter rollouts generate billions of interval readings daily. This data can fuel proactive outreach and personalized billing, but only if the customer experience platform is correctly configured to act on it.
• Regulatory intensity: Billing accuracy mandates, complaint resolution timelines, and outage notification requirements are intensifying. The 麻豆原创 CX portfolio can support compliance when features are correctly activated and configured.

麻豆原创 in utilities: a significant and growing market

Utilities are not a niche segment for 麻豆原创. Understanding the scale of the 麻豆原创 utilities community frames why the Advanced Success Plan for 麻豆原创 Customer Experience solutions matters for this industry in particular. 麻豆原创 is trusted by hundreds of utilities customers globally and is . The investment in the relationship has been made. The question is whether customers are fully using what they have.

With the Advanced Success Plan, customers can turn the 麻豆原创 Customer Experience portfolio into a driver of growth and innovation. They can gain the confidence to act decisively, supported by unlimited expert guidance and intelligent insights that help ensure every feature can deliver measurable business value.

The business case: customer retention over acquisition

For utilities operating in competitive markets, retaining customers has become as strategically important as acquiring new ones. Customers can switch suppliers in minutes via a digital platform, complaint escalation processes are visible on social media, and billing errors in a smart meter world are harder to excuse and faster to escalate.

The 麻豆原创 CX portfolio can address these challenges across the full customer lifecycle. 麻豆原创 Engagement Cloud enables targeted, segmented communications. 麻豆原创 Service Cloud can centralize complaint management and agent interactions. 麻豆原创 Sales Cloud helps manage accounts, contacts, leads, and opportunities. 麻豆原创 Commerce Cloud is the聽digital sales and self-service storefront layer and can handle how customers discover, compare, purchase, and manage energy products online. What the Advanced Success Plan for 麻豆原创 Customer Experience solutions does is help to ensure these capabilities are not just purchased but adopted, optimized, and continuously improved.

How the Advanced Success Plan structures the adoption journey

The Advanced Success Plan for 麻豆原创 Customer Experience solutions helps organize the adoption journey across four distinct phases, each with a defined purpose and a set of targeted services:

1. Introducing innovation: Identify and prioritize the right innovations for your business. Services here include product guidance sessions covering areas such as utilities session and agent desktop, intelligent selling, campaigns and segmentation, and AI foundation and use case navigator.
2. Adopting innovation: Plan and prepare for structured adoption with minimal risk. Services include the AI process fit-gap analysis, key feature advisory, release guidance, success expert engagement, service planning, value management, innovation checkpoints, and adoption checkpoints.
3. Activation and optimization: Enable hands-on activation of AI and customer experience capabilities in your environment. Services include activation sessions across 麻豆原创 Sales Cloud, 麻豆原创 Service Cloud, 麻豆原创 Marketing Cloud, and 麻豆原创 Engagement Cloud; AI agent activation; embedded AI activation; Joule activation; and technical and functional assistance.
4. Success measurement and continuous improvement: Measure outcomes and sustain momentum through ongoing engagement. Services include a continuous engagement model, release guidance, success expert, value management, innovation checkpoints, and adoption checkpoints.

8 services available to utilities customers鈥攁nd how each one helps

The Advanced Success Plan for 麻豆原创 Customer Experience solutions comprises eight distinct services. Each has a defined scope, a specific business need it addresses, and measurable benefits. For utilities customers, each service connects to a characteristic operational or strategic challenge.

Product guidance
Structured sessions introduce utilities teams to the capabilities most relevant to their context, from meter-read-driven billing notifications in 麻豆原创 Service Cloud to segmented communications in 麻豆原创 Engagement Cloud. The goal is to accelerate time-to-awareness, so teams know what exists before they have to find it.

Key feature advisory
This is curated, customer-specific guidance on which features to activate and in what sequence. For utilities, this means filtering a broad 麻豆原创 CX road map down to the capabilities that matter for smart metering, complaints management, and outage communications then discarding what does not apply.

Release guidance
Every 麻豆原创 CX quarterly release brings dozens of updates. Release guidance helps ensure utilities teams receive a focused brief on what is relevant to their industry context, not a generic list of all product changes, so adoption decisions can be faster and better informed.

Solution review
This is a structured review of the current solution configuration against best practice. For utilities, this commonly surfaces configuration gaps in complaint workflows, billing notification templates, or service agent desktop layouts that erode the customer experience over time if left unaddressed.

Transformation advisory
Strategic guidance connects 麻豆原创 CX capabilities to the outcomes utilities care about most: cost-to-serve reduction, complaint resolution improvement, and regulatory compliance. Transformation advisory helps move the conversation from features to business impact.

Activation
This is hands-on, expert-assisted activation of specific capabilities in the customer’s live environment. For utilities, this includes activating Joule for service agents and enabling AI-driven case categorization in 麻豆原创 Service Cloud. Many autonomous agents or assistants announced at 麻豆原创 Sapphire, once available, can be activated to help guide service agents to process cases.

Technical assistance
Direct technical support across all phases of the engagement covers integration architecture, performance guidance, load testing guidance for mass billing cycles, and resolution of complex system behavior. This service is especially critical for utilities given the high-volume, time-sensitive nature of meter-read processing and month-end billing runs.

Functional assistance
This comprises business process and functional configuration support throughout the engagement. Utilities-specific coverage includes complaint handling workflows, outage notification sequences, and the configuration of billing determinant displays in the service agent desktop, helping to ensure what is built serves the way utilities actually operate.

A practitioner’s perspective

Working directly with utilities customers shows first-hand how the Advanced Success Plan for 麻豆原创 Customer Experience solutions can drive meaningful business value, especially for organizations without deep technical or functional 麻豆原创 CX expertise in-house. Utility companies face a specific set of challenges: complex billing architectures, regulatory requirements, and high customer expectations for responsive service. The right services delivered at the right moment can be genuinely transformational. Four patterns stand out consistently:

1. Bridging the knowledge gap for business users: Unlike system integrators or technical consultants, many utility business teams may not fully grasp the functional nuances required to maximize their 麻豆原创 investment. Value-driven customer success manager engagement becomes critical here by translating technical possibilities into business outcomes, advising on feature adoption, and ensuring cross-team alignment across operations, IT, and customer management. The customer success manager acts as connective tissue between what 麻豆原创 CX can do and what the business needs to achieve.
2. Go-live checks are the foundation for seamless launches: Utilities cannot rely on generic go-live checklists. Are meter reads flowing end-to-end? Are billing determinants and rate structures properly mapped? Are customer service orders integrated with billing and meter management systems? Comprehensive go-live checks built for the utilities context eliminate the guesswork, giving teams confidence that key processes are functioning correctly from day one, not discovered to be broken three months into the first billing cycle.
3. Road map guidance enabling better design decisions: Each 麻豆原创 CX release brings dozens of new features, but only subsets are relevant for a given customer in a given market. Regular, curated road map guidance helps utilities focus on the features and design decisions that will most efficiently drive their specific outcomes, whether that is improving customer satisfaction, streamlining operations, or enriching digital self-service. This targeted approach prevents costly missteps and empowers business stakeholders to make confident decisions without requiring deep product expertise of their own.
4. Early-phase support building for reliability and performance: The early project phase is often when performance issues and integration risks are best addressed, but it is also where internal teams feel least certain. Proactive support that includes performance reviews spanning interconnected billing and service systems, high-level architecture guidance, and utilities-specific load testing helps ensure reliable operation under real-world peak conditions. Utilities processing millions of daily meter reads, running mass billing cycles at month-end, and handling seasonal demand spikes need to know their system will hold before the peak arrives, not after it has passed.

What stronger 麻豆原创 CX adoption looks like in practice

When utilities customers engage with the full Advanced Success Plan for 麻豆原创 Customer experience solutions at the right moments, the outcomes can be concrete and measurable:

• Adoption confidence: Teams understand the 麻豆原创 CX capabilities that matter for their industry and know how to use them. Adoption is driven by guided enablement, not trial and error.
• Configuration quality: Solution reviews and functional assistance can ensure complaint workflows, billing notifications, and service processes are correctly configured, reducing workarounds and support volume.
• Release relevance: Every quarterly 麻豆原创 CX update is assessed for utilities relevance. Teams receive a focused brief on what to adopt, not a generic list of all product changes.
• AI activation at pace: Joule, embedded AI agents, and 麻豆原创 Engagement Cloud intelligence features are activated with expert assistance, helping to move from available-in-catalogue to live-in-production for utilities use cases.
• Integration reliability: Standard integrations between 麻豆原创 Service Cloud and 麻豆原创 S/4HANA Utilities are correctly configured, giving agents real-time access to meter history, billing data, and service orders.
• Transformation clarity: Transformation advisory connects the 麻豆原创 CX road map to the outcomes utilities care about most: cost-to-serve reduction, complaint resolution improvement, and regulatory compliance.

A service portfolio built for the complexity of utilities

The 麻豆原创 investment in the utilities sector is substantial and well-established. Utilities organizations around the world have already deployed the 麻豆原创 CX portfolio and are running their customer operations on it. The question is not whether they have access to world-class customer experience capabilities, but whether they are fully using what they have.

The eight services described above, combined with customer success manager-led perspectives on go-live quality, road map relevance, and performance assurance, represent a comprehensive framework for utilities organizations to realize the full potential of their 麻豆原创 CX investment.

The Advanced Success Plan for 麻豆原创 Customer Experience solutions is not a generic offering. When applied with industry focus and customer success manager expertise, it becomes a strategic asset, one that helps utilities deliver on the promise of a superior customer experience in an increasingly competitive, regulated, and technically complex market.

Rajeev Ranjan is product manager for the Advanced Success Plan for 麻豆原创 Customer Experience.
Tara Tracey is global product owner for the Advanced Success Plan for 麻豆原创 Customer Experience.

For many 麻豆原创 Commerce Cloud and 麻豆原创 Engagement Cloud customers, this gap shows up in familiar ways: recommendation engines that surface generic results because behavioral data is not connected, e-mail campaigns timed by calendar rather than by individual habit, loyalty programs that reward transactions rather than relationships, and personalization rules that require significant manual effort to maintain at scale.

The ambition is there; the infrastructure to fulfill it, however, is often partial. Clean data is siloed, AI capabilities are underutilized, and the organizational discipline to run ongoing experimentation does not yet exist.

This is precisely the problem the version for is designed to solve.

What hyper-personalization actually requires

True hyper-personalization is not a feature you switch on. It is a capability you build systematically across three interdependent layers: data, decisioning, and delivery.

Data is the foundation. Hyper-personalization requires unified, consent-aware, real-time customer profiles consolidated across commerce transactions, engagement history, browsing behavior, service interactions, and loyalty activity. Without this foundation, even the most sophisticated AI models are operating on incomplete signals.

Decisioning聽is where AI translates those signals into action鈥攖he next best product to surface, the right offer to present, or the optimal moment to reach out. This layer requires not just model accuracy but governance, knowing when to trust the algorithm and when human judgment should override it.

Delivery聽is where the personalized experience reaches the customer, at the storefront, in the inbox, through a mobile push, or across a loyalty interaction. This layer requires orchestration across channels, consistent with the customer’s current context.

The Advanced Success Plan for 麻豆原创 Customer Experience solutions helps address all three layers simultaneously, providing the expert guidance, governance frameworks, and adoption acceleration needed to move from point capabilities to an integrated operating model.

Hyper-personalization in 麻豆原创 Commerce Cloud

麻豆原创 Commerce Cloud can provide the storefront execution layer for personalization at scale. The solution’s AI-assisted product recommendations capability enables organizations to show the most relevant products to each visitor at the right point in their shopping journey, from trending products and related items to complimentary products that support cross-sell and upsell motions. This can go beyond manual merchandising rules; it can respond dynamically to real-time behavioral signals, helping to improve conversion performance and drive product discovery at a scale no merchandising team could replicate manually.

Yet many 麻豆原创 Commerce Cloud customers have not yet activated the full depth of these capabilities. The blockers are predictable: data quality gaps that limit recommendation model performance, integration complexity between the commerce layer and upstream profile data, and an absence of the experimentation discipline needed to tune and improve models over time.

The Advanced Success Plan for 麻豆原创 Customer Experience solutions can bring targeted guidance to help address these barriers. Data readiness assessments can establish the quality baselines and integration patterns required to feed reliable signals into 麻豆原创 Commerce Cloud’s personalization engine. Adoption accelerators help teams operationalize experimentation, defining hypotheses, running A/B tests, and translating results into durable configuration changes. The outcome is a storefront that can continuously learn and improve, rather than one frozen at the point of initial configuration.

Hyper-personalization in 麻豆原创 Engagement Cloud

麻豆原创 Engagement Cloud, powered by 麻豆原创 Emarsys, can extend personalization beyond the storefront and into the full lifecycle of the customer relationship. This is where 麻豆原创 Commerce Cloud’s transactional signals combine with engagement history to help power cross-channel personalization that is individual rather than segment-based.

The solution’s AI-assisted send time optimization capability is a direct example of this philosophy in practice. Rather than sending campaigns on a fixed schedule, the capability can analyze each contact’s behavioral patterns鈥攊ndependently of time zone, language, or region鈥攁nd deliver messages at the precise time each individual is most likely to engage. This is not personalization as a concept; it is personalization as an automated, scalable operational process.

Paired with the 麻豆原创 Emarsys, AI-assisted campaign translator capability and omnichannel orchestration, 麻豆原创 Engagement Cloud enables marketing teams to move from building campaigns to orchestrating journeys where the system is continuously learning which signals should trigger which interactions and adapting those interactions based on what drives response.

The native integration between 麻豆原创 Commerce Cloud and 麻豆原创 Engagement Cloud is a critical accelerator here. By unifying commerce behavior and engagement data, organizations can drive increases in conversion rate, purchase frequency, and average order value in ways that neither system could achieve independently. The Advanced Success Plan for 麻豆原创 Customer Experience solutions helps customers realize this joint value by aligning integration architecture, data governance, and adoption milestones across both products within a single, coordinated engagement model.

How the Advanced Success Plan enables continuous improvement

Hyper-personalization projects are often treated as one-time implementations. The Advanced Success Plan for 麻豆原创 Customer Experience solutions is designed to make them repeatable, continuously improving programs. This means:

• Outcome-based governance: Co-defining the KPIs that matter, such as conversion rate lift, repeat purchase rate, engagement open rates, and average order value, and building work streams aligned to move them measurably.
• Prescriptive adoption patterns: Structured playbooks for activating AI-assisted recommendations, send time optimization, and next-best action logic, with clear milestones and measurable gates.
• Continuous enablement: Role-based coaching for the teams responsible for data, product ownership, and campaign operations, closing skills gaps that otherwise cause personalization programs to plateau or regress.
• Proactive telemetry: Regular adoption checks that surface underperforming configurations before they impact business outcomes, and AI-guided best practices that inform ongoing tuning.

Making the business case concrete

For 麻豆原创 Commerce Cloud customers, the value of operationalized hyper-personalization can be seen in storefront metrics: higher conversion from AI-surfaced recommendations, increased average order value through intelligent cross-sell, and improved product discovery that reduces bounce and exit rates.

For 麻豆原创 Engagement Cloud customers, the value can be seen in engagement quality: open rates and click-through rates that reflect individual relevance rather than list-wide broadcast, improved campaign ROI through AI-optimized delivery, and loyalty program engagement that reflects relationship depth rather than transaction volume.

Across both, the compounding effect of unified data and orchestrated decisioning is what transforms hyper-personalization from a POC into a sustained growth mechanism, one that gets measurably better over time.

Payal Sachdev is product manager for the Advanced Success Plan for 麻豆原创 Customer Experience.
Tara Tracey is global product owner for the Advanced Success Plan for 麻豆原创 Customer Experience.

Travelers nowadays have very strict requirements. From booking a ticket to arriving at a destination, they expect fast, convenient, and prompt assistance when they face issues along the way. No matter how perfectly a system is designed to meet needs, there will always be situations that can鈥檛 be avoided. Delays, confusing booking systems, long customer service wait times, and much more create frustration for travelers.

AI is reshaping how the travel and transportation industry is doing business. It delivers more avenues to provide customer care aside from the typical communication channels such as e-mail, short messaging services, and social media. AI provides smarter, faster, and more personalized customer experiences, leading to happier customers and therefore growth in revenue for the business. AI is no longer optional, it is now becoming a necessity.

Beyond tickets and timetables: how AI orchestrates the customer journey

Previously, travelers preferred travel agents over booking apps, relied on printed tickets, and valued personal service and human interaction. However, mobile apps for bookings, check-ins, and payments are now widely used. Travelers also expect real-time updates and personalized recommendations that provide seamless, end-to-end experiences.

麻豆原创 delivers an ecosystem that can provide the tools needed to meet these expectations. Using 麻豆原创 Service Cloud, organizations can manage cases, complaints, and information requests efficiently. AI can respond within seconds, unlike traditional customer service processes that rely on manual handling of support tickets. AI-powered chatbots can also manage a high volume of customer conversations simultaneously. Here is an example integration strategy:

Intelligent selling services for 麻豆原创 Commerce Cloud

Intelligent selling services for 麻豆原创 Commerce Cloud are AI-powered services that leverage machine learning and artificial intelligence to help deliver personalized customer experiences and optimize booking strategies. These services help analyze customer booking patterns, provide contextual data across customer touchpoints, and offer recommendations that can lead to increases in revenue.

Travel accelerator

The travel accelerator for the 麻豆原创 Commerce solution is an industry-specific solution designed to enable travel companies to deliver omnichannel digital traveler engagement through 麻豆原创 Commerce Cloud. For customers that already have an existing 麻豆原创 Commerce Cloud solution, they can use the travel accelerator to help tailor it for travel business demand. It can provide real-time information to offer personalized customer experiences and reinforce customer loyalty.

Loyalty management program through integration

Organizations may need a system to reward customers for coming back, like earning points, perks, or special treatment when you repeatedly book with the same travel company. For this, an integration to a loyalty management program, either 麻豆原创 Customer Loyalty Management or a third-party solution, can be used.

The way forward

can help you achieve your business goals. As a starting point, we can create a service engagement plan that provides a tailored approach to meeting your KPIs. During this phase, we also deliver sessions to help you set up 麻豆原创 Sales Cloud, 麻豆原创 Service Cloud, and 麻豆原创 Commerce Cloud while working to ensure that travel and transportation industry best practices are followed.

With AI capabilities available across every solution, you can now categorize your customer base based on travel behaviors and patterns, as well as perform sentiment analysis on customer reviews and support tickets. The Advanced Success Plan can serve as a strategic partner in helping achieve AI objectives. Our experts, backed by deep industry knowledge, can provide guidance on the most effective path forward.

To deliver services that are aligned with each customer’s specific goals, we have organized our offerings into four phases: implementation, pre-go-live, post-go-live, and continuous improvement.

Implementation

During the implementation phase, our focus is on providing adoption guidance to help set up the solutions, from front-end applications to back-end systems. We work alongside the team to establish core capabilities needed for a successful implementation and to help ensure the solution is aligned with business requirements.

This includes, but is not limited to, application user management, key user extensibility, the 麻豆原创 CX AI Toolkit, integrations, security considerations, and other essential platform capabilities. Our goal is to help build a solid foundation that supports scalability, maintainability, and future growth while enabling teams to get the most value from the platform.

Pre-go-live

In the pre-go-live phase, our focus is to validate and safeguard the solutions that have been built throughout the implementation. The goal is to make sure systems are configured correctly, performing as expected, and ready at go-live.

This includes conducting detailed reviews of business configuration settings, evaluating system performance, validating integrations, reviewing security and user access setups, and assessing analytics and reporting capabilities. We also help identify potential risks, gaps, or areas for optimization before launch, working to ensure issues are addressed proactively.

In addition, we work with teams to confirm readiness across key functional and technical areas, helping ensure that testing has been completed successfully, critical business scenarios have been validated, and the solution is aligned with operational requirements. Performing an adoption checkpoint during this phase helps reduce risk, improve system stability, and support a smoother go-live experience.

Post-go-live

During the post-go-live phase, we work closely with the team to help ensure that recommendations and best practices identified throughout the implementation have been properly configured and are delivering the intended results.

As users begin working in the production environment, new questions, opportunities for optimization, and minor challenges often emerge. During this stage, we provide continued guidance and support to help address those items, whether they are related to business processes, system configuration, integrations, extensibility, analytics, or overall solution adoption.

Our functional and technical experts remain available to review issues, provide recommendations, and help navigate any areas that require additional attention. We also help identify opportunities for further improvements and knowledge transfer, working to ensure the organization is well-positioned to maintain, enhance, and scale the solution over time.

Continuous improvement

Finally, as part of the continuous improvement phase, we help stakeholders remain informed about new innovations and enhancements introduced through 麻豆原创 release cycles. By staying up-to-date with the latest capabilities, the team can continue to maximize the value of the solutions and drive ongoing business success.

Tara Tracey is global product owner of the Advanced Success Plan at 麻豆原创.
Geoffrey Arado is product manager for 麻豆原创 Customer Experience.

They see a personalized ad, explore a product through an AI assistant, respond to an offer in their inbox, and open your app expecting everything to simply work. They expect relevance, continuity, and immediacy across every interaction in their entire journey.

Instead, they hit friction:

• They鈥檙e asked to repeat information they already shared.
• Promotions don鈥檛 reflect inventory availability.
• Fulfillment updates arrive late or not at all.
• And when support is needed, no one has the complete picture.

These are not isolated breakdowns. They are systemic customer experience failures.

In the era of agentic AI, those moments compound quickly. Customers now assume brands understand who they are, what they need, and what is happening in real time, and they expect businesses to act on that intelligence instantly across the entire customer journey.

The agentic era is accelerating this shift dramatically.

This goes beyond surfacing insights or recommendations. Agentic AI systems are increasingly capable of planning, reasoning, decision-making, and coordinated action across networks of agents. AI is fundamentally reshaping how customer experiences are created, delivered, and optimized in real time.

But it also exposes a fundamental truth: when AI moves faster than your data, systems, and processes, it reveals everything that鈥檚 broken.

That tension鈥攂etween rising customer expectations and disconnected CX reality鈥攊s exactly what 麻豆原创 and Google Cloud are solving together with .

The customer experience reality: ambition outpacing execution

Most organizations want to deliver seamless, connected experiences, but they struggle to operationalize those moments.

According to 麻豆原创 research, 78% of businesses say AI will be essential for retaining customers in 2026. Yet fewer than two in five share their customer data across CX (37%) or CRM (39%) platforms.

This is an enterprise operations problem, and its impact is felt across the entire CX business. And when these touchpoints are disconnected, the customer feels it first. And by the time the company feels that friction, it may be too late to win that customer back.

A new model for customer experience built on trusted enterprise data

麻豆原创 Customer Experience plays a key role in the expansion of 麻豆原创 and Google Cloud’s partnership to enable a fundamentally new approach to CX鈥攐ne that connects data, AI, engagement, and commerce in real time.

鈥淎gentic customer experience starts deeper in the stack than the customer ever sees鈥攊n the quality of the data, the way processes run, and the strength of the platform underneath. With 麻豆原创 and Google Cloud, we鈥檙e building that foundation together, so AI moves beyond insight to action. This delivers continuous, intelligent experiences with the control, context, and execution required at enterprise scale.鈥�

Muhammad Alam, Member of the Executive Board of 麻豆原创 SE, 麻豆原创 Product & Engineering

麻豆原创 Commerce Cloud endorses Universal Commerce Protocol: powering the next era of agentic commerce

As digital commerce enters the age of AI-driven experiences, a new standard is emerging to reshape how consumers discover and purchase products.

The Universal Commerce Protocol is an open standard designed to enable AI agents to manage the entire shopping journey, from product discovery to checkout and even post-purchase support.

Unlike traditional commerce integrations that rely on fragmented APIs and bespoke connections, UCP creates a shared language for retailers, payment providers, and AI systems. This allows intelligent agents to interact directly with commerce platforms, dramatically simplifying how products are surfaced, evaluated, and purchased.

In essence, UCP transforms commerce from a series of disconnected steps into a unified, agent-driven experience.

, long known for powering enterprise-grade digital commerce for global brands, is now aligning with this new paradigm. As part of the growing UCP ecosystem, 麻豆原创 plans to work with Google to enable its customers to participate in AI-native commerce experiences that extend beyond traditional storefronts and help its merchants鈥� products be discovered and purchased across the Gemini app and Google Search, including AI Mode.

鈥淥ur goal with UCP is to build an open, trusted standard for the future of AI-driven commerce,鈥� said Ashish Gupta, VP/GM, merchant shopping at Google. 鈥淗aving a leader like 麻豆原创 endorse the protocol is critical as we work toward bringing seamless, secure agentic commerce to everyone.鈥�

For merchants, this means dramatically lower integration costs, faster onboarding into new AI-driven channels, and the ability to reach new customers beyond their storefront experiences.

In addition, 麻豆原创 Commerce Cloud will leverage Google Gemini capabilities to power a Shopping Assistant that brands can deploy directly to their own customers. This enables organizations to offer a real-time, AI-driven shopping experience across their digital touchpoints. The Shopping Assistant engages shoppers through chat, voice, and text to help them discover products, answer questions, and turn intent into transactions.

Unlike traditional storefront interactions, the Shopping Assistant can create a persistent, conversational experience that follows the customer across the shopping journey, continuously refining recommendations and guiding decisions in real time.

It can also curate creative ideas鈥攕uch as themed outfits or complete event concepts鈥攂y intelligently combining products based on customer requests. By unifying behavioral signals, real-time inventory, and promotional intelligence, it can increase conversion rates, improve average order value, and ensure every recommendation is both relevant and fulfillable.

麻豆原创 Engagement Cloud and Google Cloud: how agents work together for marketing

This new expanded partnership comes alongside another historic milestone for 麻豆原创 and Google Cloud, announced earlier this year. A fundamentally different approach to marketing execution is now offering marketers a new model for engagement that is built on trusted enterprise data.

By combining customer data and real-time signals like inventory, orders, and fulfillment status with operational truth, marketing teams can now build, launch, and optimize personalized customer engagements, grounded in business context and executed at scale through an autonomous multi-agent framework.

At the heart of this partnership:

• 麻豆原创 Business Data Cloud聽(麻豆原创 BDC) connects semantically rich data across the enterprise with AI to help enable real-time insights and drive personalized interactions grounded in business context. This includes 麻豆原创 Business Data Cloud Connect for Google BigQuery with bidirectional, zero-copy data access and built-in enterprise-grade governance and security.

• Google BigQuery聽unlocks real-time signals across the Google ecosystem, such as geolocation, weather, ad engagement, and rich analytics, for AI-driven segmentation, personalization, activation and analytics.

• 麻豆原创 Customer Experience聽solutions can provide the real-time behavioral context: customer profiles, transactions, orders, service interactions, and consented engagement data.

• 麻豆原创 Engagement Cloud can activate enterprise data, AI insights, and predictions through intuitive tools and AI agents to help securely orchestrate real-time, personalized interactions across the entire customer life cycle.

Why this partnership matters

The collaboration between 麻豆原创 and Google reflects a broader shift in how commerce and marketing teams operate.

For commerce leaders:

• From search to agents: Consumers are no longer just searching. AI agents are acting on their behalf, making decisions, and completing purchases.
• From channels to ecosystems: Commerce is moving beyond owned channels into distributed, AI-powered environments like search, assistants, and chat interfaces.
• From integration to interoperability: Open standards like UCP eliminate the need for one-off integrations, enabling scalable participation in the AI economy.

With UCP, AI agents can seamlessly access product catalogs, manage carts, process payments, and handle post-purchase workflows, all without forcing retailers to rebuild their infrastructure.

For marketing leaders:

• From prompt to performance: Agentic intelligence becomes operational where business goals, enterprise signals, and marketing processes direct AI agents, translating into real customer interactions and automated lifecycle journeys.
• From manual to generative: Advanced generative capabilities powered by Google Gemini models, such as Nano Banana 2, introduce new agentic skills that help marketing teams dynamically generate messaging, imagery, and campaign variations.
• From dark data to unified data context: With every interaction grounded in business context and continuous engagement signals, messages become truly dynamic. Text messages can turn into immersive conversations with Google Rich Communication Services (RCS) and advertising creative and offers can continuously evolve.

Agents collaborate across 麻豆原创 and Google Cloud to personalize, activate, and continuously optimize campaigns in real time across engagement channels and media networks.

鈥淲ith this partnership, 麻豆原创 and Google Cloud bring together connected AI and a unified data foundation to create real-time understanding of the customer and business context. This enables organizations and CX teams to move from fragmented interactions to continuous, intelligent execution鈥攅mbedding AI into end-to-end processes and unlocking meaningful gains in productivity, speed, and business impact.鈥�

Jan Gilg, Global President Customer Success & Americas, Member of the Extended Board

Unlocking new value for enterprises

For 麻豆原创 Commerce Cloud customers, this partnership can unlock several strategic advantages:

• Increased discoverability in AI-driven shopping experiences
• Faster time-to-market through standardized integrations
• Ownership of customer relationships, even in third-party AI environments
• Future-proof architecture aligned with emerging commerce standards

As AI continues to compress the distance between intent and transaction, accessibility to agents becomes just as important as visibility in search results. In this new model, success in commerce is no longer defined by storefront experience alone鈥攊t鈥檚 defined by how effectively your products, data, and systems can be accessed, interpreted, and transacted on by AI agents.

For customers, this partnership unveils a new network of interoperable AI agents, grounded in enterprise data and shared context聽across 麻豆原创 and Google. Organizations can achieve measurable outcomes, including:

• Faster speed-to-market through autonomous campaign and content generation
• Lower operational overhead by eliminating manual execution steps
• Always鈥憃n optimization that continuously improves performance
• Higher ROI through relevant, timely, and consistent engagement at scale

Marketers can spend less time managing workflows and more time shaping strategy, creative direction, and customer value.

鈥淲hat matters is delivering real value to our customers. As customer experience becomes more agentic, organizations need to move faster, stay connected, and operate consistently across every interaction. By bringing commerce, marketing, and service together, we help our customers reduce complexity, respond more quickly, and deliver more relevant experiences that strengthen engagement and drive sustainable growth.鈥�

Manos Raptopoulos, Global President Customer Success Europe, APAC, Middle East & Africa and Member of the Extended Board 麻豆原创 SE

The future of AI-driven commerce and marketing: what this means for your CX strategy

The partnership between 麻豆原创 and Google for marketing and commerce聽marks a foundational shift toward what many are calling an agentic revolution鈥攁 world where AI doesn鈥檛 just assist CX teams and shoppers but actively participates in the buying process and shapes their customer experience.

For enterprise CX leaders, the message is clear: success in this new era will depend on how well your commerce and marketing platform can communicate with AI agents. With 麻豆原创 and Google Cloud, 麻豆原创 Customer Experience is positioning itself鈥攁nd its customers鈥攖o thrive in that future.

Across industries, customers are moving from pilots to production. They are investing in systems that can run pricing, orders, fulfillment, and service end-to-end, with AI actively driving decisions and outcomes. They are not looking for more tools. They are looking for results.

This is where 麻豆原创 has a clear point of view. Customer experience (CX) only works when it is connected directly to execution.

麻豆原创 Customer Experience is integrated with 麻豆原创 Cloud ERP across pricing, order management, fulfillment, billing, and service. AI operates inside these processes using real business data. That means every customer interaction can reflect what the business can deliver.

This is the shift now underway, and it is creating immediate opportunities for partners.

麻豆原创 partners are the ones who bring this to life. They take product capabilities and turn them into working solutions that improve conversion, increase fulfillment accuracy, and reduce service cost.

The next wave of growth will be led by partners who move quickly and build on this foundation.

Customer experience is now measured by what gets done

Customer experience is no longer judged by engagement metrics alone. It is judged by outcomes. Customers expect:

• Accurate pricing at the moment of purchase
• Real product availability, not estimates
• Orders that are fulfilled as promised
• Service that resolves issues without repetition or delay

When these things work, the experience works. When they fail, the problem is immediately visible.

AI is increasing the speed of every interaction. It is also exposing execution gaps faster than ever before. If pricing, inventory, or order data are inconsistent, customers see it instantly.

This is why customer experience and execution can no longer be separated.

AI is now driving actions, not just insights

AI is already acting inside key business processes. As seen in the , in 麻豆原创 CX today:

• Marketing, content, and campaign assistants can orchestrate segmentation, content creation, and optimization based on live performance signals.
• Commerce, merchandising, shopping, and order management assistants can connect discovery, conversion, and fulfillment to real-time inventory and pricing.
• Sales assistants help guide deal qualification and deal execution by linking pipeline signals to pricing, availability, and fulfillment data.
• Case and service management assistants help automate routine interactions while maintaining full context across orders, entitlements, and history.

These are not future scenarios. These capabilities are available and in use. But they only work when they are connected to trusted business data.

Without that, AI creates errors at scale. With it, AI drives measurable improvement.

Autonomous CX connects experience to execution

Autonomous CX connects core products and processes across the business, operating on a shared business context. It brings together 麻豆原创 Commerce Cloud, 麻豆原创 Sales Cloud, 麻豆原创 CPQ, 麻豆原创 Service Cloud, 麻豆原创 Field Service, 麻豆原创 Engagement Cloud for marketing, and 麻豆原创 Cloud ERP across finance, supply chain, and order management.

This is not a set of disconnected applications. It is a unified system where customer interactions and operational processes run on the same data foundation. Pricing, inventory, orders, and service are consistent across every touchpoint.

As a result, AI can move from recommendation to execution, working to ensure that every interaction is grounded in what the business can deliver. It can also remove the integration gaps that slow down CX execution.

麻豆原创 CX partners are moving faster from projects to outcomes

This shift is changing what customers expect from partners. Customers are not asking for system implementations. They are asking for outcomes such as:

• Faster time to deploy
• Higher conversion rates
• Improved order accuracy
• Lower cost to serve

麻豆原创 provides a strong starting point with embedded assistants, standard integrations, and prebuilt industry scenarios. Partners are building on this to deliver complete solutions. This is where differentiation happens.

Where partners are creating value today

The opportunity is not theoretical. It is already visible in active partner work.

Across 麻豆原创 CX:

• The cloud ERP edition of 麻豆原创 Commerce Cloud can connect storefront, pricing, ordering, and fulfillment in one model.
• 麻豆原创 Revenue Growth Management and 麻豆原创 Retail Execution support trade planning and in-store performance.
• Intelligent applications help package AI use cases across marketing, sales, and service.
• 麻豆原创 Service Cloud with partner integrations such as Parloa enables automated, context-aware service interactions.

麻豆原创 CX partners are turning these capabilities into repeatable offerings. Examples include:

• Industry packages for retail and CPG combining commerce, pricing, and fulfillment
• Preconfigured deployments of 麻豆原创 Sales Cloud and 麻豆原创 Service Cloud that reduce time to go-live
• Integration connectors linking 麻豆原创 CX with existing commerce, loyalty, and service platforms
• Extensions to CPQ and sales workflows that improve deal margin and approval speed
• Service automation scenarios that reduce manual case handling using real order and entitlement data
• AI-driven discovery connected directly to 麻豆原创 Commerce and 麻豆原创 Commerce, order management

These are practical, deployable solutions that can deliver measurable results.

The ecosystem is expanding what鈥檚 possible

麻豆原创 is strengthening this model through partnerships. Recently announced partnerships with companies such as Amazon Web Services, Google Cloud, Parloa, and Vercel enable new interaction models like conversational commerce, AI-driven search, and composable digital experiences.

What matters is that these experiences connect back to 麻豆原创 for execution. Orders, pricing, fulfillment, and service remain consistent across every channel. This gives partners the freedom to innovate on the experience layer while relying on 麻豆原创 for reliable execution.

A new economic model for partners

The economics for partners are changing. With Autonomous CX, partners can build:

• Industry solutions that can be reused and scaled
• Implementation packages that shorten delivery timelines
• Extensions and integrations that apply across customers
• Ongoing services for AI optimization and governance
• New offerings built around AI assistants, AI agents, and orchestration
• Higher-value transformation programs that combine AI, data, and process design

This creates a more predictable and repeatable revenue model. It also strengthens long-term customer relationships.

Now is the time to act with 麻豆原创 CX

Customers are making decisions now. They are selecting platforms and partners that can deliver AI-driven execution across customer experience. They are looking for partners who can:

• Connect CX to ERP processes
• Deliver solutions that work out-of-the-box and scale
• Improve measurable business outcomes

麻豆原创 provides the foundation. The platform is in place. The capabilities are real. The next step is execution.

Partners who move now can define the use cases, build the offerings, and lead in their industries. The momentum is already building. This is the moment to accelerate it.

What partners should do next

To move from opportunity to execution, partners can act now.

• See what is available today. Explore the latest AI-driven CX capabilities and partner opportunities in the .
• Understand the foundation for Autonomous CX. Learn how 麻豆原创 connects experience to execution with the .
• Deepen expertise and accelerate readiness. Gain a stronger understanding of Autonomous CX and partner use cases:
• Engage now and shape the next wave. Join the upcoming executive briefing to understand how to position and deliver these solutions:
  • June 18:
• Strengthen credibility and scale impact. Advance your go-to-market and positioning through certification:

麻豆原创 is not asking partners to start from scratch. The platform, capabilities, and ecosystem are already in place. The opportunity now is to build, differentiate, and lead.

Karl Fahrbach is chief partner officer at 麻豆原创.
Balaji Balasubramanian is president and chief product officer for 麻豆原创 Customer Experience.

The results, which are driven by authentic customer feedback, highlight the strength of 麻豆原创 CX, particularly in the enterprise segment.

鈥淓arning a Leader position in a G2 Report is highly competitive and rooted in verified customer reviews,鈥� said Godard Abel, co-founder and CEO, G2. 鈥淐ongratulations to 麻豆原创 Customer Experience for achieving this distinction. Buyers can be confident this ranking reflects the authentic experiences of real users.鈥�

Following are a few standout achievements:

• :聽Reigned supreme in the commerce space, achieving the coveted聽#1 Rank聽in the聽Enterprise Grid庐 Report for Order Management. It was also recognized as a Leader for E-Commerce Platforms and Omnichannel Commerce.
• :聽Showcased its versatility and power, earning a Leader position in multiple reports for CRM and Sales Analytics. It secured an impressive聽#2 Rank聽in the聽Enterprise Grid庐 Report for CRM.
• :聽Named a Leader in the highly competitive聽Enterprise Grid庐 Report for Help Desk, solidifying its position as a top-tier solution for customer service excellence.
• : Dominated the marketing and engagement space, earning a whopping 51 G2 Summer 2026 badges, including #1 in the Momentum Grid庐 for Location-Based Marketing and #1 in the Enterprise Grid庐 for Email Deliverability, up from #3. Also recognized as a Leader in Marketing Automation, Personalization Engines, Customer Journey Analytics, SMS Marketing, and Loyalty Management, with new country reports added across Spain, Italy, and France.

This verified success is powered by pioneering work in agentic AI with Joule, 麻豆原创鈥檚 generative AI copilot. They鈥檝e moved beyond simple automation to deploy autonomous AI agents that can reason, plan, and act across the entire customer journey.

Customers are enthusiastically adopting these intelligently automated capabilities. The is transforming keyword searches into conversational discovery and guided purchases, while the is autonomously resolving inquiries, improving satisfaction, and reducing contact center load.

The shift to AI-driven engagement is delivering real-world value and simplifying complex processes. Using , customers can embed AI-powered guidance directly into workflows, transforming employees into experts. By enabling teams to work smarter and faster across 麻豆原创 Commerce Cloud, 麻豆原创 Sales Cloud, 麻豆原创 Service Cloud, and 麻豆原创 Engagement Cloud, unlocking the full potential of your 麻豆原创 investment and delivering the exceptional outcomes that customers demand has never been easier. By automating routine and complex workflows with out-of-the-box AI agents, teams are freed to focus on strategic, high-value customer interactions, driving unprecedented efficiency and loyalty.

G2 SUMMER 2026 Grid庐 Reports: What 麻豆原创 CX customers are saying

麻豆原创鈥檚 commitment to delivering exceptional customer experiences is reflected in the high praise for聽麻豆原创 Commerce Cloud, 麻豆原创 Sales Cloud, 麻豆原创 Service Cloud, and 麻豆原创 Engagement Cloud. This recognition is a true testament to the trust our customers place in 麻豆原创 CX when it comes to powering their most critical business functions.

Here鈥檚 what users said about 麻豆原创 Commerce Cloud:

鈥淚 like 麻豆原创 Commerce Cloud’s ability to handle extreme complexity while unifying diverse business models (B2B, B2C, B2B2C) on a single platform. Its unified approach allows us to manage operations on a single technology stack, eliminating data silos, reducing operation costs, and giving us total visibility into our entire supply chain.鈥�

– Suryakant G, Managing Director ()

鈥淭he onboarding resources and 麻豆原创 support ecosystem make implementation easier, while its AI-driven recommendations, search optimization, and customer behavior insights help deliver more personalized experiences and smarter business decisions.鈥�

– Vedant G., Manager ()

Here鈥檚 what users said about 麻豆原创 Sales Cloud:

鈥淲hat really clicks for me with 麻豆原创 Sales Cloud is how effortlessly it syncs sales and marketing data in one smart dashboard. It gives me crystal-clear insights into customer behavior and pipeline status, which is pure gold for crafting targeted, automated email campaigns that feel timely and personal.鈥�

– Grecia L., Marketing Automation Specialist ()

鈥淚t addresses inefficiency and lack of visibility in the sales cycle. The main benefit is a faster, more predictable revenue stream, supported by AI and real-time data integration.鈥�

– Kuldeep D., Senior Technical Specialist ()

Here鈥檚 what users said about 麻豆原创 Service Cloud:

鈥淚 appreciate its automated processes and great SLA tracking, which helps the team to keep track of time and not miss deadlines. I also like the AI replies that enable quick responses by searching through the knowledge base. The automatic ticket routing is a valuable feature as it aids in queue management, ensuring efficient handling of customer issues.鈥�

– Kelvin E., Support Engineer ()

鈥溌槎乖� Service Cloud addresses the challenge of fragmented customer service data by consolidating all interactions and cases into one platform. This gives me clearer visibility into customer history, helps resolve issues more quickly, and supports consistent omnichannel service.鈥�

– Rekha S, Content Creator ()

Here鈥檚 what users said about 麻豆原创 Engagement Cloud:

鈥淚 love how 麻豆原创 Engagement Cloud personalizes campaigns at scale and automates customer journeys using real-time data and AI insights.鈥� ()

Shaping the future of customer experience, together

The G2 leadership awards validate the 麻豆原创 CX strategy, while investment in agentic AI defines the future.

Users can share feedback on G2 for聽, for聽, and聽for .听

The version for , part of the 麻豆原创 Services and Support portfolio, is the helping organizations adopt, activate, and scale the 麻豆原创 Customer Experience and AI innovations announced at 麻豆原创 Sapphire.

The proactive, expert-led engagement model is built to de-risk transformation, accelerate time to value, and sustain measurable outcomes across customer experience initiatives. It combines guided adoption, prescriptive functional and technical assistance, AI-powered best practices, and continuous value realization aligned to the realities of modern customer experience (CX): AI at the core, unified data, omnichannel at scale, retention over acquisition, service-led growth, and persistent skills gaps in a rapidly evolving digital landscape.

At its heart, the Advanced Success Plan for 麻豆原创 Customer Experience brings together the right expertise at the right time, program governance, solution experts, value advisors, and adoption specialists. This helps teams execute faster and smarter with 麻豆原创 Customer Experience.

What sets the Advanced Success Plan apart

• Outcome-based: Business outcomes and key value indicators are co-defined with teams, with milestones and workstreams aligned to deliver measurable Autonomous CX results.
• Proactive by design: AI Assistants, adoption checks, and innovation accelerators are embedded throughout, reducing risk and compressing time to value as agentic capabilities evolve.
• Continuous enablement: Role-based best practices and coaching are tied directly to the Autonomous CX road map, closing skills gaps at pace as new AI and platform capabilities become available.
• Cross-solution orchestration: Unified processes and shared business context across marketing, commerce, sales, and service break silos and enable enterprise-scale execution.

This is the first of a planned series to deep dive on the topics below. Here, we start with introducing how the Advanced Success Plan for 麻豆原创 Customer Experience helps operationalize seven macro trends shaping modern customer experience.

1. AI鈥憄owered customer experiences

AI now underpins everything from next best engagement to intelligent service resolution. The Advanced Success Plan embeds AI adoption patterns directly into the delivery approach, identifying high value use cases, calibrating data prerequisites, and guiding model governance.

The results are prioritization of high鈥慽mpact starting points, a plan to scale with guardrails, accelerating time from pilot to production and grounding every decision in 麻豆原创鈥檚 CX AI capabilities and product road map.

2. Hyper鈥�personalization at scale

Personalization demands more than algorithms; it requires clean, consent鈥慳ware data, robust decisioning, and experimentation discipline. The Advanced Success Plan delivers:

• Data readiness assessments and integration patterns to enrich customer profiles and segments
• Governance and testing playbooks to validate personalization hypotheses at scale
• Prescriptive journeys to operationalize next best action across every customer channel

The result: hyper personalization moves from proof of concept to standard operating model.

3. Unified customer data and breaking down silos

Siloed data undermines CX. We help establish a unified data foundation and harmonized identities, aligning business, data, and integration teams. With technical guidance and adoption accelerators, users can move faster toward a single view of the customer to fuel analytics, personalization, and service excellence.

The results are unified profile use cases, data quality baselines, and source鈥憃f鈥憈ruth decisions to reduce duplication and latency.

4. Omnichannel commerce and B2B digital transformation

Modern buyers expect seamless journeys across web, mobile, marketplace, and partner portals, especially in B2B. The plan accelerates omnichannel capability build鈥憃ut by uniting commerce, order sourcing, pricing, and fulfilment patterns, supported by outcome鈥慴ased governance.

The result: Channel consistency, catalogue and contract complexity, and the alignment of service and sales motions are all addressed, driving measurable improvement in conversion rates and repeat purchase.

5. Customer retention over acquisition

Acquisition costs are rising and retention is the new growth engine. The Advanced Success Plan helps operationalize retention strategies, churn prediction, intelligent engagement, loyalty, and proactive service across the CX stack.

The result: We align metrics such as retention rate, customer lifetime value, and service鈥憈o鈥憆evenue contribution, and ensure the data foundation supports them.

6. Service as a revenue driver

Service is no longer a cost center; it鈥檚 a growth channel. We guide users to productize services, monetize value鈥慳dded offerings, and embed outcome鈥慴ased contracts. The plan includes:

• Playbooks for cross鈥憇ell/upsell from service interactions
• Knowledge and field service patterns to improve first鈥憈ime fix and attach rates, KPI frameworks for service鈥憀ed growth

The result: With prescriptive governance and AI鈥慸riven intelligence, service organizations move from reactive cost management to consistent, measurable contribution to top鈥憀ine revenue and customer retention.

7. Navigating digital transformation complexity and skills gaps

Large transformation programs falter on orchestration and capability enablement. The Advanced Success Plan addresses both by:

• Establishing a cadence of value sprints and decision forums
• Providing role鈥慴ased enablement covering functional and technical assistance, data, product ownership, end-user adoption, and change management
• AI-guided best practices embedded throughout delivery to eliminate rework and accelerate quality outcomes across Industry AI scenarios

Organizations execute with confidence, even amid shifting requirements, resource constraints, and rapidly evolving agentic AI capabilities.

Measurable outcomes

• Accelerated time to first value through prioritized, AI-ready use cases aligned to capabilities
• Higher adoption and sustained performance via continuous enablement
• Reduced program risk through proactive governance, telemetry, and structured decision forums
• Measurable gains in conversion rates, customer retention, and service-led revenue contribution across the full CX stack

Getting started

• Define Autonomous CX priorities: Identify two to three priority outcomes for the next two quarters facilitated by the 麻豆原创 Value Management service.
• Assess readiness: Evaluate data, integration, governance, and enablement gaps to define a 12 to 18 month engagement plan.
• Engage the Advanced Success Plan: Align workstreams, milestones, and metrics with our expert team.听
• Industrialize and scale: Convert proven delivery patterns into reusable accelerators, deployable across regions and lines of business.

This series will examine each of the seven trends in depth, demonstrating how the Advanced Success Plan for 麻豆原创 Customer Experience translates CX strategy into repeatable execution and measurable business outcomes.

Tara Tracey is a global product owner at 麻豆原创.

AI is what brings that ambition within reach. It helps companies act faster, personalize at scale, and engage in new ways. But it is also raising expectations. Every interaction now reflects how well the business runs.

When a customer places an order or asks for help, the experience depends on what happens behind the scenes. If pricing is inaccurate, inventory is uncertain, or fulfillment falls short, the experience breaks.

That is why customer experience is now defined by execution. Customers do not experience systems or intent. They experience outcomes.

Agentic AI can increase speed, intelligence, and personalization. But speed alone does not improve customer experience. It amplifies what is already there. When execution is aligned with process, data and governance, AI drives better outcomes. When it is not, AI exposes the disconnect.

Aligning experience and execution

Autonomous CX brings agentic AI directly into the processes that run the business instead of layering it on top of disconnected systems. It connects AI assistants across marketing, commerce, sales, and service onto a shared business context across 麻豆原创 CX, 麻豆原创 Cloud ERP, supply chain, and connected systems. Orders, inventory, pricing, and financials are defined once and used consistently, so decisions are based on live operational reality.

At the center of this shift are AI assistants and autonomous agents. Assistants coordinate multiple agents across end-to-end customer workflows, from discovery to fulfillment, engagement to service, and issue to resolution.

At 麻豆原创 Sapphire, we highlighted assistants that make this real across the portfolio:

• In marketing, Content Assistant and Campaign Assistant orchestrate intent understanding, content creation, segmentation, optimization, and campaign execution within governance controls.
• In commerce, Merchandising Assistant, Shopping Assistant, and Order Management Assistant connect discovery, conversion, and fulfillment to operational reality.
• In sales, Sales Assistant, Deal Qualification Assistant, and Deal Closing Assistant move sellers from signal to execution.
• In service, Case Management Assistant and Service Management Assistant improve resolution and service quality, with additional assistants purpose-built for self-service, HR service, and accounts receivable workflows.

AI-driven discovery and engagement grounded in business reality

麻豆原创鈥檚 collaboration with Google follows the same principle: connect AI-driven discovery and engagement to business execution.

Together, 麻豆原创 and Google are focused on three priorities: first, applying the latest AI models, including Gemini, to deliver high-quality customer experiences; second, supporting industry standards and open protocols to enable interoperability across ecosystems; third, enabling seamless, personalized journeys across channels and Google surfaces such as Shopping and Gemini.

By combining 麻豆原创鈥檚 governed business data with Google鈥檚 AI capabilities, assistants and agents can connect customer intent from storefronts, search, and AI-driven channels to 麻豆原创 commerce and order processes. This ensures that what customers see reflects what the business can fulfill.

This is also why 麻豆原创 is adopting and expanding how 麻豆原创 product data can power AI-driven experiences wherever customer intent originates. This keeps experiences aligned with pricing, inventory, and fulfillment in real time.

麻豆原创 Commerce Cloud innovations

麻豆原创 continues to be recognized in analyst evaluations, including the Gartner庐 Magic Quadrant™ for Digital Commerce, where 麻豆原创 has been positioned as a Leader for 11 consecutive times.

, trusted by the largest enterprises, now extends to mid-market and growing companies on 麻豆原创 Cloud ERP. The new 麻豆原创 Commerce Cloud, cloud ERP edition delivers a standardized, end-to-end approach, reducing complexity, leveraging AI natively, and accelerating time to value. It connects discovery through fulfillment via tight integration with 麻豆原创 Cloud ERP.

For digitally mature organizations, 麻豆原创 is expanding composable commerce with new and modular cart and checkout services. These services integrate with core processes such as pricing, promotions, loyalty, tax, payments, inventory, sourcing, and order management across 麻豆原创 and non-麻豆原创 touchpoints. This helps organizations modernize their architecture while maintaining end-to-end execution.

麻豆原创 is also expanding its ecosystem with Vercel to accelerate storefront development and deployment with optimized performance, scalability, and composable front-end experiences.

In payments, 麻豆原创 Unified Payment, powered by Adyen, embeds global processing directly into the commerce flow to simplify integration and improve conversion. 麻豆原创 also continues to enhance its open payment framework with pre-integrated providers, such as Checkout.com and PayPal, giving customers flexible provider choices that are easy to configure and use.

Together, these capabilities reduce total cost of ownership, speed deployment, and make it easier to deliver better experiences at scale.

Sales execution turns insight into action

Customer experience extends into sales execution, where teams need clear next steps and confidence those actions can be fulfilled.

We introduced new innovations, including field sales capabilities for retail execution processes in consumer goods companies and other field-selling environments. These capabilities provide rich mobile experiences that work offline, making it easier to plan store visits, capture in-store activity, and manage execution in real time.

Sales leaders gain connected insights tied directly to pricing, inventory, and order processes, leading to more consistent execution and better outcomes.

Scaling trusted autonomous service

Autonomous CX is strengthened through partnerships that extend execution while preserving trust and governance.

Our combines its agentic AI-driven voice and digital self鈥憇ervice with service, order, and entitlement data from 麻豆原创 Service Cloud. AI-driven automation can handle routine interactions with full context, escalating seamlessly and with continuity to service teams when human expertise is needed. This approach helps organizations scale service without breaking trust and ensures customer interactions remain connected to real business processes.

麻豆原创 is also expanding its partnership with Amazon to scale AI-driven service across voice and digital channels, enabling faster, more consistent resolution while keeping service execution grounded in real-time business data.

Industry AI in action

We are also showcasing Industry AI scenarios that demonstrate how assistants and autonomous capabilities operate in real business environments.

Autonomous Revenue Growth Management supports trade planning teams and key account managers in consumer products companies that sell through retailers, with applicability to agribusiness and wholesale distribution. Industry鈥憇pecific Joule Assistants provide AI鈥慸riven insights across trade planning and execution, helping teams identify growth opportunities, optimize commercial terms and respond more quickly to performance signals. The result is more predictable growth with fewer downstream exceptions.

Unified commerce supports merchandising and operations teams across retail, wholesale, and direct-to-consumer models. Unified commerce connects demand, inventory, and customer data across channels, with Joule Assistants guiding decisions on assortment, pricing, and placement. The result is more consistent execution and faster decisions.

The next phase of customer engagement

Across these innovations and Industry AI scenarios, the pattern is clear. AI delivers value only when it acts on shared, trusted context. When experience and execution stay aligned, speed becomes a source of trust instead of risk.

This is how 麻豆原创 is approaching the future of customer experience: as a coordinated system where every decision is visible, and every promise can be kept.

Balaji Balasubramanian is president and chief product officer of 麻豆原创 Customer Experience.

The CX innovations and Industry AI scenarios highlighted here are planned for general availability in Q3 2026.
The capabilities announced as part of 麻豆原创鈥檚 Autonomous Enterprise run across 麻豆原创 Cloud ERP, including 麻豆原创 Cloud ERP Private.
Gartner does not endorse any company, vendor, product or service depicted in its publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner publications consist of the opinions of Gartner鈥檚 business and technology insights organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this publication, including any warranties of merchantability or fitness for a particular purpose.
Gartner and Magic Quadrant are trademarks of Gartner, Inc., and/or its affiliates.

In his kickoff keynote at 麻豆原创 Sapphire Orlando, Florida, Klein and other 麻豆原创 Board members detailed how 麻豆原创 plans to bring agentic AI to the world’s most critical business workflows so that humans and AI can meet the accelerating demands of global business profitably, strategically, and safely.

鈥淭oday I鈥檓 super proud to launch our new 麻豆原创 Business AI Platform, which forms the basis for our vision of the future of business: the Autonomous Enterprise, where agents run the business and you can focus on what truly matters,鈥� Klein said.

Enterprise AI is at an inflection point, Klein told his 30,000-strong in-person and virtual keynote audience, and 麻豆原创 is in a unique position to deliver what customers need to turn their businesses into autonomous enterprises.

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The business AI imperative

Across industries, organizations are investing heavily in artificial intelligence, yet many still struggle to translate that investment into meaningful business value. At 麻豆原创 Sapphire, the message was clear: This isn鈥檛 a technology problem; it鈥檚 a context and execution problem.

While 80% accuracy may be sufficient for consumer AI applications, Klein said, 鈥淓ighty percent is just not good enough when you run the world鈥檚 most business-critical businesses. They [LLMs] should not guess; they should deliver accurate, compliant, and secure outcomes.鈥� 

Klein acknowledged that while adoption of AI has become near-universal, tangible business value remains elusive. Citing a recent Stanford AI survey, he noted that almost every company is now using AI, but seeing only limited return.

The reason, he argued, lies in a structural gap. Above the waterline of enterprise AI, LLMs continue to improve at tasks trained on publicly available data, while below it lies what enterprises truly need: AI that understands mission-critical business data, end-to-end processes, and operates within security, compliance, and governance frameworks.

ERP as the foundation for business AI

麻豆原创鈥檚 answer to this challenge begins with what Klein described as 鈥渢he brain of every company: its ERP system.鈥� For over 50 years, 麻豆原创 has had solutions with incredibly deep process and data domain know-how alongside the governance requirements, compliance controls, and company-specific configurations that define how businesses actually run.

Now, as part of the company鈥檚 new vision, 麻豆原创 plans to infuse this institutional knowledge into AI agents, enabling them to navigate thousands of business processes, select from more than 7 million data fields, and verify identity and access authorizations before returning any output.

鈥淲e鈥檙e bringing together LLMs with 50 years of business know-how stored in our ERP. But to do this, we had to do nothing less than completely reinvent our company,鈥� he told the audience. 鈥淭oday we are very excited to show you the new 麻豆原创 and our vision for the Autonomous Enterprise.鈥�

麻豆原创 Business AI Platform

To bring this vision to life, 麻豆原创 executives on stage announced a series of important innovations, beginning with the launch of the new 麻豆原创 Business AI Platform, a unified architecture bringing together 麻豆原创 Business Technology Platform, 麻豆原创 Business Data Cloud, and AI Foundation under a single roof.

鈥淭he heart of this new platform is the rich context layer,鈥� said Klein. 鈥淗ere, we infuse the deep ERP business domain know-how into the AI agents. Through our knowledge graphs, our AI agents have now a compass, a map, to find the right process and data in your ERP universe. And to provide the agents even more context, we are also introducing our new 麻豆原创 Domain Models. They have been trained on 麻豆原创’s code to even better understand the business logic of your company.鈥�

But, he said, 麻豆原创 is going further: 鈥淏ecause you run your business not only with 麻豆原创 solutions, our AI agents have to also understand non-麻豆原创 data. That’s why we included our 麻豆原创 Business Data Cloud in the context layer to build a single semantical data layer across 麻豆原创 and non-麻豆原创. No more silos, no spaghetti data sprawl鈥攂ecause no AI agent can compensate for a broken data model.鈥�

Echoing Klein, 麻豆原创 CTO Philipp Herzig, who presented the platform in detail, said it has been designed to close the agent adoption gap in the enterprise by delivering outcome, speed, enterprise-readiness, and context. 鈥淚t’s the place where you build, contextualize, reason, and govern AI,鈥� he said.

Herzig explained that the platform is structured around three layers: the context layer which Klein referenced, the build layer, and the governance layer. 鈥淎gents are only as powerful as the context they operate on,鈥� he said. 鈥淟acking context is the number one reason why enterprise AI projects fail to deliver value.鈥�

Within the build layer of the new platform, the new Joule Studio is designed to understand a company鈥檚 business challenges and enables the building of new AI agents quickly and easily.

The third tier is the governance layer, anchored by the new 麻豆原创 AI Agent Hub built on 麻豆原创 LeanIX. This provides a single command center to discover, manage, and govern all AI agents鈥斅槎乖� and non-麻豆原创. It will be generally available in Q3 and included in 麻豆原创 Business AI Platform at no additional charge.

Underscoring the changing AI marketplace, Herzig was joined on stage by KPMG Global Head of Advisory Rob Fisher, who told the audience: 鈥淲hat I鈥檓 hearing from clients is a clear shift; they鈥檙e moving from AI pilots to embedding integrated AI and agents into how work gets done. Where we see leaders really separating from the pack is in the execution and the organizational adaptability.鈥�

麻豆原创 Autonomous Suite

Building on the platform, 麻豆原创 Executive Board Member Muhammad Alam, 麻豆原创 Product & Engineering, announced the transformation of 麻豆原创鈥檚 SaaS application portfolio into the 麻豆原创 Autonomous Suite, described as the most significant evolution of 麻豆原创鈥檚 applications business in the company鈥檚 history.

The suite spans five domains: Autonomous Finance, Autonomous Spend, Autonomous Supply Chain Management, Autonomous HCM, and Autonomous CX, with more than 200 agents and over 50 assistants available in the coming months. Each assistant is mapped to core business roles and carries defined KPIs tracked through 麻豆原创 AI Agent Hub.

鈥溌槎乖� Autonomous Suite brings together the depth of our process expertise, semantically rich data, and built-in governance and compliance,鈥� said Alam. 鈥淭hese agents are designed with outcomes as a core objective. Each assistant has a defined set of ROI KPIs that you can expect it to deliver.鈥� 

鈥淯nderpinning the autonomous suite are out-of-the-box agents鈥攈undreds of agents cutting across all core business processes,鈥� he shared. 鈥淭hese agents come together into what we call assistants, or Joule Assistants. We’ve mapped these assistants to roles across the core processes of an organization, because we know that the first step 
in realizing value from AI is to empower your people to do more, do it better, or do things that just weren’t possible to be done before.鈥�

Turning to Joule itself, Muhammad said 麻豆原创 is fundamentally reimagining how users will interact with 麻豆原创 applications in the future.

鈥淲e call this Joule spaces and along with the familiar Joule conversations experience and Joule Studio 2.0, it is now part of what we call Joule Work,鈥� he explained.

鈥淛oule Work represents a massive step forward in super-charging the capabilities of Joule as we know it today,鈥� Alam said. 鈥淲ith Joule Work, we’re bringing a claw-based agentic harness to Joule along with computer and file access, better support for open standards such as MCP and A2A, access to a more complete knowledge base, and, of course, amazing visualizations on the fly.鈥�

Industry AI: H&M and Sector-Specific Transformation

During the keynote, 麻豆原创 Chief Operating Officer Sebastian Steinhaeuser introduced the Industry AI initiative, delivering AI-powered solutions built on decades of sector-specific expertise across 26 industries. In life sciences, he highlighted how 麻豆原创 customer Takeda is achieving up to 10% productivity gains, up to 25% reduction in revenue loss from stock-outs, and up to five percent reduction in safety stock through Autonomous Regulated Manufacturing.

He was also joined on stage by H&M Group CDIO Ellen Svanstr枚m, who discussed how the fashion retailer is embedding AI across its value chain. Built on RISE with 麻豆原创, 麻豆原创 Business Data Cloud, 麻豆原创 Commerce Cloud, and 麻豆原创 SuccessFactors solutions, H&M has developed a Store Intelligence Agent that processes real-time signals to generate actionable recommendations for store managers. Svanstrom also demonstrated the AI-powered InStore Concierge, a customer-facing agent that bridges digital and physical retail through personalized outfit recommendations and real-time availability.

RISE with 麻豆原创 and 麻豆原创 GROW: Path to the Autonomous Enterprise

Returning to the keynote stage, Klein emphasized that technology adoption alone does not create business value. Simply plugging AI agents into your system landscape will drive zero value, he said. 鈥淢oving to the Autonomous Enterprise requires serious change management. Adoption of AI goes hand-in-hand with business process change and end user enablement.鈥�

To support customers on this journey, 麻豆原创 announced a comprehensive reset of its RISE with 麻豆原创 and 麻豆原创 GROW offerings. RISE with 麻豆原创 customers will receive contractual commitment to activate three Joule Assistants within the first year, with the Max Success Plan extending adoption across the full enterprise.  

麻豆原创 GROW customers will receive more than 20 AI assistants from day one, with an AI-enabled toolchain designed to support go-live in weeks. New partnerships with Palantir and Accenture will support the most complex migration scenarios.

Closing: The Autonomous Enterprise

Klein closed the keynote by asking Joule to summarize the key takeaways and noting that 麻豆原创 is evolving from being a software company to becoming a business AI company.

鈥淲e showed how to turn the promise of business AI into reality with 麻豆原创 Business AI Platform, which provides the data processes and governance AI need to deliver accurate and secure outcomes at scale; we introduced the 麻豆原创 Autonomous Suite, where applications reason, decide, and act for you; and we showed how to manage change management with RISE with 麻豆原创. Together with customers and partners, we showed how 麻豆原创 is helping companies realize the vision of the Autonomous Enterprise.鈥�

鈥淲e鈥檝e been reinventing how businesses run for over 50 years, and now by infusing 麻豆原创鈥檚 ERP brain into the new 麻豆原创 Business AI Platform, we鈥檙e solving one of the biggest challenges businesses are facing today: how to turn AI into business value,鈥� he said. 鈥淚t鈥檚 the end of long negotiations, supply chain disruptions, financial blind spots, and the beginning of better: Welcome to the Autonomous Enterprise.鈥�

The company introduces a unified 麻豆原创 Business AI Platform, deepening partnerships with Anthropic, Amazon Web Services, Google Cloud, Microsoft, NVIDIA and Palantir

ORLANDO聽鈥� At 麻豆原创 Sapphire in 2026, (NYSE: 麻豆原创) introduced the to help enhance the world’s most critical business workflows, so that humans and AI work together to meet the accelerating demands of global business profitably, strategically and safely.

鈥淔or the mission-critical processes of our customers, ‘almost right’ just isn鈥檛 good enough,鈥� said Christian Klein, CEO of 麻豆原创 SE. 鈥淏y uniting 麻豆原创 Business AI Platform with 麻豆原创 Autonomous Suite, we anchor AI agents in the business processes, data and governance so they can deliver accurate, compliant and secure outcomes, unlocking new sources of revenue and meaningful cost savings.鈥�

The Autonomous Enterprise includes a unified AI platform for building, contextualizing and governing agents, an autonomous suite that executes core business operations and a new user experience that redefines how people work with enterprise software.

Introducing 麻豆原创 Business AI Platform

麻豆原创 Business AI Platform is a new foundation for building and deploying enterprise AI grounded in real business context. 麻豆原创 Business AI Platform now unifies 麻豆原创 Business Technology Platform, 麻豆原创 Business Data Cloud and 麻豆原创 Business AI into a single, governed environment.

At its core is the 麻豆原创 Knowledge Graph solution, which gives AI agents a structured map of business entities, processes and relationships across a customer’s 麻豆原创 landscape. Joule Studio is 麻豆原创’s AI-first solution for building enterprise agents, applications and agentic workflows. Developers can build using the no-code, pro-code and AI frameworks of their choice on 麻豆原创-managed infrastructure that is secure, scalable and optimized for enterprise AI.

Deploying 麻豆原创 Autonomous Suite Across Every Business Function and Industry

Building on this foundation, 麻豆原创 also introduced 麻豆原创 Autonomous Suite, which enables 麻豆原创’s existing business applications with AI agents capable of running processes from start-to-finish.

The suite will deploy more than 50 domain-specific Joule Assistants across finance, supply chain, procurement, human capital management and customer experience. These assistants will automate end-to-end processes by orchestrating a subset of over 200 specialized agents to execute precise tasks. For example, the new Autonomous Close Assistant can compress the financial close process from weeks to days by automating journal entries, reconciliation and error resolution across the entire process.

麻豆原创 also launched Industry AI, expanding its deep industry portfolio through seven autonomous solutions that will enable start-to-finish industry processes and embed sector-specific process logic, data models and regulatory requirements. At 麻豆原创 Sapphire, 麻豆原创 showcased its work with European energy giant RWE to leverage Industry AI, helping reduce unplanned downtime across its offshore wind turbines. With 麻豆原创’s Autonomous Asset Management scenario, AI agents are designed to analyze data from thousands of past incidents, identify the likely root cause and generate pre-filled work orders with the right tools and proven fixes from other sites.

Designing the Autonomous User Experience

The company also revealed Joule Work, redefining how users engage with 麻豆原创 software. Instead of navigating individual applications and entering data across several screens, users will now interact primarily with Joule. By describing a desired business outcome, Joule will orchestrate the right combination of workflows, data and agents to get it done.

Joule Work goes beyond conversation, proactively surfacing relevant insights and automating routine tasks behind the scenes so work moves forward even when humans aren’t actively steering it. It will be available on desktop, mobile and voice across 麻豆原创 and non-麻豆原创 systems.

Accelerating the Customer Journey Toward Autonomy with 鈧�100 Million Infusion

麻豆原创 evolved its customer and partner programs to help accelerate the organization’s journey to the Autonomous Enterprise. To catalyze adoption, the company has launched a 鈧�100 million fund for 麻豆原创 partners to help customers deploy 麻豆原创-built AI assistants and agents. The fund is also available to partners that extend or build new partner agents on the new 麻豆原创 Business AI Platform using Joule Studio.

麻豆原创 has enhanced its RISE with 麻豆原创 and 麻豆原创 GROW offerings to accelerate AI adoption. Both include access to the Joule Assistants portfolio; RISE with 麻豆原创 customers will have three assistants activated within their first year, while 麻豆原创 GROW customers receive full portfolio access at onboarding. 麻豆原创 S/4HANA on-premises and 麻豆原创 ERP Central Component (麻豆原创 ECC) customers are not excluded: those that commit to transitioning the majority of their current landscape to 麻豆原创 Cloud ERP gain access to select AI scenarios, bridging the gap between their current landscape and their cloud destination

麻豆原创 also introduced new agent-led transformation tooling that can reduce ERP migration efforts by more than 35 percent, driving faster and more predictable projects by automating system analysis, code remediation, configuration and testing at scale.

Lastly, 麻豆原创 announced a full slate of strategic partnerships across each category:

• Platform and suite partnerships include Anthropic, with Claude among the foundation models 麻豆原创鈥檚 AI platform will leverage to power Joule agents across HR, procurement and supply chain; Amazon Web Services, bringing zero-copy data integration between 麻豆原创 Business Data Cloud and Amazon Athena; Google Cloud and Microsoft, enabling bidirectional agent-to-agent interoperability between Joule and external agent frameworks; Mistral AI and Cohere, delivering sovereign model options on 麻豆原创’s cloud infrastructure; , providing visual AI workflow orchestration inside Joule Studio; NVIDIA, whose OpenShell provides the trusted secure runtime for Joule Studio; and , bringing AI agents into 麻豆原创 Service Cloud to handle customer interactions with full access to business data and service processes.
• Implementation partnerships include Palantir and Accenture, partnering on complex data migration scenarios, and for AI-powered cloud ERP migrations.

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Customers can deploy Joule Agents in 麻豆原创 CX Solutions to build, launch, and optimize marketing campaigns

Gemini Enterprise acts as a central hub for agents to take action across 麻豆原创 and Google Cloud platforms

LAS VEGAS 鈥� (NYSE: 麻豆原创) and Google Cloud today announced a new partnership that will help marketers put AI agents to work at scale.

Through new integrations between the 麻豆原创 Engagement Cloud, 麻豆原创 Customer Experience (麻豆原创 CX) and Joule solutions and Gemini Enterprise, joint customers can now deploy agents that securely access unified data stored across both ecosystems to execute complex marketing strategies based on high-level goals defined by the user.

Together, 麻豆原创 and Google Cloud provide a unified foundation for data and AI agents to operate across both ecosystems. Gemini Enterprise will act as a central hub for data integrations and multi-agent coordination, allowing agents to take action across a customers鈥� 麻豆原创 and Google Cloud solutions. These integrations will be supported by the 麻豆原创 Business Data Cloud Connect solution for Google and BigQuery, which enable bidirectional, zero-copy data access between the two platforms, with enterprise-grade security and governance. Capabilities across both Gemini Enterprise and agent gateway APIs from 麻豆原创 will allow customers鈥� agents to more securely exchange context, trigger actions and optimize outcomes across platforms, enabling true multi-agent orchestration.

The integration allows marketers to prompt an agent within 麻豆原创 Engagement Cloud with a clear objective like, 鈥淚ncrease repeat purchases from the last 30 days,鈥� or 鈥淢aximize customer lifetime value while reducing campaign operational costs.鈥� An agent, like a Joule Agent, will handle the end-to-end process鈥攆rom content personalization to visualization to conversational engagement.

鈥淭his is more than a data integration; it鈥檚 a leap forward for AI agents that can collaborate naturally and execute seamlessly,” said Balaji Balasubramanian, President and Chief Product Officer, 麻豆原创 Customer Experience and Consumer Industries. 鈥淏y combining 麻豆原创 Business Data Cloud Connect for Google with interoperable AI agents across 麻豆原创 and Google Cloud, we鈥檙e giving organizations a path from AI experimentation to AI-enabled customer experience at scale. Marketers can spend less time on manual tasks and more time shaping the customer journey.

鈥淭o realize the full potential of agentic AI, businesses need their systems to speak the same language,鈥� said Kevin Ichhpurani, President, Global Partner Ecosystem at Google Cloud. 鈥淏y uniting 麻豆原创鈥檚 enterprise data and customer engagement platform with Google Cloud鈥檚 AI, we鈥檙e enabling marketers to move beyond simple automation to multi-agent orchestration, driving dynamic campaigns that reason and adapt to market shifts in real time.鈥�

According to from 麻豆原创 Engagement Cloud, more than half of marketers say fragmented, outdated data prevents them from acting in the moment. 麻豆原创 and Google Cloud are helping remove that roadblock by unifying data and letting AI agents turn insights into action. Using Joule with 麻豆原创 Engagement Cloud, campaigns can move from planning to activation automatically without manual stitching across tools.

Customers will benefit from autonomous campaign generation, optimization and continuous improved performance. Businesses will achieve faster speed-to-market, lower operational overhead and always-on optimization that drives higher ROI, while giving teams more time to focus on strategy and end-to-end campaign execution.

While marketing is the first example, and will be available to customers in H2 2026, this multi-agent orchestration model is designed to support high-value use cases across the 麻豆原创 CX portfolio, laying the foundation for AI-driven customer experience, powered by trusted, unified real-time data and interoperable agents.

For more information about 麻豆原创 Customer Experience solutions, visit .

For more information about Gemini Enterprise, visit .

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About Google Cloud

Google Cloud offers a powerful, optimized AI stack 鈥� including AI infrastructure, leading models like Gemini, data management capabilities, multicloud security solutions, developer tools and platform, as well as agents and applications 鈥� that enables organizations to transform their business for the Agentic Era. Customers in more than 200 countries and territories turn to Google Cloud as their trusted technology partner.

About 麻豆原创

As鈥痑 global leader in enterprise applications and business AI, 麻豆原创 (NYSE:麻豆原创)鈥痵tands at the鈥痭exus鈥痮f business and technology. For over 50 years, organizations have trusted 麻豆原创鈥痶o bring out their best by uniting business-critical鈥痮perations spanning finance, procurement, HR, supply chain, and customer experience. For more information, visit鈥�.

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Instead, they immediately encounter friction. They鈥檙e asked to repeat information they鈥檝e already shared across multiple channels and departments. Then they see an offer for the item they just purchased, rather than something similar or new. And when they encounter an issue down the line, customer support doesn鈥檛 recognize their history.

Micro moments like these do not feel minor to customers anymore. They feel inexcusable. Customer expectations have changed faster than most brands can keep up. Customers now assume brands know who they are, what they need, and what鈥檚 happening right now. And they expect brands to act on that knowledge instantly.

At the same time, businesses are embracing a new era of AI. Dubbed “agentic AI,” it represents a paradigm shift where AI doesn鈥檛 just analyze or recommend products, but increasingly plans, decides, and acts through a network of agents. This creates a massive opportunity for customer experience (CX) leaders today, in particular marketers, who, according to McKinsey, are leading in AI adoption amongst business functions. But it also raises the stakes.

Because when AI moves faster than your data, systems, and processes, it exposes everything that鈥檚 broken. That tension鈥攂etween rising expectations and disconnected reality鈥攊s exactly what 麻豆原创 and Google Cloud are addressing together.

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The marketer鈥檚 reality: ambition outpacing execution

According to recent , more than half of marketers say fragmented or outdated data prevents them from acting in the moment. Insights arrive too late. Activation requires manual stitching across tools. And even the best strategies stall before they ever reach customers.

It is clear that most organizations genuinely want to deliver great customer experiences. But fragmentation is what stands in the way of delivering connected, meaningful engagements.

On one side: Customers expect effortless, relevant, and real-time experiences. On the other hand, organizations still operate with fragmented data, siloed teams, and delayed insights.

Our latest reveals that customers are increasingly frustrated: 45% say brands can鈥檛 keep up with changing expectations, and 44% say interactions feel less personal than before.鈥�

AI accelerating the engagement divide 

The disconnect between what customers feel and what businesses believe is the “.” Customer signals live across disconnected systems. Data arrives late or without context. Execution happens separately from insight. And while customers feel this friction immediately, many companies do not realize how disconnected their experiences truly are in their customers’ eyes. Now, AI is accelerating this divide.

Agents can generate content, launch campaigns, and optimize engagement at unprecedented speed. But when those agents act on incomplete, outdated, or fragmented data, they only exacerbate inconsistency and poor customer experiences.

When talking to our customers, it鈥檚 clear that there is no shortage of ambition when it comes to AI. In our research, 78% of brands say AI will be integral to their customer retention efforts this year. But only 46% of brands can connect their data in a way that is accessible to power AI sustainably.

The real challenge for CX leaders today is ensuring that AI has the right foundation: trusted data, unified context, and direct connection to execution.

New model for engagement built on trusted enterprise data

麻豆原创 and Google Cloud are expanding their partnership to enable a fundamentally different approach to marketing execution, one grounded in trusted enterprise data and real-time signals, accelerated with multi-agent coordination, and delivered at scale through 麻豆原创 and Google鈥檚 customer engagement solutions.

麻豆原创 provides both operational truth for elements such as inventory, orders, and fulfillment status, and deep customer knowledge across customer experience interactions. Google Cloud brings additional real-time signals and analytics, along with advanced AI. Combined, they create a shared, real-time understanding of the customer, grounded in business and situational context.

At the heart of this partnership:

• 麻豆原创 Business Data Cloud (麻豆原创 BDC) connects semantically rich data across the enterprise with AI to enable real-time insights and drive personalized interactions grounded in business context. This includes 麻豆原创 Business Data Cloud Connect for Google BigQuery.
• Google BigQuery unlocks real-time signals across the Google ecosystem, such as geolocation, weather, and rich analytics, through bidirectional, zero-copy data access with 麻豆原创 BDC, while ensuring enterprise-grade governance and security.
• 麻豆原创 Customer Experience applications provide the real-time behavioral context 鈥� customer profiles, transactions, orders, service interactions, and consented engagement data.
• 麻豆原创 Engagement Cloud activates enterprise data and AI insights and predictions to securely orchestrate real-time, personalized interactions across the entire customer life cycle.

With these innovations, marketers can finally move from insight to execution automatically.

To realize the full potential of agentic AI, businesses need their systems to speak the same language. By uniting 麻豆原创’s enterprise data and customer engagement platform with Google Cloud’s AI, we鈥檙e enabling marketers to move beyond simple automation to multi-agent orchestration, driving dynamic campaigns that reason and adapt to market shifts in real time.

Kevin Ichhpurani, President, Global Partner Ecosystem at Google Cloud

From prompt to performance: how agents work together for marketing

Another critical element of this new execution model is agent interoperability. Gemini Enterprise acts as a central hub for multi-agent coordination, enabling  customers鈥� agents to securely exchange context and take action across platforms. Meanwhile, Joule acts as the engagement layer within 麻豆原创 applications, executing tasks, orchestrating campaign and content workflows, and optimizing marketing outcomes. Working together, 麻豆原创 and Google are enabling true multi-agent orchestration connected to trusted enterprise data.

Within this broader CX transformation, 麻豆原创 Engagement Cloud is where agentic intelligence becomes operational for marketing teams. It is the environment where enterprise signals, generative media, and AI agents translate into real customer interactions and automated lifecycle journeys.

Advanced generative capabilities powered by Google Gemini models, for example, Nano Banana 2, introduce new agentic skills that help CX teams dynamically generate messaging, imagery, and campaign variations. Through assistants and agents in Joule, these capabilities become embedded directly into marketing workflows, allowing brands to adjust tone, localize content, and respond instantly to changing conditions.

It is not just content generation and personalization that are being rewired. With unified data context and interoperable agents, mobile messaging can turn into immersive conversational experiences with Google Rich Communication Services (RCS) and advertising audiences, and creative, which can continuously evolve based on real-time performance and business signals, transforming campaigns into intelligent, self-optimizing systems.

And through this multi-agent network, marketers will not need to build every step of a campaign manually. Instead, they define the goal, gain more time to focus on strategy and creativity, and let agents handle the rest.

For example, a marketer can prompt:

• 鈥淚ncrease repeat purchases from customers in the last 30 days.鈥�
• 鈥淢aximize customer lifetime value while reducing campaign operational costs.鈥�

And from there:

• Joule Agents coordinate content production, grounded in customer and enterprise data, understand business context, customer history, and constraints
• Google鈥檚 Gemini Models and agents generate creative variations, messaging, and channel-specific content
• Agents collaborate across 麻豆原创 and Google Cloud to personalize, activate, and continuously optimize campaigns in real time across engagement channels and media networks

This is more than a data integration. It鈥檚 a leap forward for AI agents that can collaborate naturally and execute seamlessly. By combining 麻豆原创 Business Data Cloud Connect for Google with interoperable AI agents across 麻豆原创 and Google, we鈥檙e giving organizations a path from AI experimentation to AI-empowered customer experience at scale. Marketers can spend less time on manual tasks and more time shaping the customer journey.

Balaji Balasubramanian, President and Chief Product Officer, 麻豆原创 Customer Experience and Consumer Industries

Clear business outcomes for marketing teams

By enabling a network of interoperable AI agents and grounding them in enterprise data and shared context across 麻豆原创 and Google, organizations can achieve measurable outcomes, including:

• Faster speed-to-market through autonomous campaign and content generation
• Lower operational overhead by eliminating manual execution steps
• Always鈥憃n optimization that continuously improves performance
• Higher ROI through relevant, timely, and consistent engagement at scale

Marketers can spend less time managing workflows and more time shaping strategy, creative direction, and customer value.

Beyond campaigns: continuous engagement at enterprise scale

While marketing is a natural starting point, this is just the beginning. Customer engagement does not live in one system or team. Engagement spans commerce, service, sales, supply chain, and operations. A brand promise made in a message must be fulfilled by inventory. A personalized offer depends on pricing, availability, and delivery. And a single customer service interaction can shape the future of customer loyalty and lifetime value.

This multi-agent model is designed to support high-value use cases across the 麻豆原创 Customer Experience portfolio, laying the foundation for an AI-driven customer experience powered by trusted, unified, real-timedata.

In an AI-driven world, customer experience goes beyond any single interaction鈥攊t’s defined by every touchpoint a customer has with your company.

The problem is not the promise of AI. It鈥檚 the gap between intelligence in the system and connection in the moment. Customers are increasingly disengaging because intelligence is not being applied where it matters most.

Technology, particularly AI, has fundamentally changed what customers expect. They assume brands can recognize them across channels, understand context in real time, and anticipate their needs. When that doesn鈥檛 happen, the miss feels less like oversight and more like indifference. Timing is off. Service lacks continuity, and personalization stops at the surface, despite all the data behind it.

While many enterprises are trapped in siloed systems and disconnected data, consumer expectations are growing. Brands that don鈥檛 deliver the expected experiences are quickly abandoned.

In addition, global socioeconomic factors are increasing rapidly and unpredictably, challenging bottom lines and making customer loyalty more critical than ever鈥攁t a time when consumers are less loyal than ever.听

When economies falter, companies usually take one of two approaches. Some hunker down, cut costs and staff, and hope to survive. Others zero-in on differentiators like to drive growth and boost profitability.

The importance of CX for key metrics like churn, retention, loyalty, new sales, and competitive differentiation is well-established, so not investing in customer experience could be considered akin to saying you are willing to let those mission-critical metrics falter.

The following 15 takeouts from 麻豆原创’s highlight some of the most common CX pitfalls and opportunities.

1. 82% of consumers say a brand has disappointed them

Modern customers do not go quietly into the bad experience night. A whopping 82% of consumers say a brand has disappointed them, even when the product itself meets their needs. The issue isn鈥檛 the product or service; it鈥檚 the experience of purchasing and post-purchase care.

This is the essence of the 鈥溾��: the distance between what customers expect in the moments that matter, and what brands are actually delivering.

2. 60% do not pay attention to brands anymore and 48% care more about experience

Consumer attention in a difficult economy has shifted from logos and taglines to experiences that feel useful, contextual, and personal. So, what鈥檚 a brand to do when 60% of consumers say they simply don鈥檛 pay attention to brands and 48% care more about the experience than the product?

This is where CX outcomes become clear: engagement is no longer about shouting louder; it鈥檚 about showing up better and building experiences powered by unified data and intelligent orchestration.

3. Left unread: only 16% of customers skim email headlines, while 29% read one or two sentences

Consumer behavior in the inbox shows just how fragile engagement is:

• Most consumers only read the subject line
• Others will read one to two sentences before deciding whether to delete or engage further

Combined with the fact that 58% of consumers think most marketing emails they receive aren鈥檛 relevant, brands are staring down a massive relevancy problem. Sending more emails into the engagement abyss doesn鈥檛 solve this problem, but gaining a holistic understanding of your customers as individuals does.

4. 37% do not think brands personalize to their needs

For well over a decade we鈥檝e been talking about the importance of personalization, but today 37% of consumers believe brands don鈥檛 personalize engagements to their needs. Surface-level personalization鈥攏ames in subject lines, basic segmentation鈥攊s no longer enough.

This aligns with our assessment that 79% of companies have low or moderate CEM scores, meaning teams can access portions of shared data and deliver basic personalization, but coordination across marketing, sales, service, commerce, and product teams remains limited. Experiences often feel disconnected, forcing brands to rely on short-term tactics rather than building deeper relationships.

Consumers expect real-time, behavior-driven personalization based on context, intent, and history, not just boiler-plate persona buckets. Customers can see and feel investments in personalization and it matters.

5. 46% say customer service feels too impersonal, while 41% believe brands do not understand them as a person

Considering how much data brands collect, it鈥檚 striking that nearly half of consumers (46%) say customer service feels too impersonal.

Customers are asking a simple, and valid, question: 鈥淚f you have all this information about me, why isn鈥檛 my experience better?鈥� When data doesn鈥檛 translate into empathy and action, it starts to feel like surveillance, not service.

With 46% of consumers saying service isn鈥檛 personal, it should be no surprise that a nearly equal amount (41%) believe that brands don鈥檛 understand them as a person. However, 34% agree that AI can help brands better understand them and what matters most to them.

This presents brands with a real-time opportunity: use AI and data to close the perception gap. Instead of just predicting purchases, enterprises should also be anticipating customer needs and reducing friction.

6. 78% of brands say they deliver seamless cross-channel engagement, consumers disagree

Seventy-eight percent of brands say their engagement strategies offer seamless multichannel experiences with glowing outcomes like increased CLV, retention, and advocacy, but consumers are simultaneously reporting little emotional connection and frequent disappointment. In fact, 44% say that brand interactions feel less personal and more generic than ever before.

The takeaway: internal dashboards can create a if not tied directly to real customer sentiment and behavioral signals across channels.

7. 54% of enterprises cannot access and use real-time data, and 66% still rely on third-party data

Fifty-four percent of enterprises can鈥檛 access and use real-time data. On top of that, 60% suffer from 鈥渄ark data,鈥� which is information that鈥檚 collected but not used throughout the customer journey.

Without real-time, connected data, brands are mostly flying blind. AI, personalization, and omnichannel orchestration don鈥檛 fail because the ideas or execution are wrong; they fail because the foundations are.

Although privacy regulations and legislation are increasing while third-party cookies decline, a majority (66%) of enterprises are still heavily reliant on third-party data. Simultaneously, 55% say their data is too unstructured to use effectively.

The lethal combination of overreliance on external data plus underutilized internal data keeps brands from building strong, first-party relationships rooted in trust and value.

8. 78% of brands say AI is essential for customer retention in 2026

AI is everywhere, and 78% of brands view AI as critical to retaining customers in 2026. However, 66% report they can鈥檛 use AI to optimize campaign performance in practice, while many also note they can鈥檛 utilize real鈥憈ime AI optimization in day鈥憈o鈥慸ay campaigns.

A quick translation of the above stats: an AI strategy is crucial, but execution is lagging because of fragmented systems, poor data quality, and integration issues.

9. Only 30% share engagement data with a CX or CRM platform

Despite the collective agreement that a comprehensive customer profile is important, only 30% of brands share their customer engagement data within a CX or CRM platform. This means that most brands are attempting to deliver personalized experiences without having a unified engagement core.

If engagement data lives in campaign tools, service systems, commerce platforms, and ERP, but never gets connected via CX or CRM, customers will feel every fracture along their journey.

10. 30% of consumers have used AI agents that act on their behalf

AI is not just an enterprise capability; it鈥檚 also a customer behavior. Thirty percent of consumers say they鈥檝e used AI agents to make decisions and act on their behalf when buying from brands.

This is a game-changer when it comes to engagement. Brands are now engaging not only with humans, but also with AI buyers that ruthlessly and continuously optimize for relevance and value. If your systems can鈥檛 keep pace, AI will select your competitor whose systems are operationalized for success.

11. When it comes to customer engagement maturity, 79% of brands have yet to integrate data, systems, and teams across their business; only two in five decision-makers see their departments as actually coordinated

The Customer Engagement Maturity (CEM) scoring model assesses how well brands align people, processes, and technology to deliver cohesive, intelligent experiences. Looking at the 麻豆原创 Engagement Maturity Index:

• 16% of brands reside at low maturity
• 63% sit in the moderate middle
• 21% have high maturity

Despite year-over-year progress, most organizations are stuck in developing or evolving mode, able to execute campaigns but not orchestrate truly connected, enterprise-wide engagement. And leaders agree, with only two in five decision makers believing there is effective collaboration across departments.

12. Just 21% of brands are high-maturity, and they are gaining ground against their competition

High-maturity brands rise above the competition because they connect data and intelligence across marketing, service, sales, commerce, and operations. They use AI and automation to deliver personalized, omnichannel engagement in real-time, at scale.

And the maturity gap is becoming a performance gap. As top performers turn real-time intelligence into growth, the cost of competing with them rises for everyone else.

13. Personalized means personal: 58% of consumers respond positively to localized content

Personalization is more than a word or industry term. It means actually understanding and empathizing with your customer, including their regional traditions and social norms.

When engagement is done right, consumers respond:

• 63% say their favorite brand delivers seamless, connected experiences across mobile, web, and in-store
• 58% value localized content and product recommendations
• 55% appreciate highly personalized content
• 50% believe their favorite brand uses data to make interactions better

Customers aren鈥檛 against data or AI at heart. However, they are opposed to wasted data collection and bad experiences. It鈥檚 the job of brands to provide a great CX. If that job isn鈥檛 taken seriously, you can bet that other brands are willing to roll up their sleeves to fill the gap.

14. 77% of businesses plan to invest in AI-powered engagement in 2026

When it comes to the future state, 77% of businesses plan to invest in AI-powered customer engagement in 2026, and 76% are investing in omnichannel engagement technologies. At the same time, 29% say their top priority is connecting customer and stakeholder data across marketing, sales, service, commerce, and ERP systems.

The signal is clear: investment alone won鈥檛 close the Engagement Divide. The winners will be the brands that invest in connection鈥攐f data, teams, and systems鈥攏ot just in tools.

15. 15% say seamless integration will be the biggest driver of success

Lastly, and possibly most importantly, 15% of businesses believe seamless integration of engagement systems will be the single biggest driver of success. While that may sound like a small number, it captures a critical strategic shift: engagement is no longer a marketing problem or a channel problem. It鈥檚 an enterprise discipline that depends on unified data, coordinated teams, and embedded AI.

Artificial intelligence provides an evolving service for businesses. Employing cloud-based systems that can store, analyze, and route data will be the differentiator for brands in the marketplace.

Loyalty is transactional, and driven by great CX and a connected enterprise

Digital engagement has raised the bar when it comes to customer expectations, with more demands and a plethora of competitive choices if a brand doesn鈥檛 deliver.

It鈥檚 not a big leap to state that better customer experiences increase customer loyalty, which in turn leads to more purchases, augmented product utilization, and increased brand affinity and sentiment. And let鈥檚 not forget that an enhanced CLV lowers customer acquisition costs.

After all, loyalty is transactional and forged by the experiences customers encounter. In my conversations with customers across the globe, it鈥檚 clear that only the brands with truly at the heart of their operations will retain and grow their customer bases in the enterprises of the future.

That ambition relies on a technology foundation that can consistently deliver those experiences at scale. For British-founded luxury fragrance brand Molton Brown, moving from legacy systems to 麻豆原创 Commerce Cloud provided a high鈥憄erformance platform built for peak鈥憇eason resilience and continuous innovation. The impact was immediate: 100% uptime during peak trading, even as volumes surged to one order every three seconds during major events.

This kind of reliability is increasingly critical as the moments that shape experience and loyalty expand beyond owned channels. As product discovery shifts to social platforms and AI鈥憄owered assistants, consistent content and availability help the brand remain visible and trusted wherever customers engage. 麻豆原创鈥檚 evolving agentic commerce innovations are designed for this reality, keeping products discoverable, credible, and actionable across both human and AI interactions.

Ultimately, technology and AI are not the goal鈥攖he experience is. The brands that succeed will be the ones that use AI to show up more human, not less, turning insight into relevance and automation into trust.

The future of CX is for companies that operationalize intelligence across the enterprise鈥攃onnecting data, systems, and teams so AI can orchestrate experiences, not just analyze them.

Manos Raptopoulos is global president of Customer Success Europe, APAC, Middle East & Africa, and a member of the Extended Board 麻豆原创 SE.

AI alone is no longer a differentiator: What matters is where intelligence鈥痮perates鈥痠nside of a business. As demand volatility increases, fulfillment windows tighten, and customer expectations鈥痳ise,鈥痮rganizations need more than insights or task鈥痑ssistance. They need intelligence inside quotes, product content, service interactions, and campaigns, guiding decisions as they happen and continuously adapting as conditions change.

This shift lays the foundation for a new generation of executional AI, where capabilities evolve from supporting users to actively鈥痬onitoring鈥痜lows,鈥痑nticipating鈥痳isk, and over time acting as intelligent agents within core customer-facing processes.

With the Q1 2026 release of鈥� solutions, 麻豆原创 advances this shift by bringing AI closer to day-to-day customer-facing execution across sales, service, commerce, and engagement. Intelligence now operates closer to where outcomes are realized鈥攈elping organizations protect revenue, reduce friction, and deliver consistent, trusted experiences at scale.

Below, explore more of the highlights from the Q1 2026 release. And for full sub-solution details, see recaps for聽,听,听,听, and聽.

Optimize revenue streams with confidence

Revenue becomes more reliable when customer intent is captured early and executed consistently across sales and commerce workflows. The execution depends on speed and accuracy: accurate product information, relevant content, and seamless handoffs from inquiry to quote creation. When these are disconnected, teams face delays, manual rework, and missed revenue opportunities.

From customer inquiry to executable quote

• Email to quote with AI:鈥疉utomatically add SKUs from a deal using opportunity and email data with the Microsoft Outlook add-in for 麻豆原创 Sales Cloud. Users can choose to generate a quote, and the quote is quickly created in 麻豆原创 Sales Cloud in just a few clicks. After review, sellers can hit send; it is that easy.  
• Deep research: Accelerate account planning and reviews by synthesizing 麻豆原创 Sales Cloud and 麻豆原创 Service Cloud data with external market intelligence. For example, the deep research capability can deliver a detailed brief that can be used to better understand the account, their industry, and other crucial information like news and SWOT. Sellers will be able to engage prospects and buyers more effectively while customers will have more relevant and personalized information delivered.  
• Media attachments for product descriptions: Use AI to extract details from product documents, such as manuals, spec sheets, and PDFs, and automatically generate or enrich product descriptions in 麻豆原创 Commerce Cloud. This accelerates catalog updates and improves product data quality so that shoppers, search engines, and agentic commerce are rich with the most accurate product descriptions鈥攅nsuring product descriptions are detailed, differentiated, and discovered.

Delivering鈥痳别濒颈补产濒别鈥痵ervice at鈥痵肠补濒别

• Digital Service Agent handoff鈥痜or case creation: Connect every step of the service journey from conversational AI self-service to field resolution so service teams can resolve customer issues鈥痜aster and provide personalized service engagements that build trust.鈥疷sing conversational cues, Digital Service Agent鈥痵ummarizes intent identification for ticket creation while capturing essential information required for handoff to underlying solutions like 麻豆原创 Service Cloud.
• : Give service teams a single, real-time command center in 麻豆原创 Service Cloud, consolidating cases, tasks, and service orders into one view with visual workload insights so agents can prioritize faster, stay on top of commitments, and resolve more issues per day. 
• Retail Intelligence (麻豆原创 Early Adopter Care): Announced at NRF, Retail Intelligence provides one closed-loop, AI-enhanced retail supply chain planning environment that ties together planning, execution, and engagement. The result: human and agentic teams that don鈥檛 just execute tasks but reshape strategies, reimagine retail supply chain planning, and master autonomous growth and lasting differentiation.
  Learn more at the session.

Orchestrating engagement across the customer life cycle

Customer engagement spans browsing,鈥痯urchasing, fulfillment, and service across multiple channels. 麻豆原创 CX connects engagement directly to operational context.鈥�&苍产蝉辫;

• delivers鈥痯ersonalized, AI-personalized communications and interactions across every channel powered by connected customer and operational data all fully integrated across 麻豆原创. Teams鈥痗an鈥痙eliver consistent, intelligent engagement that builds loyalty and drives鈥痓usiness鈥痠mpact.

• :鈥疎xtend conversational analytics to SMS campaigns. A new data context model narrows analysis to the right dataset, returning faster, more precise answers to natural language questions, such as 鈥淲hat was SMS revenue last month?鈥�

• :鈥疨redictively鈥痠dentify鈥痗ontacts鈥痺ho are鈥痩ikely in the next鈥�30 days to engage,鈥痓ecome inactive, or remain inactive,鈥痵o marketers can target outreach鈥痺here it will deliver the strongest results.鈥��&苍产蝉辫;

Accelerate transformation鈥痺颈迟丑鈥痶丑别鈥痑dvanced success plan鈥痜or 麻豆原创 CX

To鈥痑ssist鈥痗ustomers鈥痮n their鈥痶ransformation鈥痡ourneys, 麻豆原创 launched the new Advanced Success Plan in the鈥�. This will鈥痟elp customers increase the value of individual applications, accelerate cloud transformation across 麻豆原创 Business Suite,鈥痑nd enable consistent adoption of new innovations and 麻豆原创 Business AI.

With expanded coverage with additional 麻豆原创 CX solutions, including and , the advanced offering is comprised of three powerful elements:

• Success expert: Regular 麻豆原创 expertise driving strategic customer outcomes
• Adoption guidance: Structured, AI-driven enablement accelerating adoption
• Activation and optimization services: Hands-on services to maximize performance and impact

Check out the鈥痺ebinar鈥痶o learn how the new service offering unlocks more of the transformative value of 麻豆原创 solutions:鈥�.

Intelligence where execution happens鈥�&苍产蝉辫;

With 麻豆原创 Customer Experience, AI moves beyond isolated鈥痑ssistance鈥痶o鈥痮perate鈥痙irectly within business execution flows. Intelligence is embedded where work happens鈥攊nside quotes, product content, service interactions, and campaigns鈥攈elping organizations respond in real time and deliver consistent customer outcomes at scale.

Learn more about 麻豆原创 CX in Q1鈥�2026鈥�&苍产蝉辫;

Read the 麻豆原创 Help documentation to get started with these new capabilities.鈥�&苍产蝉辫;

Balaji Balasubramanian is president and chief product officer for 麻豆原创 Customer Experience and Consumer Industries.鈥�

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, our new user experience, is gaining momentum and driving significant impact for our customers. Customers are already efficiency, enhancing processes, improving , and .

Joule is now live across 35 solutions and will continue to meet our customers where they are: across the applications they use, with a firm understanding of their business context and data. That鈥檚 why in Q1 we are embedding Joule into more applications鈥攆rom 麻豆原创 Datasphere, where it can now execute tasks or explain specific functionalities, to 麻豆原创 Intelligent Clinical Supply Management, where users can use natural language to retrieve critical data and navigate to relevant applications.

Joule Agents, such as the Tender Analysis Agent, are boosting customer revenue growth by extracting critical requirements and flagging risks in complex documents. While project managers in 麻豆原创 S/4HANA Cloud Public Edition are saving time setting up projects with the new Project Setup Agent. Plus, there are many more agents to discover below.

Agents are becoming a key new user鈥攁nd enabler鈥攐f enterprise software, joining humans as the only other non-deterministic operators while simultaneously expanding enterprise software鈥檚 scope and usefulness. Our agents will continue to deliver trustworthy, repeatable, and auditable results every time.

We now have over 30 specialized agents and more than 2,500 Joule Skills. The agent-to-agent protocol means our agents work across 麻豆原创 and non-麻豆原创 systems. As the number of agents grows across both, 麻豆原创 AI Agent Hub already today provides customers with the essential infrastructure and guardrails to manage, govern, and discover agents in this new ecosystem.

Some highlights from Q1 2026:

• 麻豆原创 Joule for Consultants is a conversational AI solution that provides expert guidance on cloud transformations, drawing on 麻豆原创鈥檚 knowledge base. To improve trust and traceability, citations are now displayed in a dedicated side panel and can be grouped for clarity. Administrators can enable web search, allowing Joule to draw from public content while maintaining clear source attribution. For tailored answers to problems where the system may not have customer-specific documentation, consultants can now upload up to 10 PDF or text files directly into the chat. This is further enhanced by the inclusion of content from the 麻豆原创 Enterprise Architecture Reference Library, which provides more complete and accurate answers to complex queries. Get started here.
• 麻豆原创 Business AI for supply chain minimizes disruptions and simplifies planning. The Project Setup Agent allows project managers to rapidly establish new projects by drawing on data from past initiatives. 麻豆原创 Integrated Business Planning users can now generate complex formulas in Microsoft Excel with natural language. 麻豆原创 Digital Manufacturing can distill complex manufacturing issues into clear descriptions. Joule is also helping 麻豆原创 Integrated Product Development users create problem reports and requirement models with simple, natural-language commands. Explore more below.
• 麻豆原创 Business AI for finance offers greater efficiency and insight across critical processes. Joule now translates complex e-invoicing errors into plain language. The Dispute Resolution Agent automates root-cause analysis for invoice disputes, while payment advice processing significantly reduces document processing time. Unstructured data, such as PDFs, can now be automatically transformed into sales orders, and accountants can access natural language explanations for complex fixed asset calculations. Users can personalize their home page and easily understand system errors using natural language across 麻豆原创 S/4HANA Cloud Public Edition. Learn more below.
• 麻豆原创 Business AI for procurement and customer experience enhances the entire commercial journey with new capabilities. In procurement, automated statement of work (SOW) creation in 麻豆原创 Fieldglass reduces the time to define deliverables. The Catalog Optimization Agent means e-commerce managers can continuously improve product data quality. In retail, managers can get instant, conversational answers from Joule on order management data. There’s so much more to learn below.
• 麻豆原创 Business AI for IT and developers puts the latest tools and greater control directly into the hands of developers and data professionals. Joule is now generally available in 麻豆原创 Datasphere, enabling users to navigate the platform, get answers, and execute tasks using simple conversational language. The generative AI hub in AI Foundation continues to expand, offering developers access to the newest models, including OpenAI GPT 5.2, Gemini 3.0 Pro, Anthropic Claude Opus 4.6, and Claude Sonnet 4.6. Developers also gain greater power through enhancements such as advanced prompt optimization, metadata filtering, and declarative orchestration configurations in the prompt registry. Additionally, 麻豆原创 Document AI now offers more granular control with custom confidence thresholds and expanded document support. Dive into everything below.
• 麻豆原创 Business AI for industries delivers specialized intelligence to solve unique business challenges. Sales teams can accelerate their response process with the new Tender Analysis Agent, which automates the review of complex RFQ documents to improve win rates. Joule now works with 麻豆原创 Commodity Management to turn verbal or written negotiations directly into detailed draft deals. In life sciences, clinical supply professionals can use predictive analytics to reduce inventory waste costs, and Joule dramatically cuts information search time. 麻豆原创 Self-Billing Cockpit automates invoice data extraction from any format, significantly reducing manual processing time. Discover more for industries below.
• 麻豆原创 Business AI for business transformation management provides the critical insights needed to navigate and accelerate organizational change. Joule is now in 麻豆原创 Signavio, enabling natural-language searches that cut information discovery time. Business process model and notation simulations in 麻豆原创 Signavio provide clear, actionable summaries directly within process diagrams. Meanwhile, enterprise architects can leverage guidance in 麻豆原创 LeanIX to surface actionable insights directly from their architecture inventory, accelerating transformation execution and reducing the time to uncover them. Read more about transformation management below.

Joule

Joule, enhancements

User experience is improved by streamlining startup times and introducing cross-thread search functionality that lets end users find information across all conversation threads without manually checking individual histories. The document grounding capability has also seen a substantial upgrade, now supporting seamless integration with Google Drive.

To set up, see: , , and .

Furthermore, scalability has been greatly improved, as the system now supports up to 8,000 documents per pipeline, enabling large-scale data repositories to be processed and utilized efficiently.

For more information, see .

麻豆原创 Joule for Consultants, enhancements

Enhanced Citation Visibility
麻豆原创 Joule for Consultants has improved how citations are displayed for all identified sources returned by the product. Citations have been relocated to the right side in a dedicated panel for clearer visibility, and now also include public web search results when applicable (see below).

A new grouping feature has also been added, allowing citations to be grouped. This update provides users with a more transparent view of where information originates, strengthens trust, and improves traceability across all responses.

To see the sources and panel, click the sources button below each message; the panel will open on the right, showing all grouped sources.

Enable Web Search
Administrators can now enable/disable web search via the control panel for all assigned end users in 麻豆原创 Joule for Consultants.

When enabled, 麻豆原创 Joule for Consultants will consider public web content in its reasoning and cite relevant public sources in responses when they contribute to the answer. This enhancement gives organizations greater flexibility and transparency by enabling broader coverage of information while maintaining clear source citations for all sources used.

File Uploads in the Joule Message Input
End-users can now upload up to 10 files directly from the conversational message input box and reference them throughout the entire conversation.

Supported file types include PDF and TXT. Each file should be no more than 10 MB/600K characters; for PDFs, an approximation. A 100-page limit applies; if your file is larger, split it into multiple documents. Image files are currently not processed and will be ignored. We are working diligently to make this feature even more useful to end users. This enhancement enables richer, context-aware interactions by allowing you to incorporate your uploaded documents into its conversational responses throughout the session. Please be aware that the standard data privacy terms apply. See also the help documentation for additional information on the free user quota.

Content: 麻豆原创 Enterprise Architecture Reference Library
麻豆原创 Enterprise Architecture Reference Library data has been ingested and is now available for use in conversations. As more data is added, relevant portions may be included in 麻豆原创 Joule for Consultants鈥� responses, enabling more complete, accurate, and context-rich answers to user queries. Since 麻豆原创 Enterprise Architecture Reference Library content cannot be link-referenced, you won鈥檛 see the additional content listed under sources, even though it will be referenced.

Get started .

SECTION

麻豆原创 Business AI for supply chain

Project Setup Agent
Beta release

Project managers can now rapidly establish new projects by drawing on data from similar past initiatives. The agent bypasses complex interfaces and reduces reliance on the project management office (PMO) to facilitate the swift allocation of key resources needed to launch projects effectively. With a 10% reduction in project creation time, 16% faster resource allocation, and 30% less time spent reworking projects due to incorrect templates, teams can shift focus from operational coordination to improving project profitability and driving efficiency.

Get started .

麻豆原创 S/4HANA Cloud Private Edition, AI-assisted retrieval of equipment information in service management
General availability

Service managers using the AI-assisted retrieval feature in 麻豆原创 S/4HANA Cloud Private Edition gain a complete 360-degree view of customer equipment. The feature provides instant access to warranty information and a full history of service transactions, complemented by an AI summary and actionable recommendations. This allows service managers to more efficiently oversee service schedules, reduce potential downtime, and ensure customer equipment operates at peak performance.

Get started .

麻豆原创 S/4HANA Cloud Public Edition, AI-assisted input recommendations for returns order creation
General availability

Returns clerks can accelerate the creation of customer returns with data field recommendations powered by historical data. This feature analyzes past return documents with similar process variants to automatically suggest the most common input values and return reasons, minimizing manual data entry and reducing errors. Organizations benefit from a one percent reduction in data management costs and a five percent decrease in business and operations analysis expenses, enabling returns teams to process orders more efficiently while maintaining accuracy.

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麻豆原创 Integrated Business Planning, AI-assisted MRO inventory analysis
General availability

Inventory planners get a new analytical assistant in the MRO inventory analysis feature for 麻豆原创 Integrated Business Planning. The feature accelerates root cause analysis by generating clear, natural-language summaries that explain the key drivers behind recommended safety stock and reorder points. By translating complex calculations into understandable insights, this capability enables planners to reduce time spent analyzing inventory runs by 30%, leading to faster adoption of outputs and ensuring that inventory parameters align with strategic business goals.

Get started .

麻豆原创 Integrated Business Planning, add-in for Microsoft Excel, AI-assisted planning
General availability

Supply chain planners can now simplify their work with a new AI-assisted planning add-in for Microsoft Excel. Instead of manually creating complex formulas or formatting rules, which often require technical expertise, they can simply describe their needs in natural language, and the system automatically generates the correct syntax. This intuitive way of interacting with the system removes technical barriers and improves a planner鈥檚 efficiency by 10%, freeing them to focus on strategic analysis rather than implementation details.

Get started .

麻豆原创 Integrated Business Planning, AI-assisted system security check
General availability

Supply chain planners and security analysts gain a robust way to assess system configurations against established security recommendations. The feature evaluates compliance states and provides clear guidance on required adjustments, helping administrators identify and address potential gaps while aligning configurations with 麻豆原创 best practices. Organizations can expect a 27% increase in compliance with hardening guidelines and a 32% reduction in the effort required to meet security recommendations. This feature strengthens the protection of sensitive data and reduces the risk of security breaches.

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麻豆原创 Integrated Product Development, AI-assisted problem report creation
General availability

Maintenance engineers can simplify the creation of formal problem reports by leveraging AI capabilities in 麻豆原创 Integrated Product Development. By describing an issue in their own words to Joule, it intelligently extracts key details like the problem name, tags, and priority, and then automatically generates a structured report. This streamlined process dramatically reduces manual data entry and ensures all reports are consistent and compliant with organizational standards, improving overall efficiency.

and get started .

麻豆原创 Integrated Product Development, AI-assisted requirements model creation
General availability

Requirements managers now have a more direct path to creating requirement models within 麻豆原创 Integrated Product Development by using natural language commands with Joule. This feature allows them to initiate new models, specify names, and apply templates in a single step, completely bypassing the need to navigate through complex folder structures. This streamlined approach provides a much faster starting point for new projects and empowers users to begin their work immediately without requiring deep knowledge of the repository layout.

Get started .

麻豆原创 Field Service Management, AI-assisted automated scheduling analytics
General availability

Field service dispatchers and consultants can now access clear, on-demand explanations of auto-scheduling results that demystify complex system logic. The new feature interprets scheduling reports and translates technical scoring details into business-friendly insights, explaining why specific technicians were assigned, why alternatives were passed over, and why certain activities remained unscheduled. This transparency drives a 12.5% increase in dispatcher productivity and a five percent reduction in erroneous resource allocations, strengthening trust in automated decisions while significantly reducing analysis time.

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麻豆原创 Digital Manufacturing, AI-assisted description enhancement
General availability

Quality managers documenting complex manufacturing issues can now generate clear, objective, and structured descriptions with minimal effort. 麻豆原创 Digital Manufacturing for issue resolution offers description generation that refines rough initial inputs, removes bias and subjective language, and produces balanced, factual problem statements. With support for multilingual translation and enhanced clarity, organizations can achieve up to five percent improvement in quality engineer efficiency during issue handling and up to 10% reduction in errors throughout the problem resolution process.

Get started .

麻豆原创 Business AI for finance

Dispute Resolution Agent (for 麻豆原创 S/4HANA Cloud Public Edition)
Beta release

When invoice disputes arise, accounts receivable specialists need to act quickly without sacrificing accuracy. 麻豆原创 S/4HANA Cloud Public Edition introduces an agent that automates root-cause analysis, scanning invoices, sales orders, delivery records, pricing agreements, and tax rules to identify the source of discrepancies. The agent detects incorrect charges and recommends compliant solutions, such as credit memo creation, enabling finance teams to resolve disputes faster, minimize manual investigation, and cultivate stronger vendor relationships through transparent, efficient processes.

Get started .

麻豆原创 S/4HANA Cloud Public Edition, AI-assisted smart personalization of my home for applications
General availability

麻豆原创 S/4HANA Cloud Public Edition users can easily configure their home page with the most relevant applications through AI-assisted smart personalization. By describing their task in natural language, the system identifies the appropriate app, which can then be added to their home screen with a single click. This intuitive capability reduces the cost of personalizing the home page by 33%, shortens the learning curve for new users, and improves satisfaction by keeping frequently needed tools readily accessible.

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麻豆原创 S/4HANA Cloud Public Edition, AI-assisted error explanation
General availability

When encountering system errors, 麻豆原创 S/4HANA Cloud Public Edition users can turn to a new feature that generates clear, natural language explanations and resolution recommendations. This capability transforms cryptic error messages into easy-to-understand guidance, helping users of all experience levels quickly rectify issues and continue with their work. By reducing error resolution time by five percent, organizations benefit from increased productivity, improved data quality, and shorter training cycles for new team members.

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麻豆原创 S/4HANA Cloud Public Edition, AI-assisted sales order creation from unstructured data
General availability

Sales representatives benefit from a streamlined order creation process in 麻豆原创 S/4HANA Cloud Public Edition that handles unstructured data like PDF or image-based purchase orders. After uploading a file, 麻豆原创 Document AI automatically extracts the relevant information and proposes the data for a corresponding sales order request. This automation significantly reduces manual data entry, minimizes errors, and improves overall operational efficiency, allowing teams to process orders faster and enhance customer satisfaction.

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麻豆原创 S/4HANA Cloud Public Edition, AI-assisted processing of payment advices with 麻豆原创 Document AI
General availability

Accounts receivable clerks can accelerate their workflow using the 麻豆原创 Document AI-powered payment advice processing feature in 麻豆原创 S/4HANA Cloud Public Edition. The system automatically extracts payment amounts, references, and currencies from diverse invoice formats across multiple languages, with a self-learning capability that continuously improves recognition accuracy. Organizations implementing this feature can reduce document processing time by 70%, cut template maintenance time by 83%, and decrease value loss from manual processing delays by 40%.

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麻豆原创 S/4HANA Cloud Private Edition, AI-assisted fixed asset key figures explanation
General availability

Asset accountants gain clarity on complex fixed asset calculations through a new AI feature in 麻豆原创 S/4HANA Cloud Private Edition. The feature generates natural-language explanations that detail the origins of displayed values and how figures such as depreciation are calculated; for example, illustrating the impact of mid-year acquisitions with specific depreciation keys. This transparency reduces the effort required to analyze asset values, enables faster responses to asset-related questions, and helps mitigate compliance risks.

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麻豆原创 S/4HANA Cloud Private Edition, AI-assisted settlement rule proposal for asset capitalization
General availability

Overhead and asset accountants can now streamline the complex process of creating settlement rules for investment measures, eliminating the traditionally time-consuming, error-prone manual configuration. The solution automatically determines receivers, calculates percentages, and proposes feasible rules based on contextual data and user-defined instruction profiles. Organizations reduce the effort required to create full settlement rules by 50% while simultaneously improving accuracy in asset capitalization and enhancing overall operational efficiency across their financial processes.

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麻豆原创 Document and Reporting Compliance for 麻豆原创 S/4HANA Cloud Private Edition, AI-assisted electronic document error handling
General availability

Tax accountants navigating the growing complexity of e-invoicing mandates across multiple countries gain an easy way to decode technical errors without wading through intricate XML or JSON formats. Joule, integrated with 麻豆原创 Document and Reporting Compliance, delivers plain-language explanations of electronic document errors, enabling faster root-cause identification and more efficient resolution. Organizations get an 80% reduction in time spent understanding and resolving errors, dropping from 150 minutes to approximately 30 minutes. This results in faster processing cycles, reduced penalty risks, and improved cash flow.

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麻豆原创 S/4HANA Cloud Public Edition, AI-assisted error resolution for cost accounting
General availability

Operations managers in retail organizations can now access Joule via 麻豆原创 Order Management Services, enabling them to query order data and receive real-time, role-specific operational guidance across order processing, orchestration, sourcing, availability, returns, and fulfillment flows. Joule surfaces instant insights and recommended actions directly in the workflow, reducing the need to navigate multiple systems. This enables proactive intervention before issues escalate. The feature offers faster transaction access, improved responsiveness and accuracy, and lower operational risk, which support smarter, quicker decisions across the order lifecycle.

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麻豆原创 Business AI for spend management

Expense Report Validation Agent
General availability

Business travelers can enjoy a smarter, guided approach to expense report completion with an agent that proactively identifies missing items, prompts for necessary details, and clarifies confusing alerts throughout the submission process. By simplifying how users understand and resolve issues, the agent ensures accurate, policy-compliant reports with minimal effort required. This means a 30% reduction in time spent preparing and submitting reports, a 24% increase in first-pass approvals, and a noticeably improved employee experience that removes friction from the expense management process.

Get started .

Expense Pre-Submit Audit Agent
麻豆原创 Early Adopter Care

Expense report submitters can now catch receipt accuracy issues and policy breaches before hitting the submit button, avoiding the frustration of rejected reports and delayed reimbursements. This agent automatically reviews expenses during creation, surfacing compliance problems and offering smart suggestions for quick corrections. The agent uses a non-blocking design that keeps users in control of final decisions. Organizations benefit from a 10% decrease in sent-back expense reports, reduced rework for travelers, managers, and auditors alike, and a noticeably smoother reimbursement process that enhances the overall employee experience.

Get started .

Expense Automation Agent
麻豆原创 Early Adopter Care

Employees burdened by the administrative chore of creating expense reports can now delegate the heavy lifting to a Joule Agent. This agent automatically builds expense reports by aggregating transactions, populating custom fields based on contextual details and user history, and preparing everything for a quick review before submission. The outcome is up to 30%鈥� reduction in time on task for auto-generated expense reports. This offers a modern expense management experience that slashes manual data entry, accelerates the submission process, and frees employees to focus on high-value work rather than paperwork.

See the demo .

Concur Expense, AI-assisted configuration for audit rules
General availability

Expense administrators responsible for managing complex audit rule setups can now interact with their configuration environment in plain language, eliminating the need for deep technical expertise or tedious manual adjustments. This AI-assisted feature enables admins to search existing rules, create new ones, and receive real-time explanations simply by asking questions like “What rules apply to meals in France?”, delivering clear, actionable guidance instantly. The outcome is a 40% reduction in audit rule configuration effort, fewer support tickets, and empowered administrators who work with greater independence, accuracy, and confidence in maintaining compliance logic.

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Policy Navigator
麻豆原创 Early Adopter Care

Business travelers seeking quick answers to company travel and expense policies no longer need to sift through lengthy documents or wait for admin responses. Policy navigator in Joule allows employees to ask questions in natural language and receive clear, contextual guidance grounded in approved policies, whether planning a trip, in the middle of a journey, or completing an expense report. The result is in-the-moment policy clarity that prevents non-compliant spend before it happens, reduces support tickets, and empowers travelers to make confident, compliant decisions without disrupting their workflow.

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麻豆原创 Business AI for procurement

麻豆原创 Fieldglass Services Procurement, AI-assisted SOW deliverables creation
General availability

Procurement specialists can accelerate the development of their statements of work using the deliverables feature in 麻豆原创 Fieldglass Services Procurement. The feature analyzes the defined project scope and automatically generates precise, relevant deliverables that ensure tight alignment between buyer expectations and supplier commitments. By adopting this capability, organizations can reduce the time required to manually create SOW deliverables by 70% and cut the risk of poor outcomes by 50%, while fostering stronger collaboration during the negotiation process.

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麻豆原创 Business AI for customer experience

Catalog Optimization Agent
General availability

E-commerce product managers tasked with maintaining large 麻豆原创 Commerce Cloud catalogs gain an always-on agent that continuously reviews product descriptions, attributes, and translations against company quality standards. This agent pinpoints merchandising gaps and delivers actionable recommendations to enhance catalog accuracy, ensure consistency across languages, and improve product discoverability. The business impact is a 70% reduction in time to translate catalog data, 65% less time spent adding descriptions per asset, and a five percent reduction in data quality costs, all of which contribute to higher conversion rates and a more agile merchandising operation.

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麻豆原创 Revenue Growth Management, AI-assisted trade promotion creation
General availability

Key account managers in consumer industries can benefit from a streamlined, single-view promotion-creation experience in which simply naming a promotion automatically populates key fields. Drawing on master data, historical promotions, and learned preferences specific to each retailer, the system suggests dates, types, durations, and sell-in periods, then continuously refines its recommendations based on user edits over time. The impact is a 75% reduction in promotion setup time, 30% fewer data-entry errors and rework, and increasingly personalized suggestions that eliminate repetitive manual effort across promotion cycles.

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麻豆原创 Business AI for IT and developers

Joule Studio code editor and Joule Studio CLI

Building on the transformative capabilities of Joule studio low-code, 麻豆原创 is expanding the Joule studio family with two powerful new offerings designed to meet developers exactly where they work: Joule Studio code editor, a Visual Studio Code IDE extension, and Joule Studio CLI, a versatile command-line interface. Together, these tools deliver a unified, AI-assisted development experience that spans the full spectrum of development personas and preferences on Joule.

• Joule Studio code editor brings the intelligence of Joule directly into Visual Studio Code, the world’s most popular development environment, empowering pro-code developers with AI-guided scaffolding, contextual code generation, intelligent recommendations, and seamless integration with Joule, all without leaving their preferred IDE. 
• Joule Studio CLI extends this same power to the terminal, enabling developers and DevOps teams to automate project creation, manage configurations, execute deployments, and orchestrate CI/CD workflows through scriptable, command-line commands鈥攊deal for headless environments, automation pipelines, and teams that value speed and precision at the command line.

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Joule with 麻豆原创 Datasphere
General availability

Data professionals working within 麻豆原创 Datasphere can now accomplish informational, navigational, and transactional tasks through natural conversation with Joule. Whether asking how to use specific functionalities, retrieving details about a 麻豆原创 Datasphere instance, or switching system settings like language preferences, users receive instant answers with direct references to product documentation. Joule can even execute tasks directly from the conversation without requiring interaction with the standard interface. This direct execution reduces reliance on internal IT support and enables faster, more intuitive navigation throughout the platform.

Get started .

麻豆原创 Document AI, enhancements

Document level confidence
Customers now set confidence ranges for fields in the Schemas feature. When customers edit field settings, they can define their own thresholds for low, medium, and high confidence. These custom settings are reflected in the extraction results displayed for the relevant fields on the document details screen. See and .

Expanded Transportation Management
Customers can now use the Transports feature to export and import channels and workflows. See .

New schemas: business partner + delivery note for WM
The service plans embedded edition and premium edition now also support the standard document type, business partner document. See the list of supported document types in . Get started with 麻豆原创 Document AI, and .

Generative AI Hub in AI Foundation, enhancements

Metadata
Customers can now manage metadata for documents, collections, and chunks created with the Vector API to enable advanced filtering and organization of their content. For more information, see .

Retrieval API
Customers can merge and rank search results across multiple data repositories using the Retrieval API’s post-processing capabilities. For more information, see .

Prompt optimizations
Custom metrics are supported in prompt optimizations, enabling customers to define and optimize prompts based on their specific evaluation criteria. Only LLM-as-a-judge metrics with numerical or Boolean output types can be used in optimization tasks.For more information, see and . Customers can provide separate test and train datasets for prompt optimization. For more information, see .

Prompt registry
The prompt registry now enables customers to create and manage orchestration configurations declaratively, allowing them to version and track complex AI workflows alongside their prompts for better governance and reproducibility.For more information, see .

Secrets
Customers can now enter generic secrets using a form instead of JSON. The form appears in the Add Generic Secret dialog when you activate document grounding. A dropdown menu lets them choose the type of document repository. Depending on their selection, the remaining fields adjust dynamically, allowing them to complete the data. Some fields are already prefilled.If they prefer working directly with JSON, switch to the code view by clicking the 顒� icon. For more information, see .

New models available
New models are supported, including OpenAI GPT 5.2, Gemini 3.0 Pro, Perplexity Deep Research, and Anthropic Claude Opus 4.6.For more information on new and deprecated models, .

麻豆原创 Joule for Developers, ABAP AI capabilities, enhancements

New ABAP AI capabilities mean developers can expect a 20% reduction in time and effort to write ABAP/JAVA code, 25% reduction in time and effort to test ABAP/JAVA code, and 4.4% faster time to realized value.

This quarter, developers can now easily generate ABAP Unit tests for:

• Public, protected, and private methods of global ABAP classes
• Public methods of local classes within global class pools

See .

In addition, the documentation chat allows developers to interact with documentation on the 麻豆原创 Help Portal, providing context-aware answers and links to relevant documentation. This capability enhances productivity by offering quick access to related documentation directly within the development environment. See .

Finally, developers can now get AI-powered explanations of their ATC findings and code in the Custom Code Analysis/Custom Code Migration app. See and .

Get started .

麻豆原创 Business AI for industries

Tender Analysis Agent
General availability

Sales teams can elevate their tender response process with the Tender Analysis Agent, which automates the review of complex RFQ documents. The agent extracts critical product requirements, flags potential risks and policy gaps, and suggests optimized configurations tailored to customer needs. By reducing the effort to process incoming tenders by five percent and improving win rates, organizations can achieve measurable revenue growth while accelerating sales cycles and uncovering valuable cross-sell and up-sell opportunities.

Get started .

麻豆原创 Commodity Management, AI-assisted commodity work center
General availability

Commodity traders can transform how they capture and manage complex deals using the commodity work center in 麻豆原创 Commodity Management. Working alongside Joule, the feature converts verbal or written negotiations into detailed draft deals, automatically populating the numerous fields that traditionally require extensive manual entry. This enables traders to redirect their focus toward negotiating better commercial outcomes, while improving data accuracy and driving greater operational efficiency across their trading activities.

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麻豆原创 Intelligent Clinical Supply Management, AI-assisted predictive subject dynamics
General availability

Clinical trial coordinators seeking to boost their supply planning capabilities will find a powerful ally in 麻豆原创 Intelligent Clinical Supply Management. The predictive subject dynamics feature analyzes historical and real-time data to forecast patient enrollment trends and dropout rates, automatically generating insights that would otherwise require extensive manual analysis. This enables supply chain teams to redirect their focus to strategic decision-making, while reducing clinical inventory waste costs by up to two percent and improving demand forecasting accuracy across their trial operations.

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Joule with 麻豆原创 Intelligent Clinical Supply Management
General availability

Clinical supply professionals juggling multiple tasks and complex systems need quick access to information without disrupting their workflow. Together with Joule, 麻豆原创 Intelligent Clinical Supply Management delivers an intuitive, conversational interface that understands natural-language requests, enabling users to retrieve critical data and navigate to relevant applications effortlessly. This streamlined experience results in an 83% reduction in time spent on information searches, freeing teams to concentrate on higher-value activities and significantly boosting overall productivity.

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麻豆原创 Self-Billing Cockpit, AI-assisted document processing
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Billing clerks managing self-billing workflows frequently encounter invoices arriving in a mix of formats鈥擡xcel, PDF, CSV, or text files鈥攐ften unstructured and spanning multiple languages. 麻豆原创 Self-Billing Cockpit addresses this challenge by leveraging intelligent document processing to parse and extract invoice data from virtually any format, converting it into structured payloads ready for automated billing. The result is significantly reduced time spent processing invoice line items, fewer customer-specific interfaces for integration specialists to build and maintain, and improved extraction accuracy through minimized manual intervention.

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麻豆原创 Business AI for business transformation management

Joule with 麻豆原创 Signavio solutions
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Process analysts and optimization specialists working across complex organizational workflows require rapid access to diagrams, documentation, and performance metrics. 麻豆原创 Signavio solutions integrate with Joule to enable natural-language keyword searches across process diagrams, dictionary items, and help resources. At the same time, best-practice KPI recommenders guide users to the most relevant success measures. This intuitive approach delivers 50% faster information searches and navigation, ensuring teams make data-driven decisions with improved search quality and an enhanced overall user experience.

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麻豆原创 Signavio solutions, AI-assisted business process model and notation simulation insights
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Process analysts leveraging 麻豆原创 Signavio can now access embedded business process model and notation simulations directly within their process diagrams, eliminating the need for fragmented tools and manual interpretation. Key metrics such as costs, cycle times, and resource utilization are automatically translated into clear, actionable summaries that highlight bottlenecks and opportunities for improvement. This streamlined approach reduces time to access process modeling insights by 50%, empowering teams to compare scenarios effortlessly and communicate findings to stakeholders with greater confidence and clarity.

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麻豆原创 LeanIX solutions, AI-assisted architecture guidance
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Enterprise architects seeking to accelerate transformation initiatives can leverage 麻豆原创 LeanIX to surface actionable insights directly from their architecture inventory. The feature analyzes enterprise architecture data to identify opportunities and guides users through the workflows and tasks needed to efficiently act on recommendations. Organizations benefit from a 95% reduction in time to discover insights, 80% faster transformation execution, and a five percent reduction in value erosion from delayed action. Overall, this feature drives greater architectural productivity and more agile decision-making.

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Jonathan von Rueden is chief AI officer of 麻豆原创 SE.

*Disclaimer: This article provides estimated benefits. All calculations are estimates based on 麻豆原创 customer case studies, 麻豆原创 benchmarks, and other research. Actual benefits may vary and may be affected by additional factors not considered by this article. The information is provided 鈥渁s is鈥� without warranty of any kind, expressor implied, and in no event shall 麻豆原创 be liable for any damages whatsoever in relation with the use of this article. See Legal Notice on for use terms, disclaimers, disclosures, or restrictions related to this material.

These critical pre-purchase moments generate interest and open pathways toward deeper customer relationships. Organizations that convert interest into measurable outcomes with clarity, accuracy, and speed strengthen the overall customer experience, thereby boosting loyalty and bottom lines.

CX becomes even more meaningful as opportunities progress into clear agreements supported by accurate configuration, pricing, and quoting. This transition from opportunity to agreement represents one of the most consequential stages in the customer journey.

Lead-to-cash represents a coordinated motion across sales engagement, pricing precision, service alignment, and performance visibility. When these capabilities operate together, organizations deliver consistent customer experiences while maintaining operational clarity.

Eight years running: a leadership signal at the heart of lead-to-cash

Within the lead-to-cash journey, quoting connects sales engagement, performance management, service continuity, and ERP alignment. It represents a critical moment where customer intent is translated into accurate pricing, configuration, and agreement terms.

When 麻豆原创 CPQ operates within 麻豆原创 Customer Experience, it becomes part of a connected lead-to-cash motion that spans 麻豆原创 Sales Cloud, 麻豆原创 Service Cloud, sales performance management solutions, and 麻豆原创 ERP. Sales teams engage with structured opportunity data and guided pricing logic. Service teams inherit full visibility into agreed terms. Performance leaders access insights grounded in accurate pipeline and quoting data.

When it comes to this level of intelligent, real-time, connected processes, very few companies can compete. 麻豆原创 was again recognized as a Leader in the 2025 Gartner庐 Magic Quadrant™ for Configure, Price, and Quote Application Suites. This marks the eighth consecutive year 麻豆原创 has been positioned in the Leaders quadrant based on Ability to Execute and Completeness of Vision.

麻豆原创 CPQ supports organizations in producing accurate quotes — even in environments with advanced configuration and pricing requirements — helping accelerate sales cycles and improve sales execution across complex selling environments.

Extending CPQ leadership across 麻豆原创 Customer Experience

In modern enterprises, quoting connects directly to demand generation, pipeline management, contract processes, fulfilment, and service delivery. brings together commerce, customer data, marketing, sales, service, and sales performance management into an integrated portfolio designed to support truly connected customer journeys.

Within this portfolio, 麻豆原创 CPQ plays a pivotal role in the lead-to-cash journey. When integrated with 麻豆原创 CX solutions, it helps align pricing strategy, product configuration, customer agreements, and sales performance insights across the revenue lifecycle. The result is a more reliable transition from opportunity to revenue realization.

Connected lead-to-cash experience

For CX leaders, lead-to-cash is a core driver of experience differentiation and revenue execution. A connected lead-to-cash strategy ensures that:

• Customer intent is translated into accurate configuration and pricing.
• Sales engagements reflect approved pricing and product standards.
• Customer agreements are consistently captured and supported across systems.
• Sales performance and revenue outcomes remain visible and aligned across teams.

Business impact of connected lead-to-cash

Lead-to-cash determines how consistently organizations translate customer engagement into measurable outcomes.

By combining 麻豆原创 Customer Experience capabilities with a CPQ solution recognized for its ability to execute and completeness of vision, organizations strengthen alignment across sales, pricing, service, performance management, and ERP systems, transforming engagement into measurable outcomes with confidence and precision.

In today鈥檚 environment, customer experience and operational precision are closely connected. Strength in one reinforces performance across the other.

You can learn more about how 麻豆原创 CX connects 麻豆原创 Sales Cloud, 麻豆原创 CPQ, 麻豆原创 Service Cloud, sales performance management solutions, and 麻豆原创 ERP across the lead-to-cash journey .

Sindy Conway is senior Product Marketing consultant for 麻豆原创 Customer Experience.

In today鈥檚 omnichannel reality, delivering that elevated experience consistently and at scale is essential to protecting brand trust and loyalty. Peak moments like Black Friday and Cyber Monday amplify the challenge, when traffic surges and expectations for fast, personalized service are at their highest.

The team recognized that legacy systems couldn鈥檛 provide the speed, stability, or connected view required to meet those expectations at scale, prompting a shift to a modern customer experience (CX) foundation with 麻豆原创.

Modernizing the digital core with 麻豆原创 Commerce Cloud

Moving from legacy technology to gave Molton Brown a high鈥憄erformance engine designed for peak鈥憇eason reliability and continuous innovation. The results came quickly: 100% uptime during peak trade, even as volumes spiked to one order every three seconds during major events, freeing teams to focus on enhancing the customer experience rather than firefighting, and ensuring uninterrupted service for customers across global markets.

鈥淧eak performance isn鈥檛 a one鈥憈ime effort; it鈥檚 about reliability. We have to rely on technology operations to achieve 100% efficiency so the business can succeed, which in turn helps our customers succeed. Technology should enable business success, not block it鈥攁nd 麻豆原创 has proved that multiple times.鈥�

Naresh Krishnamurthy, Senior Manager 鈥� Business Transformation, Prestige, Kao UK Ltd

That stability also matters as product discovery increasingly begins beyond owned channels鈥攆rom social platforms to emerging AI鈥憄owered assistants鈥攚here consistent, trustworthy content and availability help the brand stay visible and credible wherever customers choose to engage. 麻豆原创鈥檚 evolving agentic commerce innovations anticipate this shift, ensuring products remain discoverable, trusted, and actionable across both human and AI agents.

A seamless luxury journey across channels

With and (formerly 麻豆原创 Emarsys) working together, Molton Brown aligns what customers see online with what they experience in store. Product categories, storytelling, and navigation are mirrored across channels; store associates can act on online browsing signals; and store teams are enabled with real鈥憈ime insight to deliver high鈥憈ouch clienteling experiences.

The result is an unbroken, premium journey that reduces friction and reinforces trust in the brand鈥攅xactly what luxury shoppers expect.

Personalization that builds loyalty, not just transactions

麻豆原创 Engagement Cloud helps Molton Brown deliver channel鈥慳ppropriate experiences, from mobile鈥慺irst engagement to email and in鈥憇tore clienteling, aligned to evolving customer preferences. These programs are complemented by thoughtful gifting moments, personalized birthday acknowledgments, and sustainability鈥慺ocused communications that strengthen repeat鈥憄urchase behavior.

Crucially, the team treats every holiday period as a data鈥憆ich learning cycle: months of performance testing, UX refinements, and campaign iteration inform what customers experience in the following season. Those insights help the team refine the experience so it remains consistent, intuitive, and premium, even under peak pressure. That consistency is what sustains loyalty, not just the promotions themselves.

As Naresh Krishnamurthy explains: 鈥淏lack Friday is not just about revenue; it鈥檚 about brand engagement and building the strong foundation that enhances the relationship through loyalty.鈥�

Ready for the next era of intelligent commerce

With a dependable CX core in place, Molton Brown is now exploring to anticipate risks ahead of campaigns, sharpen decision鈥憁aking, and streamline fulfillment鈥攁ugmenting the experience behind the scenes without compromising luxury standards.

This direction aligns naturally with 麻豆原创鈥檚 broader agentic commerce vision, where AI systems help interpret intent and keep trusted products discoverable and transactable across new surfaces鈥攁nother reason a reliable, 鈥渕achine鈥憆eadable鈥� CX foundation matters.

“Everything we鈥檙e doing ladders up to one goal: a truly connected customer experience鈥攑ersonal, consistent, and effortless in every channel.”

Molton Brown鈥檚 partnership with 麻豆原创 CX has reset what鈥檚 possible at peak, and every day after: dependable operations, consistent omnichannel experiences, and personalization that earns loyalty. The brand now scales confidently during its biggest moments, and stays ready for what鈥檚 next as AI changes how people (and agents) discover and buy.

This transformation positions Molton Brown to adapt quickly as customer expectations and digital commerce behaviors continue to evolve.

To explore how 麻豆原创 Commerce Cloud can elevate your customer experience, visit .

麻豆原创 is recognized in the 2026 Gartner庐 Magic Quadrant™ for Personalization Engines. 麻豆原创 Engagement Cloud now brings 麻豆原创鈥檚 trusted enterprise backbone to the customer experience, enabling organizations to connect customer insight and operational execution in real time. It builds on market-leading personalization capabilities.

麻豆原创 Engagement Cloud also incorporates AI鈥慹nabled insight to support responsible, efficient scaling of personalized engagement.

As part of this evolution, 麻豆原创 also announced 麻豆原创 Engagement Cloud, enterprise edition, which provides advanced administration, governance, and content and data鈥慶ontrol capabilities for organizations operating across multiple brands, regions, and teams.

鈥淭his approach helps organizations maintain consistency, compliance, and brand standards globally, which is increasingly important in an age of AI decision-making and automation, while also staying responsive to local needs,鈥� said Joanna Milliken, Head of 麻豆原创 Engagement Cloud.

For example, a global consumer goods company operating dozens of brands and regional teams can manage engagement roles, permissions, and data centrally while allowing local teams to execute the relevant interactions. When inventory levels, fulfillment delays, or service disruptions occur, engagement can adapt without manual coordination across disconnected systems.

Existing capabilities of the 麻豆原创 Emarsys solution remain available within 麻豆原创 Engagement Cloud. Customers can adopt new capabilities incrementally, based on their priorities and readiness. 麻豆原创 Engagement Cloud, enterprise edition, will be available beginning February 19, with additional innovations delivered through 麻豆原创鈥檚 innovation road map.

Visit the . Get 麻豆原创 news via  and .

Media Contact:
Mallory Kuno, +1 (425) 239-9362, mallory.kuno@sap.com, ET
麻豆原创 麻豆原创 Room; press@sap.com

This document contains forward-looking statements, which are predictions, projections, or other statements about future events. These statements are based on current expectations, forecasts, and assumptions that are subject to risks and uncertainties that could cause actual results and outcomes to materially differ.听 Additional information regarding these risks and uncertainties may be found in our filings with the Securities and Exchange Commission, including but not limited to the risk factors section of 麻豆原创鈥檚 2024 Annual Report on Form 20-F.
漏 2026 麻豆原创 SE. All rights reserved.
麻豆原创 and other 麻豆原创 products and services mentioned herein as well as their respective logos are trademarks or registered trademarks of 麻豆原创 SE in Germany and other countries. Please see for additional trademark information and notices.

We believe this recognition reflects the continued momentum of in helping enterprises orchestrate real鈥憈ime, AI鈥憄owered engagement at a global scale, connecting data, channels, and experiences to drive measurable business impact.

Helping brands scale AI-powered engagement across channels

Leading brands鈥攊ncluding , John Frieda, PUMA, and Gibson鈥攗se 麻豆原创 Engagement Cloud to deliver connected, personalized journeys that increase engagement, accelerate growth, and build long-term customer loyalty.

According to the report, 麻豆原创鈥檚 ability to support enterprise鈥慻rade, real鈥憈ime engagement across channels remains a key differentiator. 麻豆原创鈥檚 personalization capabilities are powered by advanced segmentation, embedded AI decisioning, and intelligent triggering to deliver timely, relevant, and consistent experiences.

Organizations using 麻豆原创 continue to see measurable outcomes, including improved customer loyalty, higher conversion rates, and increased average order value.

Driving measurable business impact with event鈥慴ased and behavior鈥憀ed orchestration

We believe this year鈥檚 placement also reflects 麻豆原创鈥檚 strength in orchestrating engagement using real鈥憈ime behavioral, transactional, and operational signals across the business.

With 麻豆原创 Engagement Cloud, brands can activate journeys triggered by events occurring across their business to:

• Boost retention through timely, context鈥慳ware engagement
• Increase conversions with more relevant, personalized interactions
• Strengthen loyalty through connected, lifecycle-driven touchpoints

These results demonstrate 麻豆原创鈥檚 ability to move enterprises beyond channel execution toward true omnichannel orchestration.

Unifying customer experiences with native 麻豆原创 integration

麻豆原创 Engagement Cloud connects marketing, commerce, service, loyalty, sales, and operational data, creating a unified, real-time customer view that powers intelligent engagement across every touchpoint.

This bi鈥慸irectional flow of data gives every customer鈥慺acing team access to the same real鈥憈ime customer view, helping brands drive revenue impact, reduce churn, and improve service outcomes.

Global scale, flexibility, and trust

麻豆原创鈥檚 long鈥憇tanding global footprint and enterprise-ready architecture continue to support its leadership positioning. With a cloud鈥憂ative, composable foundation, embedded privacy and compliance capabilities, and a robust partner ecosystem, 麻豆原创 enables organizations to securely and reliably scale personalized engagement across regions and business models.

Whether operating in five markets or 50, enterprises rely on 麻豆原创 to deliver personalized experiences with confidence.

Customer success reflecting real鈥憌orld impact

Customers on Gartner庐 Peer Insights™ continue to recognize 麻豆原创 for ease of integration, deployment support, and customer partnership. Recent examples include:

• , which uses 麻豆原创 for CRM and marketing automation that supports interaction and communication with customers to increase buyback, retention, and loyalty. This includes CRM ads, push notification apps, personalization campaigns, e-mail and SMS campaign execution, and website and app personalization and recommendations.
• , which increased sales by 30% in three years by using 麻豆原创 solutions, to interact directly with customers within highly personalized omnichannel journeys.
• , which saw a more than 40% increase in CRM revenue and more than 150% in commerce traffic during the holiday season by using 麻豆原创 Customer Experience solutions that empower CHRIST to put customers at the center of everything the company does.

These results highlight the tangible value organizations are achieving with 麻豆原创鈥檚 AI-powered personalization capabilities.

We feel 麻豆原创鈥檚 recognition as a Leader in the 2026 Gartner Magic Quadrant for Personalization Engines underscores the strength of its strategy and continued innovation across 麻豆原创 Engagement Cloud. 麻豆原创 remains committed to helping brands activate data, personalize interactions, connect experiences, and scale engagement with confidence.

Visit 麻豆原创 Engagement Cloud area of sap.com to .

Sara Richter is CMO of 麻豆原创 Emarsys.

Gartner and Magic Quadrant are trademarks of Gartner, Inc., and/or its affiliates.
Gartner, Magic Quadrant for Personalization Engines, By , , , , 3 February 2026
Gartner does not endorse any vendor, product or service depicted in its research publications and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner鈥檚 Research & Advisory organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose.

鈥痚nables organizations鈥攈owever complex, across however many channels, and regardless of which CRM they run鈥攖o produce quick and聽accurate聽quotes, accommodating the most advanced configuration and pricing requirements,听resulting in聽a better sales experience and faster sales cycles.

Gartner evaluated 16聽vendors and聽named 麻豆原创 a Leader based on our Ability to Execute and Completeness of Vision.听We believe this聽recognition serves as an acknowledgment of 麻豆原创鈥檚聽ongoing聽commitment to providing customers with a CPQ solution that can meet聽and exceed聽their needs.听

麻豆原创 CPQ is an essential聽component聽of the 麻豆原创 product portfolio that help automate the quote-to-cash process, which enables organizations to convert sales opportunities into profitable repeat customers. 麻豆原创 customers can transform to 鈥渆verything-as-a-service鈥� with innovative revenue models, quickly adapt to聽market聽changes,听support multiple sales聽channels,听and support聽regulatory聽compliance with end-to-end automation.听聽

Our customers are the reason we do this, and they participated in the  process by providing reviews that included: 

• 鈥淚t just makes the whole sales cycle move faster.鈥�
• 鈥淚t was relatively simple to onboard when I was a new user鈥澛爌latforms and creating value for customer.鈥�
• 鈥淲e had a very positive experience with 麻豆原创, the platform is scalable, stable.鈥�
• 鈥淎n intuitive user interface that simplifies configuration, robust integration capabilities. Powerful customization options.鈥�
• 鈥溌槎乖� CPQ is amazingly stable and consistent product with the ability to connect with different platforms and creating value for customer.鈥�

Customer case studies provide descriptions of specific value. For example, , has increased聽the number of quotes created per month by 70 percent.听

鈥淵ou聽basically give聽the salesperson one to two days of their week back by using 麻豆原创 CPQ,” noted Dominic Kasten, director of Sales Technologies for聽.听“When you give time back to salespeople, you are encouraging them to sell solutions to customers instead of just reacting to specifications.”

Hear from other customers and learn more about how 麻豆原创 helps to automate quote-to-cash with鈥槎乖� CPQ,鈥�,鈥�, and鈥� at sap.com.

David Imbert is head of Product Marketing for Finance at 麻豆原创.听
Lawrence Martin is chief product officer for Finance at 麻豆原创.听

Gartner, Magic Quadrant for Configure, Price, and Quote Application聽Suites, Luke聽Tipping, Mark Lewis, January 22, 2026聽
Gartner does not endorse any company, vendor,听product聽or service depicted in its publications, and does not聽advise technology users to聽select only those vendors with the highest ratings or other designation. Gartner publications consist of the opinions of Gartner鈥檚 business and technology insights organization and should not be construed as statements of fact. Gartner聽disclaims聽all warranties, expressed or implied, with respect to this publication, including any warranties of merchantability or fitness for a particular purpose.
Gartner and Magic Quadrant are聽a trademark聽of Gartner, Inc., and/or its聽affiliates. This聽graphic was published by Gartner, Inc. as part of a larger research document聽and should be evaluated in the context of the entire document. The Gartner document is available upon request from 麻豆原创.听

Agentic AI was everywhere at NRF, emblazoned across the booths of technology exhibitors and the focus of many of the daily conference sessions. The message was simple: retailers face a major upheaval as consumers switch from traditional browser-based search to AI-enabled product discovery.

Consumers are rapidly adopting AI agents to help them find, compare, and, increasingly, buy products鈥攖his while many brands are still optimizing for search engines and are quietly disappearing from the models driving the next generation of product discovery.

鈥淎gentic commerce鈥攕hopping powered by AI agents acting on our behalf鈥攔epresents a seismic shift in the marketplace,鈥� McKinsey, the strategic management consultancy, noted in a . 鈥淚t moves us toward a world in which AI anticipates consumer needs, navigates shopping options, negotiates deals, and executes transactions, all in alignment with human intent yet acting independently via multistep chains of actions enabled by reasoning models.鈥�

This, as speakers and panelists at the NRF conference acknowledged, isn鈥檛 just an evolution of e-commerce; it鈥檚 a rethinking of shopping itself, in which the boundaries between platforms, services, and experiences give way to an integrated, intent-driven flow through highly personalized consumer journeys that deliver a fast, frictionless outcome.

As the McKinsey report noted, the stakes are high. By 2030, the U.S. B2C retail market alone could see up to US$1 trillion in orchestrated revenue from agentic commerce, with global projections reaching as high as $3 trillion to $5 trillion.

This means all the participants in the retail chain, from brands and retailers to logistics and payment service providers, will need to adapt to the new paradigm and successfully navigate the challenges of trust, risk, and innovation.

To help retailers address the immediate challenges posed by the shift to agentic commerce, 麻豆原创 argues that three steps are necessary: first, restructuring web-page product data to be machine-readable; second, adding semantic summaries for LLM reasoning; and third, tagging products by the problems they solve, not just their attributes.

麻豆原创 announced a series of AI-enhanced retail innovations at NRF 2026, including a new storefront model context protocol (MCP) server that enables retailers to make their digital storefronts intelligible to AI and the new AI-native Retail Intelligence solution in 麻豆原创 Business Data Cloud that leverages data from across 麻豆原创 software and third-party systems to help provide accurate demand planning, improved forecast accuracy, and lower inventory costs to drive more seamless omnichannel engagements.

麻豆原创 Customer Experience has also unveiled a recently that can be combined with the聽, creating one conversational AI that can handle the entire journey from product discovery and transaction to post-sales support.

These moves reflect a recognition that that LLMs have become a legitimate shopping channel, and that product discovery is moving from search engines to AI recommendations.

This shift challenges years of SEO and brand building. To stay relevant, 麻豆原创 believes retailers must take an AI-first approach and have strong, connected data that helps agents understand products, predict demand, and respond quickly. Without this strong data foundation, brands will be at risk because if customers get poor recommendations and errors in pricing, trust can disappear fast.

Although some early agentic AI adopters in the retail sector are already seeing the benefits of agentic commerce, many global retailers are still ill-prepared for the holistic transformation they need to succeed in this new retail environment.

As McKinsey noted in a separate , 鈥渨hile most retail merchandising teams have invested in automation tools聽and experimented with AI, 71% of merchants say that AI merchandising tools have had limited to no effect on their business so far.鈥�

鈥淭he challenge,鈥� McKinsey said, 鈥渙ften lies less in the technology than in how it鈥檚 integrated and used. Systems remain fragmented, data is too messy to use to deliver useful recommendations, and adoption is uneven: 61% of respondents say that their organization isn鈥檛 at all or is only slightly prepared to scale AI across merchandising.鈥�

Onstage at NRF, Andre Bechtold, president for 麻豆原创 Industries & Experience, also emphasized that retailers should prepare now for agentic commerce and noted that simply “bolting on” AI tools to existing systems is not enough.

鈥淩etailers are operating in an environment defined by volatility鈥攖ariffs, margin pressure, supply chain disruption, and customers that expect real-time, hyper-personalized experiences everywhere,鈥� Bechtold said during a discussion with Gymshark, the workout apparel retailer. 鈥淎t the same time, boards and investors are asking a tougher question than ever before: what outcomes are we actually getting?鈥�

鈥淭he challenge,鈥� he said, 鈥渋sn鈥檛 a lack of innovation. In fact, most retailers have plenty of tools, pilots, and point solutions. The real issue is that disconnected technology doesn鈥檛 translate into resilient growth. That鈥檚 why the conversation is shifting. It鈥檚 no longer about isolated AI use cases or shiny new features. It鈥檚 about whether AI and data are embedded across the business鈥攃onnecting supply chains, finance, merchandising, and customer engagement鈥攊n ways leaders can trust.鈥�

Echoing the same point, Thomas Saueressig, member of the Executive Board of 麻豆原创 SE, Customer Services & Delivery, commenting in a this week about a PwC survey of global CEOs that found that companies rarely achieve lower costs or higher sales through the use of AI, emphasized that AI only contributes value when consistently embedded in business processes. 聽鈥淎s long as AI runs alongside the core business as an isolated project, the effects remain limited,鈥� he said.

Our vision for agentic commerce is bold. In , we showcase a future where humans and AI agents collaborate to drive intelligent recommendations, proactive operations, efficient business processes, and deeper customer relationships. While this vision points forward, 麻豆原创鈥檚 focus is firmly grounded in helping retailers take practical steps today. This isn鈥檛 about flashy demos of a distant future鈥攊t鈥檚 about building the foundation now for how consumers will buy and retailers will sell in the years ahead.

Unlike traditional AI systems that respond to prompts, agentic systems act on intent. They learn from preferences, make proactive recommendations, and can complete transactions on a shopper鈥檚 behalf. These agents are increasingly becoming the starting point of the buying journey, reshaping how brands compete for visibility, trust, and loyalty.

This evolution introduces both opportunity and risk. As AI agents mediate more interactions between brands and consumers, retailers must rethink how they capture intent, transact with agents, and deliver post-purchase experiences that reinforce trust.

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Discovery is moving from search to assistants

Historically, product discovery revolved around search engines, marketplaces, and brand-owned storefronts. That model is shifting quickly. Answer engines and AI shopping agents are becoming new entry points for commerce鈥攐ften before a shopper ever visits a retailer鈥檚 site.

Like marketplaces before them, AI agents introduce a new layer between brands and customers. The difference is speed and autonomy. Agents don鈥檛 just surface options; they reason, decide, and act.

For retailers, success is no longer about ranking on a page. It鈥檚 about ensuring products are visible, understandable, and trusted by machines that influence purchase decisions on behalf of humans.

At NRF, 麻豆原创 expanded its agentic commerce vision with the announcement of the storefront MCP server for 麻豆原创 Commerce Cloud, planned for Q2 availability. The storefront model context protocol (MCP) server can enable channel-less commerce by allowing businesses to safely and reliably engage with multiple AI agents鈥攚hether embedded in a retailer鈥檚 own experiences or originating from third-party assistants like ChatGPT or Perplexity.

The storefront MCP server helps merchants surface products and can enable buying across channels for both people and machines. It鈥檚 the first of many steps 麻豆原创 is taking to help customers fully participate in agentic commerce by supporting MCP, ACP, UCP, and other emerging agentic protocols.

Product content becomes the currency of visibility

In an agent-driven world, product content is no longer just marketing鈥攊t鈥檚 operational infrastructure. AI agents cannot recommend what they cannot interpret. Every attribute, image, specification, availability signal, and proof point directly impacts whether a product is surfaced, compared, or selected.

This is where generative engine optimization (GEO) is evolving. Optimization must now serve two audiences: humans and machines. Product data must be structured, consistent, and enriched, so AI agents can confidently represent it to shoppers.

The in helps transform how merchants manage product data at scale. It can clean catalogs, enrich attributes, standardize details, fill gaps, and support multilingual content using real-time data. The agent can scale to catalogs with more than 10 million items, helping teams improve content 70% faster, increase data completeness by 5%, and reduce maintenance effort by 63%.

With AI-ready product data as its foundation, retailers can better match shopper intent, optimize merchandising by channel, and improve pricing and delivery decisions with precision.

Payments must evolve for autonomous commerce

As buying journeys fragment across devices, channels, and agents, payments must become more flexible and nearly invisible. Consumers expect to pay how and when they choose, including through agent-initiated transactions.

New payment rails like FedNow, RTP, and stablecoins are enabling faster, lower-cost transactions, while wallets and bank-based payments continue to converge. Networks such as Visa and Mastercard are already preparing for autonomous commerce by allowing consumers to set spending limits and controls for AI agents.

For retailers, the priority is delivering frictionless, secure payment experiences that integrate seamlessly into agent workflows.

The can enable this flexibility through a no-code, low-code approach. Its headless, extensible architecture helps support diverse payment methods, ensure compliance through automatic updates, and integrate natively with 麻豆原创 Commerce Cloud鈥攚orking to give retailers agility without sacrificing control or scalability.

Returns become a strategic intelligence engine

Returns are one of retail鈥檚 biggest challenges. According to IHL Group, global returns have surpassed US$1.9 trillion and are growing faster than sales. What was once a cost center is now a strategic differentiator.

The next phase of returns management is defined by intelligence. AI enables 鈥渒eep, reject, or return鈥� decisioning based on loyalty history, behavioral signals, margin impact, and lifetime value. Returns data becomes a feedback loop that improves forecasting, product quality, and merchandising decisions.

Complete, connected data is essential. 麻豆原创 can deliver this through native integration between 麻豆原创 ERP and 麻豆原创 Commerce Cloud, creating a single source of truth across inventory, costs, and transactions. found that organizations using both platforms achieved up to 80% lower TCO, up to 90% productivity gains, and 105%鈥�245% revenue uplift from hyper-personalized experiences.

can extend this foundation across the full returns journey, helping to orchestrate centralized rules, guided returns, real-time inventory visibility, and faster refunds鈥攖urning returns into a loyalty-building growth lever rather than a revenue drain.

Commerce is detaching from the storefront

As predicted at the end of 2025, AI agents are taking on more shopping tasks, pushing commerce beyond traditional storefronts. A shopper may simply state an intent and let an agent handle research, selection, and checkout.

Discoverability now depends on structured, trustworthy signals鈥攔eviews, ratings, social proof, and consistent data that agents rely on to evaluate quality and brand credibility.

Retailers must move beyond transactional efficiency to deliver connected, personalized experiences across every touchpoint. Loyalty programs must reward engagement, not just purchases. Inventory visibility, accurate delivery promises, and proactive issue resolution become table stakes.

can enable retailers to design adaptive loyalty strategies for this new environment, personalizing rewards and offers based on real-time behavior鈥攚hether purchases happen through traditional channels or AI agents. These insights can then feed transactional agents, helping to improve relevance and outcomes across the journey.

Operational reliability remains critical. 麻豆原创 Order Management Services help unify order, inventory, fulfillment, and POS data, while agentic innovations like the Order Reliability Agent can proactively resolve fulfillment issues before they impact customers.

Trust is the core retail responsibility

As agentic systems influence more of commerce, trust becomes the most valuable asset retailers can protect. Consumers must trust that their data, preferences, and payments are secure and governed responsibly.

Retailers and commerce providers increasingly act as AI trust custodians, balancing intelligence with deterministic constraints and governance. On-site AI can scale associate expertise and personalization while preserving brand integrity and customer confidence.

Commerce is becoming an ecosystem of intelligent interactions鈥攚here discovery, payments, fulfillment, and returns are connected by agents acting on behalf of shoppers and businesses alike.

The winners will be those who align product intelligence, flexible payments, data-driven returns, and trust across every touchpoint. Agentic AI can make commerce more personal, efficient, and scalable鈥攂ut only for those who build the right foundations today.

To learn more about how 麻豆原创 Commerce Cloud is powering AI-driven commerce, visit .

Kollen Glynn is global head of 麻豆原创 Commerce Cloud for 麻豆原创 Customer Experience.

This isn鈥檛 just a consumer trend; it is reshaping engagement models across industries.

The Q4 2025 (麻豆原创 CX) release propels this transformation further with new out-of-the-box agents designed for customer service and the ability to easily build custom agents with Joule Studio. Additionally, AI features like predictive segmentation and AI-assisted reporting expedite planning and decision-making鈥攆oundational catalysts for future-ready businesses.

With WalkMe Premium now available across 麻豆原创 CX applications, teams can upskill and reskill with in-the-moment guidance. And 麻豆原创 Customer Loyalty Management takes new engagement models to the next level, helping businesses strengthen relationships and drive long-term growth.

Here, explore more of the highlights from the Q4 2025 release. And for full sub-solution details, see recaps for , , , , and .

Better customer engagement with out-of-the box agents and custom tools

With 麻豆原创, customer experience applications, data and AI come together as one鈥攑owered by 麻豆原创 Business Technology Platform. Whether it鈥檚 resolving an issue or managing inventory, CX applications connected to 麻豆原创 ERP keep processes running smoothly. AI agents take it further, by reasoning and acting directly in core processes, turning complexity into clarity. One of the most critical areas is in customer support.

• : Deliver instant and accurate self-service by putting knowledge at customers鈥� fingertips. Deflect common inquiries, resolve complex questions with AI, and escalate seamlessly to human agents when needed鈥攔educing contact center load while improving customer satisfaction.
  
  Digital Service Agent can be combined with , creating one conversational AI that handles the entire journey鈥攆rom product discovery and transaction to post-sales support. Customers can ask questions, get answers, and complete purchases in a single frictionless interaction. Together these agents unlock agentic commerce and intelligent service, which strengthens customer relationships and deliver experiences that truly stand out.

• : Create custom, business-ready AI agents for 麻豆原创 Customer Experience Cloud applications鈥攆ast and without complexity. Joule Studio, a part of , gives developers a powerful low-code, no-code environment to create and deploy AI agents and connect them seamlessly to Joule, 麻豆原创 CX apps and third-party systems. These agents can retrieve information, complete tasks, and run autonomous actions grounded in enterprise data from 麻豆原创 CX, 麻豆原创 Knowledge Graph, and non-麻豆原创 systems.
  
  For example, users can build a sales assistant agent that instantly pulls historical purchase records, analyzes buying patterns, and recommends the most relevant products or offers鈥攈elping sales teams increase conversion rates and shorten sales cycles. Learn how to .

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Boost insights and workforce skilling with AI-powered guidance

AI is no longer optional; it鈥檚 the engine behind smarter, faster customer engagement. As digital experiences raise the bar, customers expect speed, personalization, and simplicity in every interaction. Meeting those expectations requires more than automation. It demands AI-driven insights and skills that scale across the organization.

• : 麻豆原创 is embedding AI upskilling into the core of customer experience applications with WalkMe Premium for 麻豆原创 CX solutions. This solution empowers employees to work smarter and learn faster, driving better outcomes from day one. With real-time, role-based guidance and automation across , , , and , teams can unlock the full potential for 麻豆原创 CX solutions without complexity.

• : Easily generate custom reports and comparisons in 麻豆原创 Emarsys, and uncover campaign and customer insights instantly.

• : Enable service agents in 麻豆原创 Service Cloud to quickly understand key consumption trends for a premise. With AI-generated summaries of consumption graphs, agents can immediately identify usage fluctuations, anomalies, and important patterns to support faster resolution for utilities customers.
• : Check the overall health of the sales pipeline in 麻豆原创 Sales Cloud and display opportunities based on quantity and probability score.
• Promotion and account plan configuration: In , customers can configure promotion types and account plan types, defining scope, levels, spend, and baseline management, in order to enable flexible planning and support future indirect promotions.

• Engagement events: In 麻豆原创 Emarsys, ingest inbound events from external data sources to further enhance segmentation and personalization throughout the journey.
• (pilot): Use predictive AI segments in 麻豆原创 Emarsys to reach audiences that are most likely to engage based on a contact鈥檚 behavior, status, or channel preference.

Build lasting connections with 麻豆原创 Customer Loyalty Management

Customer loyalty is more than a metric; it鈥檚 a long-term strategy for growth. As expectations rise, organizations need solutions that create meaningful, lasting relationships. 麻豆原创 Customer Loyalty Management helps businesses deliver personalized experiences, reward trust, and strengthen engagement at every touchpoint, turning everyday interactions into enduring connections.

• : Empowers businesses with AI-driven insights to capture and unify customer data in a dynamic, cloud-based loyalty profile. These profiles provide deep insights into individual motivations, enabling smarter segmentation and highly targeted marketing campaigns. From managing global programs on a unified platform to forming strategic alliances and scaling initiatives for impact, 麻豆原创 helps transform loyalty into a measurable, powerful engine for sustainable engagement and success. 麻豆原创 Customer Loyalty Management has integrations for 麻豆原创 Service Cloud and 麻豆原创 S/HANA Cloud Private Edition to make the transformation faster.

The future of engagement is here, get ready with 麻豆原创

How we engage is changing faster than ever. 麻豆原创鈥檚 Q4 2025 innovations in customer experience anticipate this shift on every level. 麻豆原创 CX is enabling organizations to move beyond reactive strategies and into a world of proactive, personalized experiences.

Businesses that embrace and integrate these new models throughout their enterprise, pairing agentic AI with human intelligence and creativity, will set new standards for customer loyalty and growth.

Learn more about 麻豆原创 CX in Q4 2025

Read the 麻豆原创 Help documentation to get started with these new capabilities.

• 鈥�&苍产蝉辫;
•   
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Balaji Balasubramanian is president and chief product officer for 麻豆原创 Customer Experience and Consumer Industries.

Since the launch, has empowered organizations to streamline operations for increased efficiency, reduced manual workloads, and untangled multi-channel complexity. With this approach, businesses can deliver on customer promises with seamless customer experience. This momentum has also been recognized in the market, as 麻豆原创 Order Management Services was named a Leader in by IHL Group for its robust capabilities and enterprise readiness.

, a leading German home improvement retailer, is already seeing the benefits. With 麻豆原创 Order Management Services, Hornbach connects digital and physical stores with full visibility into day-to-day transactions, providing omnichannel retail experience at scale to its customers.

However, the retail landscape is evolving continuously. While profitable growth is critical to businesses, earning and sustaining customer loyalty now is becoming more important. Ahead of the curve, 麻豆原创 has heavily invested in expanding capabilities in the 麻豆原创 Order Management Services bundle to help retailers deliver on customer promises with intelligence, scalability, and adaptability, leading to boosts in customer loyalty.

At NRF 2026, 麻豆原创 is unveiling new and enhanced capabilities that power retailers to not only operate more efficiently but also achieve loyalty-led growth through every order.

AI in 麻豆原创 Order Management Services

Joule in 麻豆原创 Order Management Services: 麻豆原创鈥檚 AI copilot, Joule, is now available in 麻豆原创 Order Management Services. Access order-related data, analysis, and insights through conversations in natural language and visual display.

Order Reliability Agent: Accelerate operational efficiency with the Order Reliability Agent in 麻豆原创 Order Management Services. Proactively mitigate and resolve any potential issues and gaps, such as stock discrepancies or process bottlenecks, to help ensure every order is fulfilled seamlessly and to boost customer loyalty.

AI-assisted copy generation and translations: Create promotional copy in seconds and translate it into any language with AI assistance, helping to reduce manual workload and accelerate time-to-market.

UI enhancements

Workflow-optimized UI: The enhanced and unified UI in 麻豆原创 Order Management Services can deliver a consistent user experience across order, inventory, and fulfillment operations. Teams can now work faster, reduce training time, and maintain full visibility across every step of the order lifecycle.

Watch the 麻豆原创 Order Management Services  to get a closer look at the AI capabilities in action. Visit the 麻豆原创 booth at NRF 2026, January 11 鈥� 13, to learn more about 麻豆原创 Order Management Services and catch an in-person demo.

Emilie Fournelle is head of Product Management for 麻豆原创 Order Management Services at 麻豆原创.

NEW YORK 鈥� (NYSE: 麻豆原创) today announced a new generation of AI-enhanced retail innovations at NRF 2026: Retail鈥檚 Big Show.

麻豆原创 continues to infuse AI into the DNA of every part of its retail solutions, reinforcing its suite-first strategy and helping retailers operate with greater intelligence, resilience and trust while delivering better experiences for customers everywhere.

鈥淩etailers face a landscape where AI is no longer optional,鈥� said Balaji Balasubramanian, President and Chief Product Officer for Customer Experience and Consumer Industries, 麻豆原创 SE. 鈥溌槎乖� provides one closed-loop, AI-enhanced retail operating system that ties planning, execution and engagement together. We put data and AI at the heart of retail, delivering speed, personalization and growth across every channel and segment.鈥�

AI that turns retail data into actionable intelligence

The Retail Intelligence solution in provides accurate demand and inventory planning, leveraging retailers’ data from across 麻豆原创 software and third-party systems to drive profitable growth through actionable, real-time insights. Purpose-built for retailers and direct-to-consumer businesses, it will be generally available in the first half of 2026.

Harmonizing real-time data from sales, inventory, customers and suppliers, Retail Intelligence uses AI-generated simulations so planners can anticipate outcomes and optimize inventory. This improves forecast accuracy, reduces manual planning effort, lowers inventory costs and raises service levels. All this drives more seamless omnichannel engagements, which strengthen customer loyalty and enable growth without adding complexity for retailers.

鈥淩etailers are seeking built-in, embedded AI solutions to help balance daily operations, future planning and agility to manage a dynamic market,鈥� said Ananda Chakravarty, Vice President of IDC Retail Insights. 鈥淲hat sets 麻豆原创 apart is the holistic nature of its approach, offering an agentic operating system that works in the background, connects data and orchestrates agents. 麻豆原创 makes it an easy lift for retailers to achieve enterprise-wide intelligence, avoiding the complexity of many point solutions.鈥�

AI that streamlines modern retail operations

Retailers must make fast, confident decisions across assortments, pricing and planning. To meet that need, 麻豆原创 announced new AI-assisted assortment management capabilities, allowing planners to create, modify or retire assortments using natural language through the Joule copilot. This reduces the bottleneck on expert users, enabling faster responses to market shifts and freeing time for higher-value merchandising decisions.

麻豆原创 also introduced omnichannel sales promotions in sales orders, integrating the 麻豆原创 Omnichannel Promotion Pricing solution with the 麻豆原创 S/4HANA Cloud Public Edition, retail, fashion and vertical business solution. This enables advanced promotions such as bonus buys to be applied consistently across diverse channels, enabling a single source of truth for pricing and promotions in store and online, so retailers can deliver a consistent experience.

In addition, 麻豆原创 is delivering deeper merchandising, segmentation and manufacturing support in the solution, tailored to fashion wholesalers and manufacturers. These enhancements provide the data and process foundation needed for AI-assisted fashion operations across the business.

AI that drives better customer engagement

As shopping journeys increasingly begin with AI assistants rather than storefronts or search engines, retailers need new ways to be present wherever buying decisions are made. 麻豆原创 helps retailers connect products, pricing, inventory and promotions directly to AI-enabled discovery and shopping experiences, unlocking agentic commerce with its new storefront MCP server, part of the 麻豆原创 Commerce Cloud solution.

Retailers can now make their storefronts intelligible to AI, driving shopping experiences not only on their storefronts but also on platforms such as ChatGPT.听This creates a truly channel-less commerce experience, one where engagement, discovery and transaction happen more seamlessly across human and AI-assisted touch points.

AI that builds customer loyalty

As customer expectations rise and fulfillment networks grow more complex, retailers need confidence that every order will be delivered as promised, using AI solutions that provide proactive visibility and guidance to help keep operations running smoothly and at scale. And as brand visibility shifts in the age of agentic commerce, reliable and consistent shopping experiences are more important than ever to drive sustained customer loyalty and trust.

麻豆原创 announced Order Reliability Agent as part of the 麻豆原创 Order Management Services bundle, planned for release in the second quarter of 2026. The new agent proactively identifies and resolves potential order issues, helping associates answer common questions about order status, stock availability and fulfillment risks before they impact customers.

By combining agentic autonomy with human oversight where judgment matters, these innovations from 麻豆原创 drive insightful planning and improve operational efficiency, both enhancing the customer experience and driving profitable growth.

Visit the . Get 麻豆原创 news via  and .

Media contact:
Mallory Kuno, +1 (425) 239-9362,听mallory.kuno@sap.com, ET
麻豆原创 麻豆原创 Room; press@sap.com

This document contains forward-looking statements, which are predictions, projections, or other statements about future events. These statements are based on current expectations, forecasts, and assumptions that are subject to risks and uncertainties that could cause actual results and outcomes to materially differ.听 Additional information regarding these risks and uncertainties may be found in our filings with the Securities and Exchange Commission, including but not limited to the risk factors section of 麻豆原创鈥檚 2024 Annual Report on Form 20-F.
漏 2026 麻豆原创 SE. All rights reserved.
麻豆原创 and other 麻豆原创 products and services mentioned herein as well as their respective logos are trademarks or registered trademarks of 麻豆原创 SE in Germany and other countries. Please see for additional trademark information and notices.

Seismic shifts are not new for retailers鈥攂ack in 1999, e-commerce was still an afterthought. By 2000, everything changed as retailers went all-in on digital.

In聽2025,听we are聽living in yet聽another pivotal year.听This holiday season might feel familiar as you scroll through deals, compare brands, and race to beat shipping deadlines. But beneath the surface, something far more transformative is happening.听The year 2025 will聽likely be聽the last consumers shop as they do now.听听

Agentic AI is reshaping commerce by making shopping faster, smarter, and effortless. Discovery is moving from people browsing their favorite brands to intelligent orchestration. Instead of opening 10 tabs to hunt for the right deal, shoppers will simply ask: 鈥淔ind me the highest rated black, puffy winter coat, size 10, under $200 that ships in two days.鈥�

The agent will handle the rest鈥攕canning thousands of options, validating reviews, confirming delivery timelines, even factoring in loyalty perks. That future isn鈥檛 tomorrow; it鈥檚 already here, and by next holiday season, most shopping journeys will begin, evolve, or end with AI agents.

While this type of shopping creates convenience for shoppers, it creates a challenge for retailers that have focused on brand campaigns and poured millions of dollars into advertising to be the 鈥渂rand of choice鈥� in the discovery process. Decades of investment into SEO, paid traffic, and brand recognition are losing their edge. While not abandoning these strategies entirely, they must evolve for the AI-first world.

However, there is something that hasn鈥檛 changed over the course of decades: the need to create loyal customers who make repeat purchases and give the greatest share of their wallets. This, too, is more challenging than ever. In fact, 72% of consumers report that this holiday season they will only that consistently meet their needs in the moment.

Creating customer loyalty in the age of agentic commerce means conquering two critical fronts:

• Optimizing for discoverability: Agents will favor retailers that make buying seamless.
• Creating customer loyalty post-purchase: With discovery being augmented by AI agents, humans will now give their ongoing loyalty based on post-purchase experiences. On-time delivery, easy returns, and rewards that feel personal are the new battleground for brand equity.听 And with agents learning from human behavior, exceeding shopper expectations post-purchase can ultimately impact a brand鈥檚 likelihood of being recommended in the discovery phase.

The question remains: how do we move from esoteric AI conversations to practical strategies?

Discovery and loyalty: How to win in the age of agentic AI

• Make your catalog agent-ready: Treat AI as a new kind of shopper. Ensure product feeds are rich, structured, and machine-readable, complete with attributes, use-case-driven descriptions, real-time pricing, and accurate inventory. Clean, structured product data is now the foundation of intelligent discovery.
• Create solutions, not just SKUs: AI-driven traffic behaves differently. Design bundles, add-ons, and value stacks that solve specific problems and allow agents to match shoppers with outcomes, not just product lists.
• Build trustworthy, accessible information: Operationalize trust by surfacing verified reviews, transparent pricing, sustainability details, and clear return policies. Make this data accessible through well-structured APIs, not scraping, so agents and humans see the same reliable truth.
• Let prediction power personalization: Use unified data and AI to predict what customers want before they act, enabling real-time next-best-actions across email, SMS, push, in-app, and other emerging channels. This predictive intelligence turns fragmented campaigns into that deliver higher engagement and revenue.
• Make loyalty the thread that ties every experience together: Loyalty is no longer a program. It鈥檚 a relationship. Use every interaction to tailor meaningful, emotional moments that adapt, remember, and feel consistent across channels in order to help convert agent-driven traffic. Then, use personalized exclusives and perks to foster high-value relationships with those new customers.
• Deliver on your promises, every time: Eighty-eight percent of customers leave a brand after one bad experience. That鈥檚 why operational reliability is the new loyalty. Bring order, inventory, payments, and fulfillment into alignment, so customers receive what they were promised, when they were promised. Loyalty now begins at checkout.
• Prepare for the new return economy: Agent-driven buying makes it easy for consumers to purchase first and decide later. Set clear limits to protect margins and reduce friction in the returns journey because a seamless return can build more loyalty than the purchase itself.

麻豆原创 is already helping brands prepare for this future with AI-enabled technologies across , , , and .

A brand already building for the future

Global sports brand . Historically reliant on seasonal campaigns, Mizuno wanted a more sustainable way to engage its diverse customer base across 10 product categories and multiple channels. Mizuno unified its customer data and used AI to create personalized journeys, turning one-off interactions into long-term relationships.

The results speak for themselves:

• 52% year-over-year (YoY) increase in active customers
• 62% increase in revenue from premium customers
• 35% increase in customer win-backs
• 33% increase in the number of orders

麻豆原创: A partner built for scale, stability, and growth

As customer behavior evolves and AI reshapes what鈥檚 possible, one thing remains constant: 麻豆原创鈥檚 commitment to helping brands win their biggest commercial moments. This year鈥檚 holiday results make that clearer than ever. We鈥檙e not just helping brands plan for peak season鈥攚e鈥檙e helping them execute it with precision, intelligence, and confidence.

Nearly 20% YoY growth in total messages sent underscores the trust brands place in 麻豆原创 Emarsys to deliver at scale. Mobile and emerging channels surged鈥攊n-app (+61%), SMS (+32%), push (+27%), and inbox (+91%) all saw significant YoY gains鈥攁s brands met customers exactly where they were browsing and buying. Omnichannel maturity accelerated with brands using a richer mix of channels to create connected, high-value experiences across every stage of the shopping journey. And with 100% uptime and flawless reliability, teams executed independently and confidently, even during their highest-volume moments.

Paired with exceptional commerce performance, the story becomes even more compelling: brands used more intelligent engagement to guide shoppers toward higher-value purchases (+18% YoY average order value) and ultimately drove substantial YoY revenue growth (+40% gross merchandise value)鈥攁ll powered by a that delivered uninterrupted performance with 100% uptime through the holiday shopping rush, ensuring we鈥檙e here for our customers when it matters most.

This is what partnership looks like: scale, intelligence, reliability, and results so brands can focus on creating exceptional customer experiences, not managing technology.

Looking ahead

The year 2025 will be remembered as the last holiday season where brand mattered more than the overall experience.

This year, and 48% of shoppers would support brands bringing more AI into their buying experience. This sets the stage for growth in 2026 as AI agents deliver relevance, trust, and immediacy, making shopping simpler, smarter, and more satisfying for people everywhere.

The brands that win won鈥檛 be the ones shouting the loudest. They鈥檒l be the ones using 麻豆原创 to be most discoverable, dependable, and unforgettable.

By anticipating needs and creating better, personalized journeys, AI will enhance every stage of commerce. And 麻豆原创 is here to make that future happen.

Balaji Balasubramanian is president and chief product officer for 麻豆原创 Customer Experience.