Enterprises are embracing agentic AI at speed, embedding autonomous AI agents into business processes and experimenting with agents in front-office activities such as marketing and customer service, as well as in operational areas such as shipment tracking, demand forecasting, and supply chain optimization.

Agentic AI builds on the economic potential of generative AI, which McKinsey has estimated could add US$2.6 to $4.4 trillion annually to the world economy. This represents the next stage of enterprise AI adoption: a shift from content generation to autonomous execution, and from isolated pilots to operational deployments.

Agent sprawl

That shift creates a new governance challenge. Agent sprawl occurs when AI agents are created, deployed, or connected across systems faster than the enterprise can inventory them, assign ownership, control permissions, monitor behavior, and optimize or retire them when they are no longer fit for purpose.

Underscoring this shift, a recently published agentic AI survey conducted by SAP LeanIX found that 98% of companies have already deployed AI agents or plan to do so. But as adoption accelerates, governance is struggling to keep pace. According to the same report, less than half of the organizations surveyed have visibility into an inventory of AI agents.

The mechanics of agent sprawl are familiar to any technology leader who has navigated a wave of SaaS adoption. Individual teams, motivated by genuine productivity goals, deploy agents independently. Each one is designed for a specific task—a marketing automation agent, a supply chain monitoring agent, an HR onboarding bot—and each works in isolation. Without a centralized platform or governance framework, the organization accumulates a fragmented landscape of agents that do not interoperate, cannot be audited consistently, and accumulate technical debt faster than they generate value.

Gartner estimates that by 2028, the average global Fortune 500 enterprise will have more than 150,000 AI agents in use, yet only 13% of organizations believe they have the right governance in place to manage those agents. Max Goss, senior director analyst at Gartner, told his audience at a London conference in April: “As CIOs and IT leaders see an explosion of AI agents across their organizations, many are contending with an ungoverned sprawl of agents that expose their organizations to a range of risks, including misinformation, oversharing, and data loss.”

He added: “Many organizations resort to blocking or restricting the use of AI agents, but this is not a long-term solution. If employees are unable to work in the sanctioned tools, they will likely go around the organization’s controls and start using shadow AI, which presents far greater risks. Organizations need to find a balance where they can govern agents and manage sprawl, but also safely empower employees to innovate with these tools.”

Agents typically need broad, cross-environment permissions to function, but those permissions are rarely governed with the same rigor applied to human users. The risk posed by unmanaged or rogue AI agents in the enterprise is therefore real and growing.

AI agent security concerns

Publicly reported enterprise-security examples also point to agents leaking sensitive information or acting outside their intended scope, including cases where malicious instructions caused agents to bypass guardrails, delete production records, or trigger irreversible financial transactions.

The security concern is what registers most sharply with enterprise technology leaders. With chatbots and early generative AI, a security failure typically meant bad output: an inaccurate or inappropriate response that could usually be corrected after the fact. In the agentic era however, the consequences of an agent failure or security breach can be far more damaging because agents can take action, call tools, access systems, and initiate business processes.

That is why agent governance is no longer only an IT operations issue. It increasingly touches board-level concerns: risk ownership, regulatory exposure, data protection, auditability, operational resilience, and accountability for autonomous decisions.

The emerging AI governance platform

Leading organizations are beginning to treat agent governance not as a compliance overhead but as a strategic capability that determines whether AI investments compound as advantages or liabilities. As a result,  effective AI agent governance has quickly become a boardroom topic and is contributing to the emergence of a new platform category: the AI governance platform.

The category is still forming, but its purpose is becoming clear. Enterprises need a way to discover agents, understand what they do, control what they can access, verify whether they are compliant, and monitor how they behave in production.

SAP is one of the agentic AI pioneers in this emerging category. Through its 2023 acquisition of LeanIX, SAP gained a foundation in enterprise architecture management. This has quickly become a recognized differentiator for SAP AI Agent Hub—positioning AI artifacts like agents, models, and MCP servers within the full architecture and business context of the organization.

SAP AI Agent Hub builds on this foundation as a command center for managing and governing AI agents and related AI assets across an enterprise, even when they come from different vendors and run on different systems.

As SAP CTO Philipp Herzig explained on stage at this year’s SAP Sapphire event, SAP AI Agent Hub is intended to provide a governance layer of record for the enterprise agent ecosystem. “Agents are everywhere,” he said. “Some are great, some are not, and almost no one has a consistent picture—no central governance, no clear view of what each agent does, whether it adds value or whether it adheres to your policies.”

He added: “SAP AI Agent Hub changes that. One entry point and command center to discover, manage, and govern all AI agents, LLMs, and MCP servers in your landscape—vendor-agnostic. [SAP] AI Agent Hub allows you to discover all your agents in context: your landscape, your business processes. Once you have identified the right agents, you can control their risk and define architectural decisions or compliance rules.”

A closing window

Given the pace of AI agent deployment, the window for implementing effective enterprise governance before a serious incident occurs is narrowing. For CIOs, CEOs, and company boards, the question is no longer whether to govern AI agents. It is whether governance gets designed into the architecture from the start or retrofitted after the first serious failure.

Organizations that treat agent governance as a strategic priority in 2026 will be better positioned to scale AI as a durable competitive advantage. Those that defer could spend 2027 cleaning up: in enterprise technology, the cost of speed without structure eventually gets paid. But with AI agents, the bill arrives faster—and at greater scale—than anything that has come before.

With a record-breaking heatwave gripping the UK in late June, the “action” in London Climate Action Week 2026 needed no explanation. Much like the temperatures outside, the conversations inside intensified, and the soaring mercury served as a live stress test for the very subjects under discussion: infrastructure, public health, business continuity, and the resilience of the systems everyone depends on.

Under the official banner of Climate Cooperation in a Fractured World, delegates spread across the city and the tone was noticeably different from previous years. Fewer pledges, more blueprints. Less “what should we aim for,” more “who is going to finance and build it.”

Sustainability is a driver of growth

If there was a single reframing that ran through the week, it was this: sustainability is not a cost of growth, it is a driver of it.

That shift was visible in how decarbonization was discussed. Conversations that once centered on targets now centered on operations: Scope 3 emissions, value-chain engagement, procurement and logistics decisions, energy demand reduction. Practitioners repeatedly pointed to an “execution gap”—the distance between climate strategies on paper and projects that are actually permitted, financed, and built—and to the unglamorous work of unblocking infrastructure and untangling supply-chain bottlenecks as the real frontier.

Electrification gave the growth argument its clearest expression. The launch of the Electrify Now initiative, which aims to lift electricity’s share of final energy demand from roughly 20% today to 35% by 2035, was framed as an industrial strategy. Nearly doubling electricity’s share of energy demand in under a decade is an acceleration, and the week’s energy-transition summits were clear about what it demands: scaling renewables at pace, doubling down on energy efficiency, and, above all, building out the grid infrastructure to carry it. Speeding up permitting and resolving supply-chain constraints were named repeatedly as the bottlenecks that will decide whether the target is met. 

The heatwave outside made that case tangible. As cooling demand surges and extreme weather stresses networks, a clean, resilient electricity system is fast becoming a precondition for business continuity and not just decarbonization. UK-focused sessions highlighted the substantial clean energy investment flowing into the country since 2024 as evidence that the low-carbon economy is now a growth story in its own right. 

The same logic ran through the finance agenda. Sessions on moving from risk to resilience and from risk to opportunity focused on mobilizing capital for adaptation and climate-resilient infrastructure, and on the practical challenge of connecting available capital with investable projects through better data, governance, and pipeline development.

Nature is now on the agenda

Perhaps the most striking development of the week was where nature sat on the agenda, and where it is headed. Speakers were blunt about the underlying problem: our economic system is very good at valuing what we take from nature and very poor at valuing nature itself. Worse, we actively pay to destroy it. Figures cited during the week put global investment flows that harm nature at around US$7.5 trillion a year, against roughly $250 billion flowing into activities that help it. As one speaker put it, the task is not to lament that imbalance, but to get the economics right and to start treating nature as something that can be measured, managed, and steered with the same discipline as carbon or financial risk.

That “getting the economics right” is fast becoming a data challenge for business. Work such as the LSE’s research on the economics of landscape restoration suggests that investing in nature can generate returns comparable to investing in factories, railways, or other conventional infrastructure. As nature-related risks and opportunities are codified into emerging frameworks and regulation, companies will have to treat nature as a set of measurable data points: impacts and dependencies per site, per supplier, and per product line, rather than a one‑off narrative in a sustainability report.

Governments have levers too, from requiring companies to stress test for nature-related risk, to shaping incentives so that capital flows toward restoration rather than degradation. For corporate leaders, that translates directly into new categories of information that need to be captured and governed: nature‑related financial exposure, land use and biodiversity metrics, and nature‑positive investment pipelines. What was once an externality is quickly becoming a set of operational KPIs.

Sir Andrew Steer, professor at the London School of Economics, noted that this was the first year nature was represented at the event, but also how far it still has to travel: “Today here in the outdoor tent, next year in the big room.” The implication for businesses is that the organizations that move nature into their core data models and decision frameworks now are better positioned when this topic inevitably moves from the tent to the board agenda.

The AI warning: get sustainability data in now

Underpinning nearly every theme was data. Location-specific climate analytics were described as “the new lens” for understanding financial risk, and AI featured in almost every discussion of how organizations can gain visibility and control over complex energy, water, and supply chain systems.

But the sharpest point made during the week was a warning. As Stephen Jamieson, chief marketing officer of SAP Sustainability, put it: “If we don’t get sustainability data into AI right now, AI will optimize around it. AI works within the systems, the data, and the constraints you give it. If your sustainability priorities live only in documents and presentations rather than in your data and processes, AI will optimize confidently in entirely the wrong direction.”

The logic is uncomfortable, but hard to argue with. Sustainability now plays out at the transaction level—such as carbon cost per shipment, Scope 3 exposure per supplier, packaging compliance per SKU—and the volume, granularity, and pace of those requirements exceed what manual processes and fragmented tools can manage. An organization whose carbon tool cannot see its financial constraints, or whose supply chain system cannot see supplier regulations, hands its AI a broken map.

SAP’s answer to this is the Autonomous Enterprise: a maturity journey that starts with intelligence based on trusted, transparent data; moves to optimization where AI is weighing trade-offs across cost, risk, and sustainability impact in real time; and progresses toward autonomy, where sustainability rules are embedded directly into enterprise workflows and executed within defined guardrails. Sustainability stops being a reporting activity and becomes a governing factor in how decisions are made. The architecture choices organizations make now will determine whether that automation can scale safely later.

From the tent to the big room

London Climate Action Week 2026 closed with an uncomfortable message delivered in 35-degree heat: the climate is not waiting for business strategies to mature. But a hopeful signal surfaced, too: the growth case, the nature case, and the technology case for climate action are converging, and each is being made in the language of returns, resilience, and competitive advantage.

The task for business leaders is to bring those cases inside capital allocation, procurement, and the data and systems where decisions are actually made. The organizations that thrive will be the ones that move the sustainability agenda into the big room, before the next heatwave makes the argument for them.

For more information on scaling sustainability for your business, visit SAP Sustainability.

Monica Molesag is global head of Sustainability Communications at SAP.

Do your workforce insights drive decisions or sit in dashboards? OMV uses the People Intelligence solution in SAP Business Data Cloud (SAP BDC) to spot workforce composition patterns and move talent where it’s needed. Looking ahead, OMV plans to expand into workforce planning and learning analytics, bringing people investments closer to measurable business outcomes.

OMV is a multinational oil, gas, and chemical company headquartered in Vienna, Austria. With operations spanning Europe, the Middle East, Africa, New Zealand, and Norway, OMV is a truly global enterprise.

Like much of the energy sector, OMV is navigating a significant strategic pivot. The company is investing heavily in sustainability initiatives: transforming plastic waste back into oil, building one of Europe’s largest waste-sorting facilities to produce feedstock for refineries, and recycling plastic cups collected from aircrafts into sustainable kerosene. This shift in the business model has triggered a corresponding transformation inside the business—and nowhere more so than in HR.

OMV’s People and Culture (P&C) function launched a strategic program to place people at the center of the company’s transformation. The ambition was clear: become a global HR center of excellence, increase service quality, standardize and harmonize processes, and move decisively towards digitization, automation, and self-service.

The challenge: fragmented data and a manual reporting cycle

Before SAP BDC entered the picture, OMV’s HR data landscape was fragmented across a patchwork of systems that were never designed to work together for workforce reporting and analytics. Employee data lived in two on-premise SAP HCM systems and SAP SuccessFactors HCM, alongside Microsoft Excel, SharePoint, and a system originally built for financial consolidation that P&C used for headcount reporting and planning.

The day-to-day consequences were significant. When a business unit head or department manager wanted a workforce KPI—headcount figures, turnover rates, or anything beyond a basic report available in the system—they would raise a request with their HR business partner. From there, the HR business partner would spend considerable time navigating multiple systems, manually pulling data, compiling it into spreadsheets, and formatting it into a presentation before handing it back to the manager. It was time-consuming, error-prone, and consumed HR capacity that should have been spent on strategic work. Managers had no direct, self-service access to their own workforce data.

Choosing SAP Business Data Cloud and People Intelligence

“Normally, our strategy is not to be the first with a new solution. With SAP BDC it was different,” Bernhard Graser, head of SAP Finance, HR, and Reporting at OMV, said. “We saw the potential immediately and wanted to stop the outbound migration of our HR and SAP data and keep it firmly in the SAP ecosystem.”

The timing was fortuitous. OMV had already completed a substantial SAP SuccessFactors HCM implementation, having deployed SAP SuccessFactors Performance & Goals, SAP SuccessFactors Learning, and SAP SuccessFactors Succession & Development and going live in 2023 with SAP SuccessFactors Employee Central, SAP SuccessFactors Compensation, and SAP SuccessFactors Recruiting. With all core employee data now sitting in a cloud-based SaaS system, the foundation for SAP BDC connectivity was already in place.

OMV’s implementation of SAP BDC and People Intelligence

OMV structured its SAP BDC journey in three steps.

The first step—turning People Intelligence live—was connecting SAP SuccessFactors HCM to SAP BDC. This was not entirely without friction: OMV discovered that its on-premise HCM systems sat on a different Identity Authentication service than SAP SuccessFactors HCM, which required alignment before integration could proceed.

A more substantive challenge was data governance. As an Austrian company with a Works Council, it was not possible for OMV to simply push all HR data into SAP BDC. The team implemented data masking, configured Read Access Logging, and established permission controls that mirror SAP SuccessFactors HCM exactly, meaning a user can only see data in SAP BDC that they are already authorized to view in SAP SuccessFactors HCM. This level of governance was a prerequisite before any business users could interact with the system.

The second step, currently in progress, involves migrating both HCM systems to SAP S/4HANA. Once complete, SAP S/4HANA will connect directly to SAP BDC, enabling a fully unified data feed from both SAP SuccessFactors HCM and SAP S/4HANA into a single platform.

The third step, planned for the near future, is the retirement of the legacy reporting stack entirely, eliminating the spreadsheets and replacing the current workaround in the financial consolidation system with SAP BDC as the single reporting and planning environment for HR.

SAP BDC’s architecture played a key role in the decision. SAP-managed data products—pre-built data models maintained and updated by SAP—were particularly attractive, especially because OMV had kept its SAP SuccessFactors HCM configuration close to standard. That near-standard posture meant a larger share of OMV’s HR data could be served through SAP-managed products, reducing the internal maintenance burden. When something changes in a source system or a data definition, it is SAP’s responsibility to update the model, not OMV’s.

Current and future use cases

After evaluating the intelligent content available in People Intelligence, OMV decided to start with workforce composition insights, now live and providing out-of-the-box dashboards on headcount, workforce structure, and composition, fully configurable and filterable by business users.

With the foundation in place, OMV’s P&C team has been actively collecting ideas for what to build next on SAP BDC. On the operational side, the team wants to track accident-related data as a workforce KPI, monitor open positions across the business, and measure time-to-hire. Diversity is another priority—data that currently sits fragmented across systems. Through its participation in SAP’s forward deployed engineering program, OMV is co-building use cases around learning and certification compliance—a business-critical need in a refinery environment where workers must hold current safety certifications to enter operational sites—as well as skills and headcount. Looking further ahead, OMV intends to move into machine learning and predictive modelling, using the SAP Databricks capability in SAP Business Data Cloud to forecast workforce demand and identify gaps in skills and FTEs before they materialize.

The bottom line

The direction is clear: a single source of truth for HR data, self-service access for every manager and business unit head, and a platform capable of growing from descriptive reporting today into predictive workforce intelligence tomorrow. As Graser encouraged his audience at the end of his session at SAP Sapphire Madrid: “We see great potential in SAP BDC—not only in HR, but also in finance. You should try it.”

Learn more about People Intelligence here.

At SAP Sapphire in 2026, SAP announced major innovations in SAP Cloud ALM, including seven new migration and modernization assistants covering system analysis, custom code, data management, configuration, business process, testing, and adoption—all embedded in the agent-led toolchain to help reduce ERP migration effort. Importantly, SAP Cloud ALM is also the operational observability hub for AI agents within the new SAP AI Agent Hub, helping customers trace agent sessions, monitor goal completion, and govern the full AI agent lifecycle across their enterprise landscape. Customers have new opportunities to transform their SAP landscape, and SAP Cloud ALM is the basis for this transformation.

These announcements bring AI-led transformation to focus and are very relevant as we approach the end of mainstream maintenance for SAP Solution Manager on December 31, 2027*, many customers are transitioning to SAP Cloud ALM to stay competitive and future-ready. SAP recommends that customers complete the transition to SAP Cloud ALM before this date.

We are proud that SAP Solution Manager has served thousands of customers exceptionally well over two decades as a key element of SAP’s support offerings, providing the governance, monitoring, and lifecycle management capabilities needed to support mission-critical landscapes.

Twenty-five years in, the business environment that it was built for has significantly evolved to one where enterprises innovate continuously, scale globally, adopt AI, maintain a clean core, and deliver business outcomes at unprecedented speed. Market expectations have changed, technology stacks are running on cloud-ready architecture, and the revenue potential of businesses has exponentially grown. These realities require a fundamentally different approach and functional scope for application lifecycle management. SAP Cloud ALM was designed with exactly these factors in mind. As a cloud-native solution coming with SAP Enterprise Support, or any cloud subscription from SAP, it can close the gaps that modern organizations face in an increasingly fast-moving digital landscape.

All the information required for the transition from SAP Solution Manager to SAP Cloud ALM is available on the Transition to SAP Cloud ALM page. You can access essential tools for a seamless transition as well as recommendations based on your current landscape, project plans, and operational needs. You can also find focused guidance on typical customer situations.

Take action now:

• Request your SAP Cloud ALM tenant via SAP for Me. 
• Join the welcome session, a 90-minute onboarding designed to help you get up and running quickly. 
• Run the SAP Readiness Check tool for SAP Cloud ALM to get an exact understanding of your current SAP Solution Manager footprint and receive guidance for your transition journey. 
• Expand your knowledge through the SAP Cloud ALM learning portal and join our community events. 
• Subscribe to the monthly ALM newsletter to stay updated on innovations, tips, and use cases. 

While SAP Solution Manager’s end of mainstream maintenance in itself is a call to action, it isn’t the primary business case. The real need for transitioning lies in the value that SAP Cloud ALM delivers: accelerated implementations, AI-powered and autonomous operations, continuous feature innovation, lower TCO, and a platform purpose-built for modern, cloud-first landscapes. As every digital touchpoint around you is being modernized and optimized for value, your ALM landscape should not be an exception.

Stefan Steinle is executive vice president and head of Global Customer Support at SAP.

*Details related to maintenance options are covered in SAP Notes 52505 and 3255311.