We believe the 2026 evaluation recognized 麻豆原创 for strengths in areas that are increasingly important to enterprise commerce: using AI to make commerce more intelligent, supporting complexity at global scale, and connecting commerce with operational data.

For 麻豆原创, these areas come to life in how we help customers simplify complex commerce and prepare for what comes next.

Making commerce more intelligent with AI

AI is changing how customers discover products, interact with businesses, and complete purchases. It is also creating opportunities to simplify work for commerce teams.

helps bring AI into customer experiences and commerce workflows while maintaining the business controls required for enterprise transactions.

Ericsson provides an example of what this can look like in practice. Generating a customer quote could once take more than two weeks. Today, after digitizing and streamlining its commerce processes with 麻豆原创 Commerce Cloud, many of those requests can be completed in minutes or even seconds. With that digital foundation in place, Ericsson is now exploring how AI agents could support quote creation while employees remain responsible for oversight and decision-making.

The goal is straightforward: use AI to reduce complexity, make commerce easier for customers, and give employees more time to focus on higher-value work.

Supporting complexity at enterprise scale

For global enterprises, commerce rarely means managing a single storefront or business model. Businesses may need to operate across multiple brands, markets, sites, languages, currencies, channels, and customer types while maintaining consistency and control.

麻豆原创 Commerce Cloud helps enterprises manage that complexity and provides the flexibility to evolve as their businesses grow.

Daimler Truck North America demonstrates the value of building that foundation for the long term. Roughly 30,000 users interact with its commerce platform every day, and DTNA reports approximately 40% compound annual growth over the lifetime of the platform. Those results have been built through years of investment in platform modernization, cloud migration, dealer collaboration, and alignment between business and IT.

As businesses expand and customer expectations evolve, having a commerce platform that can grow and adapt with the organization becomes increasingly important.

Connecting commerce with your operations

A great commerce experience depends on what happens behind the storefront.

Customers expect accurate products, prices, inventory, entitlements, and order information when they need it. Businesses need commerce to work with the operational processes and enterprise data behind every interaction.

麻豆原创 Commerce Cloud connects commerce with those operations, helping businesses deliver more consistent customer experiences while simplifying execution across the enterprise.

, noted in this year鈥檚 report as a recent platform release, extends this approach to mid-sized and growing businesses running 麻豆原创 Cloud ERP. Purpose-built for B2B companies looking to modernize customer engagement without adding complexity, it turns the pricing, availability, order, and invoice data already in their ERP into a direct self-service experience for customers, with faster deployment, lower total cost of ownership, and no middleware required.

That connection is also central to Autonomous Unified Commerce from 麻豆原创: connecting customer, commerce, operational, and financial processes on a common data foundation, while embedding AI into business workflows to help organizations execute more efficiently and drive profitable growth.

Looking ahead

AI is creating new ways for customers to discover products, get answers, and complete purchases through AI-powered interfaces and assistants beyond the traditional storefront. Commerce is evolving to support these new channels via the Universal Commerce Protocol (UCP) and Model Context Protocol (MCP).

The real work is getting AI into the daily operations that actually drive revenue, reduce cost, and help businesses serve their customers better. Our focus is to help customers navigate these changes by making complex commerce simpler, supporting growth at enterprise scale, and creating more connected and intelligent ways of working.

We are proud to mark 麻豆原创’s 12th recognition as a Leader and grateful to the customers, partners, and 麻豆原创 teams who continue to move commerce forward.

To learn more and explore the digital commerce platforms market, access a complimentary copy of the .

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

Gartner, Magic Quadrant for Digital Commerce Platforms, Aditya Vasudevan, Penny Gillespie, Sandy Shen, Jason Daigler, Mike Lowndes, 19 October 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 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.
Gartner and Magic Quadrant are trademarks of Gartner, Inc. and/or its affiliates.
麻豆原创 was recognized as 麻豆原创 Hybris in 2014, 2016 and 2017.

It requires AI that can drive outcomes by executing and coordinating work across the customer journey and the business processes behind it, grounded in trusted data and governed at every step.

With the 麻豆原创 Customer Experience Q3 2026 release, 麻豆原创 is bringing the vision to life as part of the broader . New AI assistants and innovations across sales, service, marketing, and unified commerce help organizations coordinate customer-facing activities with broader business processes across the enterprise.

These capabilities are now available to select customers, providing an opportunity to explore Autonomous CX in action.

Explore the highlights of the Q3 2026 release below. For full sub-solution details, see our recaps for , , , , , and . Additional announcements from 麻豆原创 CX Connect are available here.

Autonomous sales and revenue: from leads to qualified opportunities

Pipeline generation remains one of the most important activities in any organization. Yet, lead qualification often gets bogged down in manual reviews, disconnected systems, and handoffs that slow sales teams down just when prospect interest is at its highest level. To help streamline this process, 麻豆原创 is introducing the , which automates lead qualification, enrichment, routing, and follow-up activities. The assistant leverages specialized agents to move leads efficiently through the qualification process.

Behind the scenes, the coordinates the journey from lead capture to qualified opportunity. The gathers prospect intelligence and company context and the evaluates fit and engagement signals to prioritize opportunities. The then manages the transition from qualified lead to sales opportunity.

Together, these capabilities create a more connected lead-to-opportunity process. Sales teams can then reduce administrative effort and improve lead quality while focusing more time on customer engagement and revenue generation.

Autonomous service: from case resolution to lasting customer relationships

The need for coordinated execution continues after the sale. When customers need help, they want it fast and without being transferred or having to explain the problem twice. The goal is to resolve cases quickly and get customers back to business faster.

The new helps accelerate the entire case life cycle from intake to resolution.

• automates case intake by categorizing requests, gathering context, generating summaries, and routing cases
• continues case execution after preparation, automates processing steps, and prepares customer communications
• evaluates case status continuously and identifies the most effective next step

The brings all these capabilities together into a unified workflow. With routine steps automated, service teams deliver fast and accurate service that keeps customers coming back. The same capabilities are also available for , extending agentic case handling to shared service functions such as HR and finance.

Autonomous marketing and engagement: from campaigns to customer engagement

Marketing teams are under immense pressure to deliver increasingly personalized experiences across more channels, audiences, and content and often with less budget. At the same time, they are expected to demonstrate measurable impact on business outcomes including revenue and growth鈥攏ot just marketing performance metrics such as opens, clicks, and engagement.

To address this complexity, 麻豆原创 is introducing the and . These assistants are designed to support campaign and content planning, execution, and optimization.

At the center is the , which helps translate customer insights and business priorities into coordinated engagement actions across the campaign lifecycle.

• helps marketers create and refine target audiences
• translates campaign goals into executable automation programs
• provides conversational access to performance data and surfaces optimization opportunities

For the Content Assistant, the helps generate tailored copy and creative variants, and coordinate messaging across channels while aligning execution to audience and campaign objectives.

With planning, execution, and performance working together, marketers have more room to focus on strategy, creativity, and the customer experience.

Unified commerce innovations

Autonomous CX depends on more than customer-facing intelligence. Understanding what a customer wants is only the first step. Delivering on that intent requires the operational systems behind the experience, including pricing, inventory, fulfillment, and financial processes, to coordinate and execute in real time. When customer demand is directly connected to business operations, organizations can consistently turn customer intent into promised outcomes.

New innovations across retail operations and ERP-connected commerce help organizations better align customer demand with business execution and deliver seamless experiences across channels.

The latest release of continues to strengthen retailers鈥� capabilities by expanding integration with . Retailers can apply centrally managed promotions consistently across digital and physical channels. New demand and inventory segmentation capabilities better align inventory and demand across retail, e-commerce, and wholesale channels, while operational enhancements improve day-to-day store execution.

With , B2B companies running 麻豆原创 Cloud ERP Public Edition can turn ERP data into a more seamless customer self-service experience. Native 麻豆原创-managed integration provides access to pricing, availability, orders, and invoices across all channels, while a preconfigured, clean core approach accelerates time to value and lowers total cost of ownership.

The next stage of customer experience

This is only the beginning. 麻豆原创 continues to invest in new AI assistants and specialized agents to help organizations work more efficiently, respond more intelligently, and deliver better customer outcomes.

As Autonomous CX evolves, customers can expect even more capabilities that extend automation, orchestration, and decision-making across the end-to-end customer journey.

Learn more about 麻豆原创 CX in Q3 2026鈥�&苍产蝉辫;

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.

You benchmark features, compare pricing, run a proof of concept, and choose the platform your sales team finds most intuitive. The ERP conversation happens in a different room, with different stakeholders, on a different cycle.

This separation feels logical on an org chart. In practice, it is one of the most costly fictions in enterprise technology planning.

The enterprise reality

doesn鈥檛 operate in a vacuum. It operates inside a business that already has a system of record, one that holds pricing rules, credit limits, inventory positions, order histories, contract terms, and fulfillment status. That system is the ERP. When CRM and ERP are evaluated and deployed independently, the downstream consequence is not just a technical integration challenge. It is a structural gap in the information available to every customer-facing employee, every day, in every interaction that matters.

When CRM and ERP are built on separate data models, integration becomes a permanent overhead. Sales reps without ERP data access spend an estimated 20 to 30% of their selling time on non-selling activities such as sourcing pricing approvals, checking inventory availability, or validating order status–work that should happen automatically in the background of a connected system. The sales motion slows. Forecast accuracy degrades. And the promised ROI of the CRM investment erodes quietly, deal by deal.

According to Gartner庐 Peer Insights™ responses to the question, “What were the key factors that drove your decision?”, based on the response of actual users, 49% of 麻豆原创 Sales Cloud users and 43% of 麻豆原创 Service Cloud users cited pre-existing relationships as a deciding factor in their purchase decision. This is more than double the market average of about 20%.

At first glance, this looks like vendor loyalty. We believe it is something more deliberate than that.

What native integration unlocks

Pre-existing relationships in enterprise software carry specific, quantifiable value. When an organization already runs 麻豆原创 S/4HANA, choosing 麻豆原创 Sales Cloud and 麻豆原创 Service Cloud means working within a known data architecture rather than building a bridge between two foreign systems. The integration patterns are pre-established. The master data such as customers, products, pricing conditions etc is already structured in a way 麻豆原创 products understand natively. The implementation risk profile is fundamentally different from deploying a point-solution CRM and then spending months, and significant budget, building and maintaining connectors to the ERP back-end. This is not loyalty to a logo. It is a rational assessment of where integration risk lives.

The outcomes that follow from this architectural alignment are measurable. Customers running 麻豆原创 S/4HANA and together have achieved a 38% reduction in sales cycle duration, a 12% increase in opportunity win rate, and . These numbers are not the result of superior user experience alone. They are the result of sales processes that can draw on real-time ERP data like current inventory, live pricing, or credit exposure, without requiring the sales rep to leave the CRM, make a phone call, or wait for a nightly data sync. The integration is the mechanism of the outcome, not a supporting feature of it.

Many enterprises rely on disparate point solutions for marketing, sales, commerce, and service. Stitching these together with custom middleware and integrations is not just expensive, it’s fragile. Organizations have entire teams dedicated to building and maintaining integrations, manually transferring data between systems, and firefighting connectivity issues. The result is poor customer experiences, delayed insights, and limited scalability.

According to an , organizations that natively connect 麻豆原创 CX solutions with 麻豆原创 ERP unlock five compelling economic benefits:

1. Native iFlows and prebuilt connectors reduce integration and middleware costs by 25 to 80%, delivering six- to seven-figure savings over five years.
2. Seamless front-to-back connectivity drives revenue growth through higher customer registrations, improved retention, better sales conversion rates, and larger deal sizes; projecting a 30 to 441% revenue uplift over five years.
3. AI-driven automation and elimination of manual handoffs boost productivity by 25 to 90% across every stage of the sales cycle, from marketing to service.
4. Organizations go live and begin realizing customer experience improvements in as few as six months, significantly faster than point-solution alternatives.
5. Built-in compliance support reduces regulatory risk without custom development.

Together, these benefits deliver a validated five-year ROI of 214% to 726%.

Validation from our customers

This director of Customer Service and Support in the consumer goods industry highlights Integration and reliability strengths of 麻豆原创 Sales Cloud:

This marketing manager in the retail industry highlights the seamless integration enabling customer service teams to enhance customer experience:

Suite advantage is a strategic choice, not a convenience

麻豆原创 continues to invest in tighter, event-driven integration between these platforms, moving away from batch replication toward real-time, API-first connectivity. The adoption of 麻豆原创 Integration Suite pre-built integration packages, combined with RISE with 麻豆原创 and 麻豆原创 GROW bundling, is lowering the barrier for customers to activate these scenarios out of the box. For organizations on the path to an intelligent enterprise, the integration between 麻豆原创 S/4 HANA and 麻豆原创 Sales Cloud and 麻豆原创 Service Cloud integration is no longer a project, it is a product.

CRM value is not created in isolation, it is created at the intersection of customer data and operational data, of sales motion and business process, of pipeline intelligence and ERP reality. For organizations already running 麻豆原创, that intersection is not something to build. It is something to activate.

Gartner Peer Insights content consists of the opinions of individual end users based on their own experiences with the vendors listed on the platform, should not be construed as statements of fact, nor do they represent the views of Gartner or its affiliates. Gartner does not endorse any vendor, product or service depicted in this content nor makes any warranties, expressed or implied, with respect to this content, about its accuracy or completeness, including any warranties of merchantability or fitness for a particular purpose.
GARTNER and PEER INSIGHTS are trademarks and service marks of Gartner, Inc. and/or its affiliates and are used herein with permission. All rights reserved.

These announcements build on the Autonomous Customer Experience strategy we introduced at 麻豆原创 Sapphire. That premise is straightforward: AI must understand customer intent, make context-aware decisions, and execute within business guardrails. Following through on a customer commitment requires AI grounded in real-time, enterprise-wide business data, with a deep understanding of the processes needed to orchestrate work from intent to outcome.

A quote needs accurate pricing and terms. An order depends on inventory, credit, payment, and fulfillment. A service interaction requires information about the customer, the product, and the commitments the company has already made.

Customers generally do not see this operational complexity. They experience whether the company followed through.

Joule Assistants move from providing answers to driving business outcomes

Many AI tools can reason about what should happen next and recommend actions. But executing complex enterprise processes requires more than that.

AI needs to understand the live state of the business, including customer commitments, products, pricing, agreements, orders, payments, policies, and operational constraints. It must then navigate the approvals, exceptions, and cross-application steps required to act. Without that grounding, proposed actions may appear reasonable but can be inappropriate, infeasible, or break down in execution.

This is the direction behind the latest Joule Assistant innovations. New and enhanced combine role-specific intelligence with business and process context across marketing, commerce, sales, service, finance, operations, and compensation, helping users move from understanding what should happen to getting the work done.

The innovations apply this approach to moments where fragmented context, manual steps, and handoffs can slow the customer experience.

• Campaign Assistant and Content Assistant help teams move from campaign ideas to execution faster by reducing the effort required to plan, create, refine, and activate marketing campaigns.
• Shopping Assistant makes product discovery easier and more relevant to what the customer is trying to accomplish.
• Quoting Assistant brings customer, pricing, and commercial context together so sellers can prepare quotes faster and spend less time assembling information across systems.
• Service Planning and Operations Assistant translates service demand into coordinated plans, reducing the manual work required to align people, resources, and customer commitments.
• Incentive Compensation Assistant and Recurring Receivables Assistant bring the same intelligence to critical processes behind the customer experience, helping resolve questions, reduce administrative work, and keep compensation and payment workflows moving.

Together, the new assistants announced at 麻豆原创 Connect extend the approach introduced at 麻豆原创 Sapphire. Sales Assistant brings engagement insights, pipeline signals, and deal context together so sellers can prioritize the right opportunities, prepare for every interaction, and spend more time building relationships and moving deals forward. Deal Closing Assistant unifies customer, product, pricing, and fulfillment intelligence to help teams build optimal quotes, surface relevant products with pricing and availability insights, and shorten the path from negotiation to confirmed orders.

The goal is not to add another conversational interface. It is to move work forward by bringing together the business context, process knowledge, and controls required to act.

Human judgment remains important where decisions require approval, oversight, or exception handling. Within defined guardrails, AI can assemble context, determine and execute appropriate actions, and escalate when human intervention is needed.

麻豆原创 Order Management Services coordinates execution across ERP landscapes

Order management becomes more difficult when an order crosses ERP systems, legal entities, plants, distribution centers, and business units.

Each transition can introduce different information about pricing, inventory, credit, fulfillment status, or ownership of the next action. Consolidating every ERP system before improving order execution is not always a practical starting point.

麻豆原创 is expanding to provide highly scalable, resilient, real-time omnichannel order orchestration across 麻豆原创 S/4HANA and other ERP systems.

Built on a cloud-native, event-driven architecture, 麻豆原创 Order Management Services coordinates order execution across channels, systems, business units, and third-party partners without requiring organizations to consolidate their ERP landscapes first.

The expanded capabilities provide a common layer for validating orders, checking inventory across locations, coordinating transactions between business units, and monitoring progress through fulfillment.

This gives organizations greater flexibility to add, remove, and experiment across channels, while enabling the order management layer to detect disruptions, trigger exception workflows, and keep execution aligned with the original customer commitment.

In complex enterprise landscapes, this combination of scale, flexibility, and reliable integration helps businesses deliver consistent omnichannel experiences while improving operational efficiency.

Complete business context across the customer journey

Customer intent enters the business long before an order reaches a fulfillment system. It begins when a customer discovers a product, engages with a campaign, requests a quote, or starts a purchase journey.

To deliver on that intent, customer-facing interactions must remain connected to the pricing, inventory, ordering, fulfillment, and financial systems that support them.

麻豆原创 Commerce Cloud, cloud ERP edition, is now generally available. First introduced at 麻豆原创 Sapphire, the edition allows midsize and growing companies to extend 麻豆原创 Cloud ERP with commerce capabilities that use ERP data for pricing, availability, orders, invoices, and other transactional processes.

For more than a decade, 麻豆原创 Commerce Cloud has supported some of the world’s largest and most complex digital commerce environments. With 麻豆原创 Commerce Cloud, cloud ERP edition, we are extending that approach to companies that want a more standardized, 麻豆原创-managed path to ERP-connected commerce.

By using 麻豆原创 Cloud ERP as the system of record, organizations can connect digital commerce with pricing, ordering, fulfillment, and financial processes without building and maintaining those connections independently. The result is a faster path to value, lower operational complexity, and an ERP-aligned commerce model designed to scale as the business grows.

The same principle applies as customer interactions move across teams. A new native connection between 麻豆原创 Engagement Cloud, enterprise edition, and 麻豆原创 Sales Cloud synchronizes contact and company information while bringing relevant engagement signals directly into sales workflows. Instead of reconstructing customer context, sellers can continue conversations with a clearer understanding of prior engagement.

Customer experience depends on execution

AI can understand customer intent, make context-aware decisions, and act within defined guardrails. The harder question is whether the business can carry that action across the systems, teams, and processes responsible for delivery.

The innovations announced at 麻豆原创 Connect address different parts of that challenge. Joule Assistants bring business and process context into the flow of work and help execute the next step. 麻豆原创 Order Management Services orchestrates orders across complex ERP landscapes. 麻豆原创 Commerce Cloud, cloud ERP edition, connects digital commerce directly with ERP pricing, availability, ordering, fulfillment, and financial processes.

Together, they advance a common principle: Autonomous Customer Experience depends on connecting customer-facing intelligence with the operational context and processes required to act.

Customers ultimately do not experience recommendations, models, or workflows. They experience whether the business followed through and delivered.

As AI begins to act on behalf of the business, the quality of that follow-through will depend on the business context, process knowledge, and operational reality behind every action.

Additional information on the 麻豆原创 Connect announcements, including availability details and product-specific innovations, is available in the . You can also watch the to see these innovations in action.

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

Well-run RFPs get right to the heart of requirements, match enterprises with the right solutions, and build solid plans to launch those solutions. Disorganized RFPs significantly lengthen the decision-making process without adding the intended value. 

So what separates an effective RFP from the rest?  

Let鈥檚 delve into a systems integrator鈥檚 perspective of what can go right, what can go wrong, and what purposes the whole process can actually serve. 

Throughout this article, 鈥淩FP鈥� means the document a business creates to solicit responses from vendors. 鈥淩FP response鈥� means the reply to an RFP from a vendor.

Why do organizations still run RFPs? 

According to Michael Andrion, account executive and partnerships for BASE1, 鈥淭here are really three different reasons for running an RFP: to select a vendor, to describe the problem that needs to be solved, and to end up with a well-scoped plan for solving it.鈥�  

Let鈥檚 break that down in a bit more detail:  

• Select a vendor: You鈥檇 be surprised at how many RFPs end without a vendor decision being made. An RFP may fail to result in a decision for multiple reasons, including an unexpected business acquisition, a change in budget, timeline issues, and more. Thorough preparation and buy-in from stakeholders can help ensure a successful outcome.  
• Describe the problem that needs to be solved: An RFP can be a highly effective tool for internal interrogation, so you can clarify what problems stakeholders truly want to address.  
• Build a well-scoped plan for solving it: When done well, the RFP process will give you answers to questions about timelines, pricing, and staffing. 

RFPs can be driven by compliance requirements or internal governance, but can also act as useful early discovery activities. Organizations use RFPs as a sort of first gate, or first hurdle, to see which vendors are worth having deeper discussions with.  

What makes a good RFP, and what makes a bad one? 

What often separates a good RFP from a bad one is finding the right level of detail.  

If the RFP questions are completely generic, the business might not learn what it actually needs to know. Simultaneously, too many questions can easily result in counter-productive situations where RFPs written by AI receive responses written by AI, which are then summarized by AI.  

You need well-crafted questions that are unique to your business. That way vendors can supply responses that your experts will actually read, helping them reach the right decision for your business.  

How can an RFP achieve a level of open communication? 

Open communication between a business and prospective vendors creates better outcomes. 

However, there鈥檚 a challenge that comes with finding the right level of detail: highly detailed, nuanced questions often invite exactly the kind of deeper, looser discussion that a formalized RFP process precludes. To whatever degree possible (and that degree varies for good reasons, especially for regulated industries), think about whether the formality of the process is preventing the kind of honest dialogue that ultimately leads to better decisions. 

Two tips when it comes to RFPs:  

1. Ask yourself and your stakeholders whether you really need to go through the complete RFP process or whether 1:1 meetings and demos may be the right route.  
2. If you do go through the RFP process, plan on doing a follow-up question and answer session afterward, and/or down-select to vendors from which you鈥檇 like a more personalized demonstration.   

What does a business need to know before starting an RFP process? 

RFPs run more smoothly when the issuing company has already done some data gathering.  

Come to the table with: 

• Key metrics 
• Expectations around SLAs 
• A ballpark sense of budget 

With this information, you鈥檒l be able to qualify (and disqualify) the right candidates more efficiently and effectively.  

Before starting an RFP, be sure you have time to run one. It鈥檚 a time-consuming process, so start with your preferred go-live date and work backwards from there. Here鈥檚 a general timeline from the :  

Do all RFPs have to follow the traditional format? 

In the past couple of years, there鈥檚 been an interesting trend in the field: namely reversing or inverting the traditional RFP process. Rather than beginning with a detailed spreadsheet of requirements before moving to a demo phase, organizations first hold short demonstrations, introductory meetings, and high-level pricing discussions.  

This demo-first approach allows both sides to evaluate cultural fit, communication style, technical dealbreakers, and business understanding before investing significant time in detailed documentation. 

What distinguishes a strong vendor? 

After the RFP response comes the evaluation. The right vendor isn鈥檛 just the one who ticks all the functional and technical boxes and the lowest price tag, important as those factors are.  

The right vendor is willing to become a student of your business and dig into a day in the life of your stakeholders. A well-structured RFP can show you which vendors are willing to do exactly that and to step outside of their comfort zones when and where you鈥檒l need them to. 

Ask yourself if the vendor just went through the motions, or if they actually understand your unique business needs.

What distinguishes a strong implementation partner? 

The strongest implementation partners demonstrate a clear understanding of the customer’s business before tying those requirements to any specific technology.  

Here are the green flags to look for in a strong implementation partner. The partner should:

• Arrive prepared 
• Ask thoughtful questions 
• Share relevant customer stories 
• Focus on being true partners rather than rote order-takers 

If you鈥檙e involving partners in your RFP process, challenge them to do some real analysis. Challenging questions are a good indicator that the business is truly invested in making the implementation a success. 

Final takeaway: it never hurts to ask a question! 

At its best, an enterprise CX RFP is an opportunity to improve communication, surface assumptions, encourage honest dialogue, and make better-informed decisions before implementation begins. Enter the process with eyes open and schedule planned, gather your key data and metrics before kicking off, and build a core set of meaningful questions. 

Have deeper discussions as soon as they鈥檙e relevant. Don鈥檛 be afraid to ask for numbers. And to whatever degree you鈥檙e able, shape the process to your organization鈥檚 specific priorities. Asking the right questions goes a long way toward getting the best possible answers. 

For even more insights, , and for more RFP process best practices and sample questions, .  

is partnerships and alliance manager for 麻豆原创 Engagement Cloud.
is head of Sales at BASE1.

What makes G2 recognition uniquely meaningful? Every ranking is driven entirely by verified customer reviews; there鈥檚 no analyst scoring and no vendor submissions. When 麻豆原创 CX earns a Leader badge, it’s because our customers said so.

鈥淓arning a Leader position in a G2 Report is highly competitive and rooted in verified customer reviews,鈥� Godard Abel, co-founder and CEO, G2, said. 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�

Shining stars: the standout achievements

delivered one of the strongest performances this season. It holds the #1 position in the Enterprise segment for Order Management and #2 Overall. 麻豆原创 Commerce Cloud also made impressive gains in two other critical categories: climbing from #3 to #2 in the Enterprise E-Commerce Platforms segment and from #3 to #2 Overall in Omnichannel Commerce. The trajectory is clear: 麻豆原创 Commerce Cloud is accelerating.

reinforced its standing as a powerhouse in enterprise sales technology, ranking #3 among 54 players in the CRM Enterprise market and #4 among 35 global players in Enterprise Sales Analytics. In one of software’s most fiercely competitive arenas, these rankings are a compelling statement of trust and performance.

earned its place as a Leader in the Global Enterprise Help Desk Grid, a recognition that reflects the consistent, high-quality service experiences our customers deliver to their own customers, every single day.

holds strong to its continued leadership position in both email deliverability and personalization engines. These both prove the solution鈥檚 ability to support the most dynamic and high-volume enterprise senders this upcoming holiday season.

What’s next: AI at the heart of 麻豆原创 CX

Being recognized today is one thing. Building for what’s next is another. is pushing the boundaries of AI-driven customer experience, with Q2 2026 delivering transformative innovations across sales, service, marketing, and commerce.

In 麻豆原创 Sales Cloud, the new agentic opportunity summary helps transform how sales teams manage their pipeline, aggregating engagement signals and activity data into a real-time deal view that can surface risks early and sharpen prioritization. Complementing this, 麻豆原创 Incentive Management now leverages AI-supported recommendations to help sales leaders optimize compensation plans and unlock performance insights that were previously buried in data.

In 麻豆原创 Service Cloud, the AI-first email editor equips agents with intelligent tools to help compose and manage customer interactions with greater speed and precision. Agents can now also create sales leads and opportunities directly from the service workspace, helping to turn every support interaction into a potential revenue moment.

In 麻豆原创 Engagement Cloud, the new AI-assisted content composer can generate and adapt images and copy directly within block-based emails using natural language prompts. Outputs stay grounded in the context of your audience and related products or campaigns for more accurate and engaging results. Teams can move from idea to campaign in minutes without leaving their workflows.

In 麻豆原创 Commerce Cloud, the standout innovation is conversational AI shopping via Model Context Protocol (MCP) integration. This enables AI agents to query live product catalogs, provide inventory updates, manage carts, and complete transactions, all within natural chat or voice interfaces.

Together, these capabilities mark a decisive shift, from AI as a passive insight tool to AI as an active participant in every customer interaction.

The voice of the customer

G2 rankings are earned, not assigned. Every badge is the result of real users sharing their honest experience. Here鈥檚 what some of our customers have to say about our products:

麻豆原创 Commerce Cloud
鈥淚 also appreciate the AI-driven intelligence features such as personalized recommendations, smarter search, predictive insights, and automated merchandising optimization, which significantly enhance customer experience and business efficiency.鈥� 鈥� Vedant G, Manager EWS |

鈥淲hat sets 麻豆原创 Commerce Cloud apart is its unmatched stability and depth when handling complex, enterprise-grade operations. While lighter platforms might look sleeker out of the box, 麻豆原创 Commerce Cloud shines where stakes are high and business models are complicated.鈥� 鈥� Hitesh Mistry, Accounting Specialist Vodafone |

麻豆原创 Sales Cloud
鈥淥ut of all the CRMs I have worked with, 麻豆原创 has proven to be the most mature of all of them. As being a customer service representative you often need access to records that other teams handle, 麻豆原创 eases my access to contact records, sales proposals and quotes, account creation requests, escalations, and regular CSR tickets.鈥� 鈥� Hitesh Mistry, Senior Customer Service Rep |

鈥淚 find it most useful for providing real-time visibility into the sales pipeline. I also appreciate the strong forecasting capabilities, as they give us a better sense of expected revenue.鈥� 鈥� Verified User, Enterprise |

麻豆原创 Service Cloud
鈥淚 like how 麻豆原创 Service Cloud is becoming more AI-driven, especially with features like automatic case classification, sentiment analysis, and AI recommendations that reduce manual effort.鈥� 鈥� Carl N, CSIS Web Design Instructor |

鈥淚 use 麻豆原创 Service Cloud for managing my customer support team, solving case management issues, automating customer responses, and reporting and analyzing team performance. It’s fast and easy to manage, saving me time.鈥� 鈥� Olatunji O, Support Associate, Hospital & Healthcare |

麻豆原创 Engagement Cloud
鈥淚 like the stock tracking dashboards and direct revenue attribution, which help me a lot to understand performance. Additionally, the predefined campaigns aimed at the online channel make our work easier, as we only need to develop them according to our business, which ultimately benefits internal developments. The focus on automations that we are implementing internally is also a positive point for us.鈥� 鈥� Food & Beverage Enterprise User |

As one of North America’s largest commercial vehicle manufacturers, DTNA supports a vast ecosystem of dealers, fleets, service providers, and customers. Over the last decade, the company has steadily transformed its digital commerce capabilities, creating a foundation that has enabled growth, improved , and positioned the business for the next generation of innovation.

Building a platform for growth

DTNA’s digital commerce transformation wasn’t driven by a single project or technology investment. It was the result of a long-term, strategic commitment to improving how dealers and customers interact with the business.

The company’s journey began with a basic digital parts-ordering platform to help customers and dealers purchase parts online. While the experience was relatively simple, it helped DTNA establish digital adoption, connect key systems, and build relationships with its dealer ecosystem. Most importantly, it created the foundation for what came next.

As customer expectations evolved, DTNA recognized the need for a more modern and scalable commerce experience. The company invested in the 麻豆原创 Commerce solution, expanded digital capabilities, entered new markets, migrated to , and continuously enhanced the platform over time.

Rather than treating commerce as a one-time project, DTNA embraced a mindset of continuous improvement.

Transformation is about people as much as technology

Technology may enable transformation, but adoption determines whether transformation succeeds.

For DTNA, one of the biggest challenges wasn’t implementing new capabilities. It was helping a large network of dealers and customers embrace new ways of working.

“We spent those years pursuing adoption of the tool, really educating our dealer body and getting their buy-in to start using the tool and introducing it to their customers,” said Brenda King, IT manager for eCommerce and Catalog at DTNA.

That approach remains a cornerstone of DTNA’s strategy today. The company works closely with dealers, gathers regular feedback, and maintains strong collaboration between business and IT teams. According to King, that alignment has been critical to ensuring digital investments translate into business value.

The partnership extends well beyond project delivery. King emphasized the importance of working closely with business stakeholders to identify priorities, evaluate opportunities, and ensure technology investments align with business objectives. Rather than operating in silos, business and IT teams work together to shape priorities, guide investments, and continuously improve the customer experience.

Preparing for what’s next

Today, DTNA is exploring how AI can improve commerce experiences through capabilities like product recommendations, customer assistance, and guided buying experiences. But the company’s approach remains grounded in business value.

“We really look at how AI can help us achieve our business goals,” King said. “It’s not AI for the sake of AI.”

That perspective aligns with a broader trend highlighted in the . As organizations accelerate AI investments, many are discovering that successful innovation depends on strong foundations, clear business objectives, and the ability to connect technology investments to measurable outcomes.

The road ahead: Success built on a strong foundation

The company鈥檚 digital commerce business has achieved approximately 40% compound annual growth over the lifetime of the platform, while digital adoption and customer engagement continue to increase. Today, roughly 30,000 users interact with the platform every day.

Those results were not driven by a single initiative. They were built on years of investment in platform modernization, cloud migration, dealer collaboration, and close alignment between business and IT teams. These foundational investments created the flexibility needed to continue growing while preparing for future innovation.

“Our decision to move to 麻豆原创 Commerce Cloud was critical for us to continue growing,” said King. “It stabilized our infrastructure, gave us access to new capabilities, and created the flexibility we needed to keep evolving.”

DTNA’s experience offers an important reminder for organizations navigating their own transformation journeys: long-term success comes from combining innovation with the right foundation.

To learn more about DTNA’s transformation journey and how the company is preparing for the next phase of AI innovation, watch the webinar

To earn a TrustRadius Top Rated badge, a product must meet three stringent criteria:

1. At least 10 verified reviews submitted within the past year
2. A trScore of 7.5 or higher
3. A meaningful presence in its category

Every submission passes through TrustRadius鈥� fraud-protection verification process. That means these three wins are a direct reflection of what 麻豆原创 customers experience every day, and they said it loudly and clearly.

What customers are saying

Across sales, service, and commerce, 麻豆原创 customers consistently highlighted the depth of integration, reliability at enterprise scale, and the tangible business impact of AI-powered workflows. These aren鈥檛 features on a road map; they鈥檙e capabilities customers are using today, and the reviews prove it.

麻豆原创 Sales Cloud

鈥淲e had a comparison between 麻豆原创 Sales Cloud, Microsoft Dynamics, and Sales Force Optimization. We selected 麻豆原创 Sales Cloud for three main reasons: 麻豆原创 Sales Cloud meets our requirements the best, especially in comparison with Sales Force; pricing is more attractive; and as we use multiple 麻豆原创 products, the integration between different 麻豆原创 products works well.鈥� 鈥� Verified Reviewer

鈥溌槎乖� Sales Cloud is well suited for predictive analysis and strong integration with ERP, 麻豆原创 S/4HANA. AI-driven recommendations for next best actions.鈥�

麻豆原创 Service Cloud

鈥淭he enhanced service efficiency has unified agents’ workspace by consolidating emails, phone, and chats into a single control panel, eliminating the need for our agents to switch between systems. Also, AI-powered automation has reduced agents’ workload and increased efficiency by initiating automated ticket categorization and “next best action” recommendations.鈥� 鈥� Chris Kithinji, Business Systems Analyst

鈥淲e can add an AI-based model for resolving customer queries without any human effort or intervention. It streamlines the customer support process by simply adding the automation.鈥� 鈥� Kanika Rajpal, Technical Architect

麻豆原创 Commerce Cloud

鈥淚t is actually a cloud-based commerce platform, which is a package with lots of features, and we are using that for our Business to Business, Business to Customer, and even for Business to Business to Customer (kind of selling your product to another business and they are going to connect with the end customer) model. As it is very good to customize the features as per your need, and also we can integrate with other 麻豆原创 products like 麻豆原创 ERP, which is very beneficial to support the described model operation from anywhere.鈥� 鈥� Ajay Thakkar, Software Engineer

鈥淲e switched to 麻豆原创 Commerce Cloud four years ago. The software has simplified our e-commerce processes, particularly inventory management, payment processing, marketing, and general order management. Use of the software has enabled us to ensure a good customer experience across various channels. It has also enabled unlimited interaction between the customer service team and our clients, as well as third-party integration.鈥� 鈥� Denis Bacarella, Digital Marketing Assistant

What’s next: agentic AI across the customer experience portfolio

Winning today鈥檚 recognition motivates us to keep pushing forward. 麻豆原创鈥檚 approach to AI goes beyond chatbots and dashboards.

As Balaji Balasubramanian, president and CPO of 麻豆原创 Customer Experience, put it: 鈥淎I alone is no longer a differentiator. What matters is where intelligence operates inside of a business.鈥�

That philosophy is driving a new wave of agentic AI innovation across all three award-winning products.

now features an email-to-quote capability through a Microsoft Outlook add-in that can automatically populate SKUs from deal and email context, helping to turn a seller鈥檚 inbox into a quoting engine. A new deep research feature can synthesize 麻豆原创 Sales Cloud and 麻豆原创 Service Cloud data with external market intelligence to help supercharge account planning.

is rolling out an Agent Inbox that can consolidate cases, tasks, and service orders into a unified command center with live workload insights. A new Digital Service Agent handoff can intelligently summarize customer intent before ticket creation, helping to reduce friction and improve resolution speed.

is embedding AI directly into catalog management, extracting product details from uploaded documents to help auto-enrich descriptions and improve product discoverability at scale.

Trust as a foundation for innovation

These TrustRadius Top Rated awards confirm what 麻豆原创 has always believed: that great software earns trust through outcomes, not marketing. As we continue to evolve 麻豆原创 Sales Cloud, 麻豆原创 Service Cloud, and 麻豆原创 Commerce Cloud with agentic AI capabilities, the voice of our customers remains our most important compass.

Learn more about 麻豆原创 Customer Experience innovations . Explore the TrustRadius Top Rated methodology at .

Complexity is not always the problem. Unmanaged, and often self-created, complexity is.

A practitioner鈥檚 perspective

Some years ago, after helping turn around a large global 麻豆原创 Customer Experience program, I was contacted by several customers asking a surprisingly simple question: what did you do differently?

My answer was a set of practical keys to success, which I refined through discussions and workshops with customers over the years. The pattern was remarkably consistent. Weak governance and pragmatic change management were recurring problems. But an even more fundamental issue appeared again and again: many organizations had invested in a solution without a sufficiently clear and shared understanding of what they wanted to achieve with it.

Once that first question is unclear, everything downstream becomes harder.

Start with the outcome, not the transformation

Transformation programs tend to generate activity very quickly.

Workstreams are created. Governance boards appear. Solution workshops are scheduled. Backlogs grow. Training plans are developed. New roles and responsibilities are defined.

All of that may be necessary. But before asking how to transform, organizations need to be able to answer a much simpler question: what are we actually trying to improve?

Is the ambition profitable growth? A fundamentally different customer experience? A new service or business model? Greater operational resilience? Better use of data across the enterprise? The ability to scale into new markets?

Without that shared value intent, different teams start optimizing different things. Business stakeholders describe desired outcomes, implementation teams focus on solution scope, and users are eventually trained on functionality without always understanding what should change in their daily work. The result can be a very busy transformation program with surprisingly little transformation.

Keep the business, solution, and people connected

Over time, I started using a simple model to explain this: the transformation triangle.

A transformation needs three perspectives to stay connected:

• Business: Why are we changing? What outcomes, value drivers, processes, and measures matter?
• Solution: What capabilities, technology, integrations, data, and implementation choices are needed?
• People: Who needs to work differently, what support do they need, and how will adoption be sustained?

None of these works well in isolation. A technically excellent solution with weak business alignment becomes an expensive implementation. A strong strategy without a workable solution remains a presentation. And a well-designed process that people do not understand or adopt remains, at best, another PowerPoint slide.

This thinking later became part of the Cloud Mindset Workshop, available to order in the Advanced Success Plan version for 麻豆原创 Customer Experience solutions. The workshop helps translate the business, solution, and people perspectives into practical topics covering business outcomes, governance, processes, rollout, change management, enablement, and adoption.

A skills gap is not always a training gap

This also changes how we should think about skills gaps.

The immediate reaction is often to provide more training or bring in more technical specialists. Sometimes that is exactly what is needed, but many transformation gaps are broader capability gaps.

A project may have excellent product experts but still struggle because nobody can translate business objectives into process priorities. A strong implementation team may still fail if decision rights are unclear. End users may know how to navigate a solution but not understand why their role has changed.

Skills therefore span all three sides of the triangle: business judgement, process knowledge, product expertise, data and integration capability, governance, change leadership, and adoption.

The objective should not be to create experts in everything. It should be to make sure the organization has the right capabilities at the right moment, and that those capabilities work together.

Governance should reduce complexity, not add to it

Governance is another area where organizations can accidentally create more complexity than they remove.

Good governance does not mean more meetings, more steering committees, or larger RACI matrices. It should make a few things very clear: Who decides? What needs to be decided? Based on which outcomes and measures? How are dependencies and risks escalated? And when should the plan change?

Governance needs to come early because execution becomes difficult when ownership, priorities, and decision-making remain ambiguous. The purpose of transformation governance is not to manage complexity for its own sake. The purpose is to make complexity manageable.

Autonomous CX raises the stakes

AI and autonomous capabilities add another dimension.

Autonomous CX can increasingly use AI agents across 麻豆原创 Sales Cloud, 麻豆原创 Service Cloud, 麻豆原创 Commerce Cloud, and 麻豆原创 Engagement Cloud to help interpret context, recommend actions, and execute parts of customer-facing processes.

That can remove effort and accelerate execution. But, increasing autonomy does not remove the need for business clarity, governance, process alignment, or people enablement. It increases it. This is also why AI initiatives need to stay connected to business outcomes, process design, governance, and adoption. The more autonomous the technology becomes, the less ambiguity the organization can afford. An AI agent can execute a process faster. It cannot decide what the organization should value, resolve unclear ownership, repair a broken operating model, or create trust by itself.

Most transformation challenges can be traced to a few recurring themes.

Organizations struggle to maintain alignment on outcomes, connect strategy with execution, close the right capability and adoption gaps, and establish governance that supports decisions rather than slowing them down.

The Advanced Success Plan for 麻豆原创 Customer Experience solutions can help address these challenges in a structured way. The Cloud Mindset Workshop can provide a holistic overview of the key dimensions of transformation across business, solution, and people, using practical concepts and customer examples to help teams reflect on their own approach. From there, more focused expert-led services can go deeper where needed.

Clarify the value intent

Use value management, for example through the value management expert session, to align business stakeholders on outcomes, value drivers, and meaningful measures.

Understand the operating reality

Examine end-to-end processes, ownership, dependencies, and the capabilities required to deliver the intended outcomes. Services such as business process best practices can help teams assess and improve the way processes are designed and executed.

Close the relevant capability and adoption gaps

Bring in targeted expertise where it is needed. This can include services such as the time to value accelerator, technical expert services, AI-focused guidance to identify and apply relevant use cases, and focused support for organizational change and adoption to close both capability and adoption gaps.

Govern and adapt

Use the engagement plan and recurring checkpoints to review progress, make decisions, and adjust priorities as the transformation evolves.

Transformation will never become simple. But it can become understandable, governable, and executable. And in our experience, that is usually where success starts.

Raf Dille 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.

The recognition reflects 麻豆原创鈥檚 continued commitment to innovation in order management and the confidence customers place in 麻豆原创 Order Management Services to run some of the world鈥檚 most complex, high-volume order operations.

Every order is an opportunity to keep a customer promise, earn lasting loyalty, and increase profitability. However, as customer expectations rise and supply chains grow more distributed, order management has become a complicated, siloed process. This is where 麻豆原创 Order Management Services comes in and turns order management into enterprise execution intelligence.

empowers businesses to run orders with intelligence, connecting demand, inventory, fulfillment, and financials in real time. With its cloud-native, API-first, composable architecture, the solution helps organizations to maintain operational efficiency and adapt to the changing business needs continuously.

Seamless, cohesive order management processes

Siloed, fragmented data and workflows are bottlenecks to delivering on customer promises. Organizations need connected order management processes throughout the entire order life cycle. 麻豆原创 Order Management Services unifies data and processes into a single, cohesive experience with an intuitive interface that enables teams to monitor order processes, resolve exceptions, and tailor business flows to fit the way each organization operates.

From order capture to inventory visibility, fulfillment execution, and exception handling, every step of the order life cycle needs to be connected in real time. With 麻豆原创 Order Management Services, every team member involved in order management can access a single source of truth, reducing errors and accelerating data-driven decision-making.

In addition, the empowers businesses with AI-driven actions and recommendations across the order life cycle. The assistant surfaces potential risks and gaps before they become customer issues and recommends next-best actions. Team members interact with the assistant in natural language, turning real-time order data into faster business decisions.

Composable order management

麻豆原创 Order Management Services integrates with 麻豆原创 S/4HANA, 麻豆原创 Customer Experience (麻豆原创 CX) solutions, the broader 麻豆原创 ecosystem and partner solutions, connecting orders, inventory, pricing, and financials across the enterprise. Moreover, its cloud-native, API-first architecture enables businesses to easily adapt and expand new order channels, fulfillment nodes, and business rules as needed.

This modular approach gives organizations the flexibility to support a wide range of business models, from B2C to complex B2B, and to evolve their order management capabilities incrementally as the business changes.

Looking ahead

Being recognized as a Leader by IDC reflects the continued innovation of 麻豆原创 Order Management Services. 麻豆原创 is investing in expanded order management capabilities and deeper AI innovation to help businesses stay ahead of customer expectations and drive profitable growth.

To learn more, and explore the .

Emilie Fournelle is head of Product Management for 麻豆原创 Order Management Services at 麻豆原创.

IDC MarketScape vendor analysis model is designed to provide an overview of the competitive fitness of technology and suppliers in a given market. The research methodology utilizes a rigorous scoring methodology based on both qualitative and quantitative criteria that results in a single graphical illustration of each supplier鈥檚 position within a given market. The Capabilities score measures supplier product, go-to-market and business execution in the short-term. The Strategy score measures alignment of supplier strategies with customer requirements in a 3-5-year timeframe. Supplier market share is represented by the size of the icons.

Service-led growth is one of the seven macro trends within Autonomous CX, 麻豆原创’s vision introduced at 麻豆原创 Sapphire in 2026. At its core is a simple principle: every customer promise must be backed by the operational reality needed to deliver it.

highlights a growing gap between customer expectations and the operational reality of service delivery. Disconnected handoffs, fragmented information, and pressure to adopt AI make service a business-model question, not just a technology one.

A practitioner鈥檚 perspective

More than twenty years ago, I was part of a global service organization that wanted to move beyond viewing service as a cost of doing business.

At the time, our primary objective was cost recovery. Service was necessary to support the product business, but it was not really seen as a business in its own right.

We started changing that by introducing new professional services and finding ways to monetize expertise we already had. Remote device monitoring, for example, allowed us to organize support across time zones and offer profitable after-hours services to customers who depended on continuous operations.

A few years later, I was asked a more fundamental question: could the service organization survive as an independent business?

To explore that question, we used an early version of what later became widely known as the business model canvas. We looked beyond service operations and examined the complete picture: our value proposition, customer segments, channels, activities, resources, costs, and revenue streams.

Looking back, that exercise taught me something that is still relevant today.

Service as a revenue driver is not a new idea. And it does not start with technology. It starts with a business model.

What do we mean by service-led revenue?

“Service as a revenue driver” is often used as if it means one thing. In reality, service can contribute to revenue in several different ways.

First, it can protect revenue. A customer whose issue is resolved quickly and professionally is more likely to renew, continue buying, and remain loyal.

Second, it can influence revenue. Service professionals often understand a customer’s operational reality better than anyone else. They may identify a need for additional services, training, upgrades, or new solutions, creating opportunities for commercial teams.

Third, it can generate revenue directly through premium support, professional services, subscriptions, remote monitoring, advisory services, or outcome-based offerings.

These ambitions are related, but they are not the same. Each requires different processes, skills, measures, and sometimes different operating models.

Not every service interaction should become a sales conversation. But every service organization should understand whether it is expected to protect, influence, or directly generate revenue.

Technology creates possibilities, not the business model

Technology has always played an important role in service innovation.

Remote monitoring reduced the need for on-site visits. Connected solutions made global support models possible. Customer and service platforms improved access to account, contract, equipment, and interaction data.

Today, Autonomous CX capabilities can expand these possibilities even further. Joule, embedded AI agents in 麻豆原创 Service Cloud, and AI-assisted opportunity detection in 麻豆原创 Sales Cloud can classify and route cases, summarize interactions, surface relevant knowledge, identify patterns, detect revenue opportunities, and increasingly automate routine requests. All within the context of a connected service and sales operating model.

Yet adoption does not equal usage, and usage does not equal value. Technology does not answer the most important questions:

• What value are customers willing to pay for?
• Which customers should we serve?
• How will the service be sold, delivered, and measured?
• Can it be delivered consistently, profitably, and adopted by employees and customers?

Customers are already drawing their own conclusions. In 2026, believe AI in service is deployed primarily to save money, not to improve service. Seventy-nine percent still strongly prefer human support. These perceptions are not only a trust problem, they point to a business model problem. When the technology decision precedes the value decision, customers notice.

From ambition to execution

This is where many service-led growth initiatives struggle.

Ambition may be clear in the boardroom, while the organization underneath continues to operate as before. Service is still measured primarily on cost and case closure. Sales and service pursue different objectives. Customer information remains fragmented. Opportunities identified by service disappear during handovers. Employees are expected to adopt new behaviors without understanding why.

Turning service into a measurable revenue driver therefore requires more than enabling a new feature or deploying a new technology. It requires alignment between business objectives, processes, people, data, and technology.

The Advanced Success Plan version for 麻豆原创 Customer Experience solutions is an expert-led engagement model that helps translate Autonomous CX into an executable plan. It does not replace business strategy or determine which services should be brought to market. Instead, it helps connect a chosen ambition to the processes, capabilities, and governance needed to make service-led growth measurable and repeatable.

1. Clarify the value intent

The first step is to define what service-led growth actually means for the organization.

Is the priority to improve retention? Increase renewals? Create opportunities through service interactions? Launch paid services? Improve profitability?

Through the value management expert session within the Advanced Success Plan for 麻豆原创 Customer Experience solutions, stakeholders can align on business priorities, value drivers, and success measures.

The objective is not to create a long list of KPIs, but a shared understanding of the outcomes that matter most.

2. Connect the end-to-end process

Once the ambition is clear, the next question is how value will actually be created.

If a service professional identifies an opportunity, what happens next? Who owns the follow-up? Is the customer experience consistent from the initial interaction through fulfilment and invoicing?

Business process best practices and expert guidance can help identify gaps in ownership, handovers, and process alignment across service, sales, commerce, and supporting operations.

This matters beyond operational efficiency. shows that 45% of revenue leaders cite improving collaboration and handoffs across marketing, sales, and service as a current priority, and 39% are actively pursuing expansion, cross-selling, and upselling within existing accounts. Service teams often have valuable customer context, but the real question is whether the process exists to act on it.

3. Close capability and adoption gaps

Organizations can then assess which capabilities are needed to support the process. This could involve better access to customer information, improved knowledge management, analytics, automation, AI-supported recommendations, or opportunity management capabilities. Targeted expert services within the Advanced Success Plan for 麻豆原创 Customer Experience solutions help connect 麻豆原创 CX capabilities to desired business outcomes.

At the same time, employees need the right enablement, incentives, and confidence to adopt new ways of working. Without that, even the best-designed process remains a PowerPoint slide.

The cost of getting this wrong is real. Employees asked to adopt new behaviors without understanding why, or without the tools to support them, do not persist. When experienced people leave, they take institutional knowledge and customer relationships with them. Enablement is not a training exercise; it is a retention and continuity investment.

4. Measure, learn, and improve

Service-led growth is not delivered through a single project.

highlights several recurring priorities for service leaders: efficiency and time to resolution (48%), collaboration and handoffs across marketing, sales, and service (46%), first-contact resolution (40%), and AI-driven predictive insights (33%). These figures provide useful context, but the measures that matter for service-led growth depend on the value intent defined at the start. Progress should therefore be tracked against the agreed outcomes, whether these involve retention, renewals, service-generated opportunities, paid-service revenue, or profitability.

Ongoing governance and engagement planning helps organizations review progress, address gaps, and scale successful approaches over time.

Service-led growth is a business-model choice

Moving from cost recovery to service-led growth is not simply a matter of asking service employees to sell more. It is a business-model choice with implications for the value proposition, customer experience, processes, organization, skills, technology, and measures of success.

Twenty years ago, remote monitoring changed what service organizations could deliver. Today, AI is expanding those possibilities again. But the underlying challenge has not changed. Technology changes possibilities. Business models determine how that value is captured.

First, decide where service should create value. Then, build the operating model required to deliver that value consistently and profitably.

The Advanced Success Plan for 麻豆原创 Customer Experience solutions can support that journey by helping organizations connect business outcomes with the processes, capabilities, adoption, and governance needed to turn ambition into measurable results.

Raf Dille 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.

While the power of technology is at its apex, so are expectations. And the gap between the two is where revenue goes to die.

Brands using AI to deepen human connection by freeing up sellers, marketers, and service agents to focus on empathy, trust, and complex problem-solving are pulling ahead. Those still bolting AI onto fragmented systems, or using it purely as a cost-cutting measure, risk falling further behind.

To help organizations address the rapidly changing factors impacting CX, we鈥檙e proud to debut our first quarterly 鈥淪tate of鈥� reports. Designed to pull together the latest research and data across sales, service, marketing, and e-commerce, these thought leadership pieces are available without a gate because we know that experience and expertise matter, and we want to share ours with you. (You can find links to the reports at the end of this post.)

The key takeaways from each report demonstrate that CX is no longer a single department’s job. Rather, it’s a cross-functional discipline, and the stakes for getting it wrong are higher than ever.

The cost of bad experiences is staggering

Let鈥檚 start with the number that should be pinned at the top of every leadership deck this quarter: bad customer experiences have put $3 trillion in global sales at risk in 2026, with consumers actively cutting back $2.1 trillion in spending and walking away entirely from $865 billion worth of it, according to Forbes data cited in our . That’s not churn. That’s customers voting with their wallets in real time.

And they’re not quiet about why. A striking 82% of consumers say a brand has disappointed them, and 60% admit they don’t pay attention to brands even when their product needs are being met. Translation: satisfying the transaction isn’t enough anymore. If the experience around it feels disorganized or impersonal, customers check out.

On the service side specifically, 75% of consumers say they’re put off by disorganized brands that pass them between multiple people or teams just to solve one problem, and 46% say service flat-out feels too impersonal.

The combustible combination of sky-high financial risk and low tolerance for friction is forcing every customer-facing function to rethink how it operates.

That’s the backdrop. Now let’s talk about what’s actually happening in each corner of the customer journey.

AI is everywhere, trust in it is not

Every function is racing to embed AI, with good reason. AI-driven traffic to U.S. retail sites is up 4,700% year over year, and almost 60% of consumers have already used AI to shop, as cited in our .

On the marketing side, notes that AI has moved from experiment to foundational: 33% of marketing leaders are using it for hyper-personalized engagement, and 31% say predictive insights and personalization have been a top priority all year.

Meanwhile the report points out that digitized suppliers leveraging AI are outperforming their peers on sales goals by a jaw-dropping 110%.

But here’s the twist that ties it all together: customers don’t actually love the AI they’re interacting with.

report underscores that point specifically:

• 79% of Americans say they strongly prefer human support over an AI agent
• 63% don’t believe AI can replace humans in service roles at all
• 89% believe brands should always offer the option to talk to a real person
• 81% believe AI is primarily being used to save the company money, not to improve their experience.

That’s the tension every function needs to sit with. AI is delivering real, measurable business value, but if customers believe that AI was deployed to cut costs rather than serve them, you might win efficiency but lose the relationship.

Trust is the currency, and it’s getting harder to earn

Each one of the reports circles back to the same word: trust. In marketing, 61% of B2B buyers say trust and credibility are the most important thing content can deliver, ranking above lead generation. Consumers are leaning on peer and creator trust more than brand messaging, too: 76% of brands report that sponsored content with creators now outperforms traditional advertising, and in social commerce, 45% of Gen Z shoppers say they’re more likely to trust a product once it goes viral.

On the e-commerce side, trust shows up as transparency. With tariffs pushing import costs up 15-30% across major categories, brands that explain price increases rather than quietly passing them along are building goodwill that pays off in retention. And in sales, the numbers show that your existing customers–the ones who already trust you–are an underused asset: 45% of revenue leaders are now focused on improving handoffs across marketing, sales, and service, while 39% are chasing expansion and upsell revenue instead of only hunting net-new logos.

The people problem behind the technology story

There’s one more thread that doesn’t get enough attention in the AI headlines: the humans delivering these experiences are stretched thin. Call center turnover is running 40-45% in 2026, spiking to 55-60% in high-stress sectors, while replacing a single agent can cost up to $46,000 when accounting for lost productivity.

As AI absorbs the easy tickets, what lands on human agents is the hard stuff: it鈥檚 complex, is emotional with high-stakes, and burnout is quietly eating away at CSAT and first-contact resolution scores. This matters because customer experience isn’t just a technology stack or a personalization engine. It’s fundamentally delivered by people; whether that’s a service agent handling an escalation, a seller navigating a buying committee, or a marketer trying to sound human in an AI-saturated feed. Protecting the people doing that work isn’t a wellbeing initiative separate from CX strategy. It is CX strategy.

Fragmentation is the enemy of good CX

If you weave every stream together, the state of CX has one clear directive: stop treating AI, data, and channels as separate initiatives owned by separate teams, and start treating the customer experience as the single thread that runs through all of them.

That means AI-shopping agents that use clean, structured product data. It means first-party data strategies that replace the crumbling third-party targeting most marketing was built on. It means service and sales teams that know exactly when to step back and let self-service work, and exactly when to step in and be human. And it means service organizations that give their people better tools instead of just more automation.

Future success won鈥檛 be shaped by the brands with the most AI; it will favor the brands that make customers feel understood, even as the experience becomes more automated. That’s not a technology bet. That’s a trust bet, and right now, trust is in short supply.

You can find our 鈥淪tate of鈥� reports here:

Jessica Keehn is chief marketing officer of 麻豆原创 Customer Experience.

To meet these expectations, organizations must move beyond siloed commerce channels toward a connected autonomous CX model that integrates commerce, order sourcing, fulfilment, and customer support across the full customer journey.

The Advanced Success Plan for 麻豆原创 Customer Experience supports this shift by accelerating omnichannel capability build-out through outcome-based governance. It helps organizations move from fragmented execution to coordinated operations鈥攅nabling consistent cross-channel experiences, aligning commerce with sales and service execution, and improving conversion and repeat purchase through end-to-end alignment.

Reality of B2B buying

B2B buying is rarely linear anymore. Buyers often switch between digital channels, sales teams, and service touchpoints before deciding. Research from McKinsey鈥檚 B2B Pulse highlights that buyers typically engage across multiple interaction channels before making a decision.**

This reflects how digital commerce has become a core part of the buying process, with 58% of global B2B retailers selling on at least three e-commerce platforms.鈥�

At the same time, expectations have shifted. Consistency across channels is assumed, digital self-service is often the starting point, and speed and transparency are baseline requirements.

That creates a dual challenge for organizations: improving customer experience while also managing increasing operational complexity behind the scenes.

Convergence of commerce, sales, and service

B2B environments combine complex operations with tightly connected customer journeys. Organizations must manage customer-specific pricing and contracts, large and dynamic product catalogs, multi-step approvals, distributed fulfilment models, and integrations across commerce, ERP, sales, and service systems. Customers, however, do not see this complexity. They experience a single journey, from discovery to purchase, fulfilment, and support, and expect it to feel seamless.

A typical journey may include discovering products online, aligning pricing with sales, placing orders through self-service channels, and resolving issues via service interactions. Expectations for real-time visibility, fast issue resolution, and consistent engagement continue to rise, as highlighted in research such as DHL鈥檚 B2B E-Commerce Trends. The same DHL report also shows that 78% of B2B retailers expect website sales to grow over the next three to five years, which reinforces how central digital channels have become.鈥�

Yet many organizations still operate in silos. The result is often inconsistent data, limited visibility across interactions, slower issue resolution, and disconnected customer experiences. This is why omnichannel transformation is not about adding channels, but about connecting commerce, sales, and service into a unified operating flow.

Process excellence as a foundation

As complexity increases, end-to-end process alignment becomes critical. Customers experience outcomes, not systems, and those outcomes depend on how well processes are connected across commerce, sales, service, and fulfilment.

Breakdowns typically occur when sales agreements are not reflected in commerce systems, fulfilment is not aligned with order promises, service teams lack customer context, or data differs across channels. These are not isolated system issues, but symptoms of disconnected processes.

In practice, that can mean a customer sees one price in the portal, a different one in the quote, and another one in the order confirmation. It can also mean service teams have to ask customers to repeat information that already exists elsewhere in the organization. Even when the underlying technology is in place, the experience still feels broken if the process is not connected.

Organizations that invest in process excellence are better positioned to deliver consistent experiences, reduce friction, improve operational efficiency, and scale complex B2B models. Process alignment also makes it easier to respond to change, because teams can adapt faster when the underlying journey is not held together by disconnected handoffs.

Why omnichannel matters

Omnichannel in B2B is not just about offering more ways to buy. It is about making those ways work together. When the customer starts on one channel and finishes on another, the handoff needs to feel natural. If not, the customer experience becomes fragmented very quickly.

That matters because B2B buyers are increasingly comparing their business purchasing experience to the consumer experiences they already know. They expect simple navigation, transparent pricing, reliable order updates, and a service team that understands the full context of the account. In other words, they want the convenience of digital commerce without losing the support and complexity that B2B purchasing often requires.

This is also where many companies struggle. They may have a strong storefront, but weak back-end coordination. Or they may have good sales support, but poor visibility once the order has been placed. Omnichannel transformation closes that gap by connecting the customer-facing experience with the operational processes behind it.

Accelerating outcomes with the Advanced Success Plan for 麻豆原创 Customer Experience

The Advanced Success Plan for 麻豆原创 Customer Experience helps organizations accelerate omnichannel capability build-out through outcome-based governance and alignment between strategy and execution.

Rather than focusing only on implementation, the emphasis is on measurable outcomes across the full customer journey. This includes enabling consistent omnichannel experiences, aligning 麻豆原创 Commerce Cloud, 麻豆原创 Sales and Service Cloud, and fulfilment processes, managing catalog and contract complexity at scale, improving order sourcing and fulfilment coordination, and strengthening end-to-end process alignment.

The goal is to ensure all capabilities operate as one connected system rather than separate functions.

That becomes especially important in B2B environments where a single transaction can involve multiple stakeholders, custom pricing rules, approval steps, and several systems working together at once. Without clear governance and alignment, even well-designed digital tools can create confusion instead of clarity.

With the right operating model, however, organizations can turn complexity into a strength. They can reduce friction for customers, improve efficiency internally, and create a more reliable buying experience across every channel.

The Advanced Success Plan for 麻豆原创 Customer Experience includes access to Business Process Best Practices which helps customers understand the end-to-end process flow and best practices for executing business processes across 麻豆原创 Sales and Service. This service showcases reference processes and bridges the gaps that can occur during rapid implementations of solutions in complex landscapes or when implementation of multiple solutions creates fragmented processes without taking into consideration unique end-to-end view.

Conclusion

Omnichannel commerce and B2B digital transformation are reshaping how organizations engage customers and deliver value. Success depends on connecting commerce, sales, service, and fulfilment into a unified operating model supported by strong end-to-end processes.

Organizations that focus on process excellence and outcome-based governance are better positioned to scale effectively and meet rising customer expectations. The Advanced Success Plan for 麻豆原创 Customer Experience enables this by connecting strategy to execution and supporting consistent outcomes across the customer journey.

For B2B companies, the real shift is not just digital adoption. It is building a model where channels, processes, and teams work together in a way that feels simple to the customer, even when the operation behind it is complex.

Nikola Stojanovski is a 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.

*Digital Commerce 360, 2025
**McKinsey, 2021
鈥燚HL, 2025

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.

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:

• : Reigned supreme in the commerce space, achieving the coveted #1 Rank in the Enterprise Grid庐 Report for Order Management. It was also recognized as a Leader for E-Commerce Platforms and Omnichannel Commerce.
• : Showcased its versatility and power, earning a Leader position in multiple reports for CRM and Sales Analytics. It secured an impressive #2 Rank in the Enterprise Grid庐 Report for CRM.
• : Named a Leader in the highly competitive 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 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聽麻豆原创 Sales Cloud, and聽for 麻豆原创 Service Cloud.聽

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.聽
• 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.

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.

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 .

Visit the . Get 麻豆原创 news via  and .

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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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.聽

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�

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 .

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.