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.

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.

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

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.

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 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.

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

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

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

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The business AI imperative

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

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

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

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

ERP as the foundation for business AI

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

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

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

麻豆原创 Business AI Platform

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

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

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

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

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

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

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

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

麻豆原创 Autonomous Suite

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

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

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

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

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

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

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

Industry AI: H&M and Sector-Specific Transformation

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

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

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

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

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

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

Closing: The Autonomous Enterprise

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

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

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