Companies are finally seeing AI ROI — and now they know how much more value it can deliver

Presented by SAPEnterprise AI has moved from experiment to execution, and that shift is beginning to show real returns. The SAP Value of AI Report 2026, produced with Oxford Economics and based on a survey of 2,600 business leaders across 13 countries, found that AI now supports nearly one-third of all tasks in the average organization, rising to 30% from 25% last year. ROI expectations for agentic AI have jumped from 10% last year to 17% this year, but many organizations believe AI could be delivering far more value. The report reveals that the gap comes down to strategy, data, and governance, rather than access to the newest model, says Sean Kask, chief AI strategy officer at SAP."AI has moved from experiment to execution, and that's beginning to show real returns, but there's still a long way to go," Kask says. "That's because AI that lacks context, whether that's processes, data, or governance, at best creates activity without outcomes and at worst creates risk." Companies are still taking a piecemeal approach to AIEven as investment accelerates, more than half of organizations still invest in AI in an ad hoc or piecemeal way, and only 17% report a strategic, holistic approach to prioritization, though that figure has nearly doubled from 9% a year ago. That fragmentation may go back to board-level demands that employees start adopting AI without a strategy or adequate AI literacy behind it, which could produce scattered skunkworks efforts. In other companies, a lack of attention at board level can leave employees bringing their own tools to work and just experimenting."You end up with a lot of organic, disjointed AI initiatives that pop up, and they struggled sometimes just because of data quality," Kask said. "But even the initiatives taking a strategic approach are still working in silos, where they may have consistent data that works in that one use case, but they're still not at the level where they're transforming an entire business process." That may help explain one of the report’s more counterintuitive findings: 69% of businesses say they are satisfied with their AI ROI, because they've proven AI can generate returns. Yet 67% remain unconvinced the technology is delivering its full potential, because that learning experience has made them aware of both how much more value AI can deliver and the challenges they need to overcome to scale it.Agents are changing the economics of enterprise AISAP shipped more than 400 AI use cases across its portfolio so far, with many more in the works. Agents represent the next expansion, because they can plan and reason through multiple steps and tools to reach an objective, which mirrors how people and processes work, Kask says."You're giving a task or an objective to an AI system, and it's able to iteratively work through several steps and access various tools to achieve that outcome," Kask said. "For instance, we've released, in beta, an agent for accruals accounting, a job that would typically take an accountant around 12 hours a month for a mid-size-company, and it gets reduced to two or three hours. So now scale that out across all these processes and its huge potential." In fact, general AI ROI went from 16% to 21% this year, and should grow to $15.9m in two years’ time, even as only 3% say they are fully prepared for it.Data quality remains the biggest barrier to AI valueGetting ready for agents comes down to two fundamental requirements: connecting agents to contextually rich data, and governing them at scale. Data quality and availability are now the number-one reason organizations say they're not getting more value from AI, according to 73% of respondents, with 79% reporting rework, delays, or backlogs from low-quality outputs at least occasionally. The nature of the problem has changed compared to classic deep learning. Foundation models eliminate much of the need to find data, extract it, clean it, and train bespoke