In conversations with customers across industries, trust is the barrier to AI adoption that comes up more often. Leaders and organizations need confidence that AI systems will perform reliably and remain accountable for suggested business actions, protecting sensitive data and treating people fairly. Ethical AI deployment closes that gap. When a system is technically robust, legally aligned, and ethically governed, customers, partners, and employees can scale it with confidence rather than caution.

SAP has updated the SAP Global AI Ethics policy to version 3.0, reflecting the fast-changing AI landscape and lessons drawn from deep-dive interviews with more than 50 international experts. The revision reflects concrete changes made to strengthen the ethical safeguards, accountability, and safety measures within enterprise AI adoption, addressing the substance of business leaders’ concerns, not just the perception of them.

Our approach to AI rests on three words: relevant, reliable, and responsible. For SAP, ethical deployment isn’t a regulatory checkbox or a marketing layer; it’s the foundation that makes the Autonomous Enterprise possible, where intelligent agents coordinate and execute complex workflows while humans remain firmly in control.

Three disciplines for responsible AI

Ethics sit at the very center of SAP’s broader social sustainability agenda, where responsible AI depends on three disciplines working together, not in isolation:

1. Ethics define our values. We set clear moral boundaries for how technology should serve humanity.
2. Security protects our systems and data. We safeguard core enterprise assets against emerging cyber threats and vulnerabilities.
3. Compliance ensures legal alignment. We are proactively preparing for global regulatory standards like the EU AI Act.

Together, they help teams assess whether an AI use case should proceed, what safeguards it requires, and who is accountable for its performance after deployment, ensuring that as business processes become automated, human dignity, fairness, and agency remain intact.

Ethics underpin the Autonomous Enterprise

As AI shifts from assisting people to acting on their behalf, the governance question becomes more urgent. The Autonomous Enterprise represents a repositioning where AI agents no longer just make recommendations, but execute business processes end-to-end, across finance, supply chain, procurement, HR, and customer experience. As CEO Christian Klein put it, “For the mission-critical processes of our customers, ‘almost right’ just isn’t good enough.”

That’s precisely why ethics and governance can’t be an afterthought bolted onto agentic AI. They have to be the foundation it runs on. Every action an SAP agent takes is logged and traceable, so organizations can always know what an agent did, why it did it, and what data it used. Autonomy without that foundation is just an experiment; autonomy built on it is an operating model organizations can trust at scale.

Translating principles into practice

The SAP Global AI Ethics policy, grounded in UNESCO’s Recommendation on the Ethics of Artificial Intelligence, governs the development, deployment, use, and sale of every AI system across the company. SAP’s updated AI Ethics Handbook translates these principles into day-to-day practice. It helps give engineering and product teams a one-stop reference for applying the policy’s guiding principles, from fairness and non-discrimination to human oversight and sustainability.

To bridge the gap between high-level principles and day-to-day engineering, we embed strict operational checkpoints into our product life cycle:

• Mandatory AI ethics impact assessments: Every SAP AI use case must undergo a rigorous evaluation before reaching deployment, based on 10 AI ethics principles. Use cases receive a risk score and the SAP’s AI Ethics Office helps product teams enforce mitigation measures and safeguards in place. Depending on the risks involved, some use cases go through several review steps and may be escalated to the SAP Global AI Ethics steering committee for a final decision.
• Human oversight by design: SAP systems are built to ensure humans remain in control of critical decisions.
• Internal accountability: Compliance is non-negotiable across SAP. All employees acknowledge the policy, and the rules apply universally, including to internal, self-developed AI applications built using tools like Hyperspace AI or Claude Code.

The process provides clear guardrails that are visible to SAP’s stakeholders, including customers and employees.

Leading the industry in AI governance

The demand for transparent governance is urgent, and independent evaluations highlight significant gaps in corporate readiness worldwide. In benchmark research by the Thomson Reuters Foundation and UNESCO assessing 2,972 companies, SAP was among the only 12.4% found to maintain an explicit policy ensuring human oversight in AI systems.

SAP’s leadership in ethical AI has earned external recognition: the company was named a leading positive example in the Collective Impact Coalition for Ethical AI Progress Report. SAP is also an active participant in the World Economic Forum’s Global Coalition for AI and Social Innovation, convened by the Global Alliance for Social Entrepreneurship in partnership with the Schwab Foundation. Working alongside leading technology companies and social innovators, SAP is helping shape responsible AI deployment to accelerate social impact and extend the benefits of enterprise AI to organizations tackling the world’s most complex challenges.

The broader landscape shows how much ground the industry still has to cover. Data from the S&P Global Corporate Sustainability Assessment (CSA) shows that only 36% of responding companies have a dedicated or integrated AI policy. Research from BSI paints a similar picture: just 27% of global businesses currently factor their AI footprint into their broader sustainability strategies, a notable gap given the surging energy demands of AI data centers.

Unlocking sustainable growth

Ethical AI goes beyond risk mitigation and regulatory readiness—it drives tangible economic value. By ensuring fairness, transparency, and robust data privacy, organizations protect their brand reputation while empowering their workforce to perform at their highest level.

When AI coordinates, executes, and optimizes routine business processes within an ethical context, employees are freed from manual tasks to focus on strategy, creativity, and human connection. This synergy between human potential and artificial intelligence forms the backbone of sustainable enterprise growth.

Organizations that understand this will reap the rewards as regulations tighten and societal expectations increase. However, ethical AI only becomes a reality when we work together. We look forward to continuing to work alongside our customers and partners as we embrace one of the most important challenges and competitive advantages in the AI era.

Read the SAP Global AI Ethics policy version 3.0. Find out more about the Autonomous Enterprise.

Matthias Medert is head of Sustainability at SAP.

As artificial intelligence moves beyond providing information and recommendations, enterprise software is becoming more capable of reasoning, making decisions, and taking action across business processes.

It’s a noticeable shift that could fundamentally change how enterprises operate. But according to Manish Sood, CEO of Reltio, an SAP company, and co-author of Agentic Intelligence, it also exposes a challenge companies have wrestled with for decades: fragmented, inconsistent, and poorly connected data.

“There is tremendous enthusiasm around agentic AI right now, and for good reason,” said Sood. “Almost every company is experimenting with it in some form or the other. But there is a very significant gap between ambition and underlying readiness.”

Research conducted by Harvard Business Review Analytic Services underscores that readiness gap: 94% of organizations surveyed are exploring or implementing agentic AI, while only 15% believe their data foundation is truly ready.

“An AI demo can tolerate a lot of imperfections and gaps in the data,” said Sood. “An AI agent operating inside a real business context cannot. The moment we give software the ability to make decisions or to take action, the quality, timeliness, and context of underlying data becomes much more consequential.”

AI highlights legacy data challenges

Sood believes fragmentation, trust, and governance are three recurring barriers when it comes to AI adoption. Because data remains scattered across hundreds or even thousands of enterprise systems, governance models built for traditional enterprise systems must evolve for an environment in which AI agents can initiate actions.

While these are familiar challenges, the speed and autonomy AI introduces is much different.

Historically, humans have compensated for fragmented systems via tapping into institutional knowledge. Employees know which spreadsheet to check, which colleague to call, or which exception to make. AI agents do not inherently possess that organizational context.

“If a human has incomplete information, we can often recognize it, ask another question, or find someone who knows the history,” said Sood. “In comparison, an autonomous agent may simply act.”

The question for enterprises therefore shifts from whether they can build an AI agent to whether they can trust the environment in which that agent operates.

From digital filing cabinets to connected intelligence

Many organizations still operate around what Sood refers to as the “filing cabinet” model. When businesses digitized, individual functions created their own systems for marketing, finance, sales, supply chain, service, and human resources. Moving those systems to the cloud did not necessarily eliminate the underlying silos.

“The intelligence age requires us to move from storing information to connecting that information,” he said.

This connected foundation supports what Sood and his co-author describe as “co-agency,” a new relationship between people and intelligent machines.

Historically, enterprise technology followed a relatively simple model: humans made decisions and machines executed them. AI changes that equation. Machines can contribute speed, scale, pattern recognition, and continuous operation, while people provide judgment, experience, accountability, and an understanding of nuance.

“If the human sees one version of the customer and the agent sees another, you don’t have co-agency; you have conflict,” said Sood.

Turning insight into action

These implications extend beyond technology architecture as agentic intelligence requires companies to rethink strategy, investment, workflows, governance, and the relationship between people and machines. It’s a notable shift that moves from insight to action.

“Insight by itself does not create economic value. Action does,” Sood shared.

For example, predicting that a piece of industrial equipment is likely to fail can provide valuable information. But an intelligent system capable of coordinating maintenance, locating the necessary parts, and scheduling service before the failure occurs can potentially transform that insight directly into a business outcome. In order for this to happen, however, trusted information needs to be available when and where decisions happen.

According to Sood, four responsibilities are necessary for organizations preparing for this environment:

1. Connect critical enterprise data.
2. Contextualize it with relationships, history, policies, and interactions.
3. Operationalize that trusted context at the point of decision.
4. Govern how AI agents act.

“Define where an agent can act, where a human needs to intervene, how decisions are traced, and how systems learn from mistakes,” Sood explained. “The goal isn’t simply to make more data available to AI. The goal is to make the enterprise understandable through AI.”

Building for the intelligence age

As AI models continue to improve, Sood believes competitive advantage will increasingly come from proprietary data, business context, workflows, processes, and feedback mechanisms surrounding them.

Trust will also become more consequential as organizations delegate greater authority to AI. As a result, companies will need confidence not only in what an agent knows, but where its information came from, how current it is, and what boundaries govern its actions.

“The companies that win won’t simply be the companies with the most sophisticated AI,” said Sood. “They will be the companies that create environments in which AI can understand the business, operate with trusted context, and act responsibly.

At SAP Sapphire earlier this year, SAP defined the role of its Business Transformation Management portfolio in the Autonomous Enterprise and SAP Business AI Platform: to apply process intelligence, enterprise architecture, digital adoption, and transformation execution as the operational backbone through which enterprises deploy, govern, and scale AI agents.

Today, at the SAP Transformation Excellence Summit in Atlanta, SAP unveils the latest innovations for SAP Signavio, SAP LeanIX, WalkMe, and SAP Cloud ALM to show how their integrated capabilities become the system on which enterprises can rely for end-to-end agent operations.

SAP Signavio expands AI capabilities and advances AI agent excellence

SAP Signavio is bringing AI deeper into every stage of process transformation while giving enterprises the foundation to run AI agents with confidence. Enterprises no longer question whether to use AI. They ask where it matters most, and how to trust the agents that act on their behalf. With its latest release, SAP Signavio answers on two fronts: building AI agents into every part of the suite to make transformation faster and more accessible, and helping organizations achieve AI agent excellence by giving their agents the knowledge and governance they need to deliver real value.

The latest release of the Process Consulting Agent introduces process mining capabilities, analyzing process performance data across SAP and non-SAP environments, surfacing bottlenecks, and recommending where agents can be applied to create the most value.

Knowing where to act is only the starting point. Realizing that potential requires processes that are designed for AI from the ground up. The re-designed SAP Signavio Process Modeler brings in AI-native process design, integrated governance, real-time collaboration, and suite-wide connectivity into a single canvas, making process modeling faster, more accessible, and built for the way transformation teams actually work.

Good process design sets the intent. SAP Signavio Process Intelligence closes the loop with AI-native and increasingly agent-driven analysis, providing an ever-more autonomous monitoring layer that watches how work runs and helps teams act on recommendations with confidence.

To enable agents to act on those recommendations within organizational context and guardrails, SAP Company Memory preview will offer both people and AI agents a single, consistent source of an organization’s rules, standards, and process know-how.

SAP Signavio provides the process intelligence layer where AI supports both the people analyzing processes and the agents acting on them. That leads to the question of the enterprise architecture underpinning AI agents.

Bring architecture intelligence to life with SAP LeanIX

Enterprise architecture has long been one of the most strategic functions in any organization, yet its value rarely travelled beyond the architecture team. SAP LeanIX is changing that by delivering architecture intelligence directly to the CIO making portfolio decisions, to the transformation office managing change at scale, and to the AI governance leads navigating an expanding agent landscape.

That requires architectural knowledge that is active, not just documented. The enterprise knowledge repository stores approved architecture principles, security standards, and technical decisions as governed knowledge that agents reason over, turning a documented standard into active context that shapes agent design and runtime behavior. The AI Enterprise Architect Assistant has been extended to combine live workspace data, industry benchmarks, and SAP best practices to support complex architecture decisions, with human judgment retained throughout.

The SAP AI Agent Hub provides the foundation for enterprise-grade AI governance, inventorying agents, large language models, and MCP servers across the enterprise, mapping them to capabilities and owners so governance becomes a continuous operational discipline. The new AI Governance Assistant embeds EU AI Act and NIST compliance intelligence directly into this inventory, automating risk classification and reassessing automatically when systems change.

As AI reshapes the enterprise faster than traditional governance can handle, SAP LeanIX shows that architecture intelligence is no longer purely reserved for the experts. It is the architectural backbone the whole business needs to move forward into the AI-native era with confidence.

But architectural confidence does not automatically become employee behavior. That gap is where the WalkMe portfolio operates.

Adoption and execution across every application

Transformation only delivers when people actually change how they work.

Three years into the enterprise AI era, the numbers make that gap painfully clear: Gartner research shows 87% of IT leaders say AI adoption has not happened automatically, and only eight percent of employees use enterprise AI in any meaningful way. That’s happening largely because AI still sits outside the flow of work. With the latest innovations announced today, WalkMe is closing that gap.

Contextual AI assistance proactively surfaces AI tools, including Joule Agents, assistants, and skills, directly where people are already working, across SAP and non-SAP applications. The right capability reaches the right person at the right moment, with the governance controls to manage what agents run, who can access them, and when they trigger.

But surfacing AI is only part of the answer. Agents still need to act, and much of the enterprise is not reachable through an API. WalkMe’s UI-native agent solves the connectivity problem by operating the interface itself, extending Joule’s reach into legacy systems, heavily customized ERP environments, and third-party applications that standard agent architectures simply cannot touch.

Two new capabilities sustain this at scale. AI Authoring lets teams describe the experience they want in plain language and generates a live, deployable plan. With AI Insights, leaders can query how workflows and content are performing and receive analysis, charts, and dashboards built from real data.

More than simply providing access to AI, its real business impact is felt when people start actually using AI, consistently, in the moments that count.

Sustaining that performance, from the first transformation project through live operations, requires one platform to govern the entire life cycle.

One life cycle, one governance model

The measure of agentic AI is what happens after deployment. SAP Cloud ALM governs the full journey: from managing cloud transformation end to end with AI assistants and Joule spanning system analysis, configuration, testing, and rollout. Once agents are live, its AI agent monitoring capability tracks every agent action so operations teams can confirm AI is working as designed, catch deviations before they compound, and demonstrate the business outcomes that justify the investment.

Business Transformation Management solutions from SAP drive the Autonomous Enterprise by operating as a system: SAP Signavio defines how work should run and measures whether agents are improving it; SAP LeanIX governs where agents operate and whether they can be trusted; WalkMe ensures people and agents can execute together across every application; SAP Cloud ALM governs the transformation and deployment life cycle, validating that agents are tested, trusted, and ready before they go live, and monitoring their performance in production to protect the outcomes the business invested in.

Business Transformation Management solutions from SAP are the system that keeps all of it in motion.

Andre Wenz is general manager and chief product officer for Business Transformation Management at SAP.

A delayed delivery rarely stays in one part of a business. It can disrupt production, postpone a launch, affect a customer commitment, and change a financial forecast.

The signal may appear in one transaction or document, but the response depends on context from several functions. Which orders are affected? What alternatives are available? Who needs to act and what should happen next?

Why context matters

This is the role of Industry AI: applying artificial intelligence to industry-specific business data, processes, and expertise so people can move from a signal to an informed action. SAP Business AI Platform supports people in the applications they use every day. Industry AI extends that foundation across the workflows and decisions that matter in a particular industry.

The same event can mean something different in manufacturing, retail, or global trading. A change in demand can affect planning, procurement, and production. A delivery date can influence revenue forecasts and customer commitments. AI becomes more useful when it can recognize these relationships and work with the people who understand them.

People define objectives and guardrails and remain in control. AI assistants, agents, and intelligent applications can help analyze complex situations, bring together relevant information, and coordinate next steps. The value is not only faster execution of an individual task. It is the ability to connect decisions across the business.

How context turns into action

ITOCHU Corporation, a global trading company, is working with SAP to apply AI to financial processing for complex trading transactions. The initial use cases bring together information from transaction documents and related business activities so finance specialists can determine the appropriate accounting treatment more efficiently.

One example is intelligent general ledger posting for raw-material transactions. The solution helps identify the relevant general ledger account and commission information, including the context required for different transaction models. This makes the first step concrete: use AI where a defined process contains repetitive work, complex business rules, and a clear need for context.

The approach keeps the human role clear. Finance specialists review and guide the result, while AI helps prepare information and reduce manual effort.

Beyond a single process

The opportunity extends beyond one finance process. ITOCHU and SAP are using the initial implementations to explore a broader Industry AI approach for the trading industry. Trading companies connect suppliers, customers, products, logistics, contracts, and financial outcomes across business units. An AI capability that understands one process can create a foundation for connecting the next.

SAP brings enterprise applications, business data, process knowledge, and AI together. In collaboration with customers and industry experts, SAP uses that foundation to develop capabilities around the outcomes and requirements of a specific industry. Lessons from customer projects can inform solutions that are repeatable and scalable, while the original customer context keeps the work grounded in actual business needs. That is how SAP can turn an individual customer use case into a foundation for future industry capabilities.

From use cases to Industry AI

This approach will be discussed at SAP Connect in Las Vegas from October 5-7, 2026. At the Finance Connect keynote, “Autonomous Finance: From road map to reality,” ITOCHU Corporation will share how its initial AI use cases can support an Industry AI initiative across the trading business. The session will be available in person, live online, and on demand.

AI should be measured by more than the quality of a single answer. Its real value lies in helping people understand what is happening, decide what matters, and act across the business. That is how the value of AI multiplies: insight creates clarity, and action creates impact.

Andre Bechtold is president of SAP Industries & Experiences and chief revenue officer of Industry AI at SAP.

One way to look at AI in the context of the Autonomous Enterprise is how sophisticated AI models are, their speed, reasoning capabilities, or ability to automate complex tasks. But that’s looking at the story from the inside out.

The real measure of an Autonomous Enterprise isn’t how intelligent its AI is. When you add the customer to the picture, the conversation shifts to making their lives easier, helping them achieve their business goals, and earning their trust and confidence.

This customer-centric perspective and the possibilities of AI reshape how organizations think about work.

One of our life sciences customers is already putting this vision into practice. Every day, the company receives tens of thousands of customer emails across sales, service, and support. Each message must be reviewed, categorized, prioritized, and routed to the right team, often requiring employees to navigate a patchwork of legacy systems, disconnected applications, and manual workflows. As customer expectations continue to rise, simply adding more people to keep pace is no longer a sustainable strategy. By enabling intelligent agents to coordinate work across complex business processes, the company is transforming how customer requests are handled while improving both operational efficiency and customer experience.

This is more than another wave of process automation. It represents the beginning of the Autonomous Enterprise, where enterprise operations become more efficient without sacrificing quality, governance, or control.

Powered by agentic AI, intelligent agents are becoming active participants in executing business processes, collaborating with one another, and driving measurable business outcomes. Their defining characteristic is not just intelligence. They act autonomously within defined guardrails while humans remain accountable for the decisions they make, the actions they authorize, and the business outcomes they deliver.

With that, entire operating models begin to evolve as intelligent agents handle routine coordination, accelerate decision-making, and enable the business to respond with greater speed and resilience. In a volatile environment where customer expectations and market dynamics continue to accelerate, the Autonomous Enterprise is emerging not simply as a technology strategy, but as a new operating model for delivering better outcomes for customers and business alike.

The evolution of service delivery in the AI era

AI is fundamentally reshaping how enterprise software services are delivered and consumed. Service delivery is evolving to proactive, AI-powered operations where intelligent agents automate routine tasks, predict and prevent issues, accelerate incident resolution, and continuously optimize system performance. This enables service delivery organizations to scale efficiently, improve customer experience, and shift human expertise toward higher-value innovation and strategic customer engagement.

The shift to agentic AI makes AI fluency a foundational skill for becoming more productive and efficient in day-to-day activities.  Effective collaboration with intelligent agents can streamline routine tasks, generate higher-quality insights, and free up time for higher-value activities while facilitating innovation-focused customer advisory and strategic decision-making.

Rather than replacing expertise, agentic AI augments teams by enabling faster decisions, automating routine work, and delivering better customer outcomes. Key areas include:

• Customer engagement readiness: AI consolidates customer context, surfaces key insights and risks, and recommends next best actions to help teams prepare faster and engage more effectively.
• Intelligent service delivery processes: AI automates repetitive tasks, orchestrates workflows, predicts issues, and accelerates resolution, enabling more proactive and efficient service delivery.
• Connected Intelligence: AI captures expertise, recommends relevant knowledge, identifies patterns across engagements, and continuously optimizes delivery processes and best practices.

Together, these capabilities enable us to work alongside our customers as their partners, anticipating needs, tackling their challenges, and delivering intelligent, outcome-focused services.

How customers adopt AI as they become autonomous enterprises

For customers, service delivery becomes more proactive, personalized, and outcome-driven. Technical challenges and incidents are no longer assessed solely through a technical lens. They are evaluated based on their impact on business outcomes, customer experience, operational resiliency, and organizational performance.

Instead of reacting to incidents after they occur, AI agents continuously monitor environments, predict potential issues, autonomously resolve common problems, and provide faster, more intelligent support, resulting in higher service reliability, quicker resolution times, and a more seamless customer experience. This shift enables organizations to prioritize response and remediation according to business criticality rather than technical severity alone.

One of our manufacturing customers leverages Joule Agent to continuously monitor ERP system health, identify root causes of issues, recommend corrective actions, and autonomously execute approved resolutions. The solution delivers approximately 3,000 hours of annual effort savings, minimizes unplanned business downtime, and enables standardized, 24/7 AI-powered reporting that aligns with the firm’s enterprise reporting requirements.

Customers are ready to scale AI across their organizations, but they expect transparency, governance, and clarity on where AI acts autonomously and where human judgment remains essential. While enthusiasm for AI’s potential remains strong, customers are seeking clear insights into where AI’s potential lies within their organization. Three themes consistently emerge:

• Measurable ROI: Customers expect AI to deliver a measurable impact beyond innovation. Through our success plans, SAP partners with customers to define success metrics, align AI initiatives to business priorities, and drive outcomes across execution speed, productivity, experience, and operational resilience.
• End-to-end use cases: Point solutions optimize individual tasks, but customers seek enterprise-wide value. Cross-functional workflows such as claims processing and procure-to-pay, where multiple AI agents collaborate, deliver far greater impact than isolated copilots. One of our American customers leverages AI for proactive monitoring, AI-powered root cause analysis, automated exception resolution, and autonomous execution of approved actions, accelerating issue resolution while minimizing manual effort. With up to 80% gains in operational efficiency and improved on-time vendor payment by 25%, the company streamlined its business processes and enhanced supplier collaboration.
• Governance is a business imperative: AI governance is no longer an IT agenda. It became a strategic imperative as business leaders now shape AI decisions, recognizing that trust is the foundation of enterprise adoption. Leading organizations are investing in AI innovation and AI governance in parallel, because sustainable scale requires both.

To address evolving customer needs, the Global Delivery Hub offers personalized success plans that tailor service experiences from self-guided resources to strategic partnerships, helping customers achieve continuous value and success.  Delivering this level of personalized, AI-enabled customer success requires a fundamentally different approach to service delivery.

At the core of this transformation are four guiding pillars that enable the Global Delivery Hub to combine human expertise with intelligent automation and deliver consistent, outcome-driven experiences.

Four C’s power the Global Delivery Hub’s AI-driven delivery model

The transition to agentic AI is not defined by technology alone. It is defined by how organizations prepare their people, evolve their ways of working, and establish the right foundations for responsible autonomy. Service delivery organizations that successfully embrace this shift will combine human expertise with intelligent automation to create more agile, resilient, and outcome-driven services.

Our AI-driven delivery model is built on four critical pillars: customers, capabilities, collaboration, and controls, required to unlock the full potential of AI while maintaining trust, accountability, and business alignment.

• Customer: Through the combination of intelligent agents, automation, and human expertise, service delivery teams can better understand customer needs, anticipate challenges, and provide more proactive, personalized advisory. This creates a stronger partnership model where AI continuously optimizes delivery services and accelerates customer success.
• Collaboration: Driving value with AI requires stronger, more connected, agile, and cross-functional collaboration across the organization. Product, engineering, delivery, and customer teams must work as one team, continuously aligning technology, operations, and customer outcomes to accelerate innovation and business value.
• Capabilities: Realizing the full potential of agentic AI is not simply a technology transformation; it is a people transformation. The future of delivery will be shaped by professionals who combine deep operational expertise with the ability to work effectively alongside intelligent agents. Technical excellence must complement AI fluency, automation skills, and the ability to leverage data-driven insights to make faster, better decisions. The organizations that succeed will be those that empower their people to adapt, learn, and collaborate with AI, creating a workforce that is not only AI-enabled, but AI-empowered.
• Controls: As AI becomes more autonomous, governance must evolve from a set of controls into a strategic capability that enables trust, scale, and responsible decision-making. Organizations need clear principles that define where AI agents can act independently, where human oversight is required, and how decisions are monitored and improved. Strong governance provides the foundation to scale agentic AI with transparency, accountability, and alignment to business objectives.

Without these foundational pillars, organizations risk adding complexity rather than creating value. Those that take a customer-centric approach while investing in the right capabilities, cross-functional collaboration, and appropriate controls will be better positioned to scale agentic AI, strengthen operational resilience, accelerate innovation, and deliver superior customer outcomes.

As agentic AI continues to reshape enterprise operations, these capabilities are becoming the foundation for the next generation of software service delivery.

Leading the next era of software service delivery

Organizations that act early will be better equipped to unlock the value of autonomous capabilities while ensuring governance, trust, and alignment with strategic business priorities. As software service delivery shifts to proactive, AI-powered operations, customers increasingly view providers as strategic advisors and AI co-pilots that help navigate their transformation journey.

By combining intelligent agents, automated workflows, and human expertise, organizations can proactively address challenges and achieve stronger business outcomes. To succeed in this new model of engagement, customers should focus on key areas that will accelerate their path toward an Autonomous Enterprise:

• Adopt a mindset of continuous adaptation: The AI landscape will continue to evolve rapidly. Customers that treat AI adoption as an ongoing transformation will be better equipped to innovate, respond to change, and build a resilient foundation for the autonomous enterprise.
• Build AI-ready teams and operating models: As delivery becomes increasingly collaborative between humans and intelligent agents, teams need a clear understanding of what is automated, what requires human judgment, and how to engage when exceptions occur. Preparing employees with AI fluency, updated processes, and new ways of collaboration will be critical to maximizing the value of agentic AI.
• Early alignment on shared outcomes, governance, and ways of working: Successful AI-enabled delivery requires clear alignment between customers, SAP, and partners on business objectives, success metrics, decision rights, and accountability. Establishing joint KPIs, governance models, and escalation paths early helps ensure AI-driven operations remain transparent, trusted, and focused on measurable business value as autonomous capabilities evolve.

The shift to agentic AI is not simply about automating existing services. It is about creating a new partnership model focused on continuous improvement, shared accountability, and business outcomes. Customers that prepare today will be better positioned to take advantage of the agility, resilience, and innovation enabled by the Autonomous Enterprise.

Sanjay Kulkarni is global head of Delivery Hub and Customer Value Group at SAP.
Julia Kloppenburg is a technology consultant in Customer Engagement & Adoption at SAP SE.
Benedikt Gieger is Product Strategy and AI lead in Digital Manufacturing at SAP SE.

SAP Customer Experience has once again claimed top honors across the G2 Fall 2026 Grid Reports, earning 100+ Leader positions across categories, segments, regions, and report types. Driven entirely by verified customer reviews, this recognition reflects the trust thousands of businesses place in SAP CX to help power their most critical customer-facing operations.

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

“Earning a Leader position in a G2 Report is highly competitive and rooted in verified customer reviews,” Godard Abel, co-founder and CEO, G2, said. “Congratulations to SAP Customer Experience for achieving this distinction. Buyers can be confident this ranking reflects the authentic experiences of real users.”

Shining stars: the standout achievements

SAP Commerce Cloud delivered one of the strongest performances this season. It holds the #1 position in the Enterprise segment for Order Management and #2 Overall. SAP 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: SAP Commerce Cloud is accelerating.

SAP Sales Cloud 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.

SAP Service Cloud 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.

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

What’s next: AI at the heart of SAP CX

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

In SAP 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, SAP 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 SAP 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 SAP 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 SAP 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’s what some of our customers have to say about our products:

SAP Commerce Cloud
“I 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.” – Vedant G, Manager EWS | Read the full review

“What sets SAP 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, SAP Commerce Cloud shines where stakes are high and business models are complicated.” – Hitesh Mistry, Accounting Specialist Vodafone | Read the full review

SAP Sales Cloud
“Out of all the CRMs I have worked with, SAP 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, SAP eases my access to contact records, sales proposals and quotes, account creation requests, escalations, and regular CSR tickets.” – Hitesh Mistry, Senior Customer Service Rep | Read the full review

“I 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.” – Verified User, Enterprise | Read the full review

SAP Service Cloud
“I like how SAP Service Cloud is becoming more AI-driven, especially with features like automatic case classification, sentiment analysis, and AI recommendations that reduce manual effort.” – Carl N, CSIS Web Design Instructor | Read the full review

“I use SAP 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.” – Olatunji O, Support Associate, Hospital & Healthcare | Read the full review

SAP Engagement Cloud
“I 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.” – Food & Beverage Enterprise User | Read the full review