Deloitte’s 2025 survey of 1,854 executives across Europe and the Middle East, supported by 24 in-depth interviews, shows that momentum is building. In ten percent of organizations, the CEO is the primary leader of the AI agenda. Increasingly organizations view AI as a strategic imperative, not just a technology upgrade – especially as agentic AI begins to reshape assumptions about how businesses will operate in the future.

To capture value, leading enterprises are shifting towards CEO-led, organization-wide prioritization of AI. They are also becoming more selective in their choice of use cases and are building structured programs to drive the more profound organizational change needed to scale AI across the business. Generative AI is already delivering measurable productivity gains. Agentic AI involves greater complexity but holds the potential for end-to-end process redesign.

However, embedding AI into the fabric of an organization is not a simple upgrade; it is akin to the transition from steam to electricity. When factories switched from steam power, they had to reconfigure their production lines, redesign workflows, invest in new infrastructure and reskill their workforce.

The full benefits only emerged once organizations fundamentally changed how they operated. The same is true for AI. It demands significant planning, long-term investment and often deep organizational change. Over time, AI will become embedded into core operations, reshaping how businesses create value.

Rising investment, lagging ROI

Across industries, investment in AI is rising fast. According to Deloitte’s 2025 survey, 85 percent of organizations increased their investment in the past 12 months, and 91 percent plan to increase it again this year.

Yet despite this momentum, most respondents reported achieving satisfactory ROI on a typical AI use case within two to four years. This is significantly longer than the typical payback period of seven to 12 months expected for technology investments.¹ Only six percent reported payback in under a year, and even among the most successful projects, just 13 percent saw returns within 12 months.

According to the executives interviewed as part of this research, AI rarely delivers value in isolation. It is typically introduced alongside efforts to improve data quality, reconfigure teams or streamline operations, which makes it difficult to isolate its value.

The challenge becomes even more pronounced with agentic AI. These systems promise end-to-end process automation and autonomous decision-making, but they also come with greater complexity and longer implementation timelines. Of the respondents already using agentic AI (representing 57% of the total respondents), just ten percent of surveyed organizations said they are currently realizing significant ROI from agentic AI. Among these agentic AI users, while half expect to see returns within three years, another third anticipate that ROI will take three to five years.

Despite these hurdles, executives remain convinced of AI’s long-term potential – and feel increasing pressure to act.

Why ROI is hard to achieve

ROI remains one of the most persistent challenges in AI adoption. It is a long-term endeavor, often taking several years to realize due to complex implementation, user adoption challenges and the need for process integration. The executives that Deloitte interviewed as part of this research give five key reasons:

Why AI investment continues to rise

Despite unclear ROI, most organizations are not holding back. Deloitte’s 2025 survey confirms that investment continues to grow, driven by strategic necessity and belief in AI’s long-term impact.

Executives described AI adoption as a business imperative driven by the fear of falling behind and the promise of improved performance.

Investment is expanding beyond pilots to more integrated deployments, particularly in content generation, customer service, fraud detection and IT operations. Nearly half of surveyed organizations now use AI to streamline workflows and support employees, from simple automation to complex decision-making.

At the same time, organizations are narrowing their focus to high-confidence initiatives. They are also evolving investment models. While 38 percent favor a hybrid approach – combining in-house development with external tools, 32 percent lean more heavily on vendor-built solutions for speed and scalability and 24 percent plan to invest in internal build capabilities.

Even where ROI remains difficult to quantify, many executives believe they are building long-term advantage. Sustained investment depends on a strong belief in AI’s long-term potential.

What successful organizations do differently

To accurately measure an organization’s success with AI, we created a comprehensive AI ROI Performance Index by combining four key business metrics into a single score: direct financial return, revenue growth from AI, operational cost savings, and the speed at which these results were achieved. Based on their overall score, we then grouped the top 20 percent of performers as AI ROI Leaders - to clearly distinguish the characteristics of high-performing companies from the rest.

Only around one in five surveyed organizations qualify as true AI ROI Leaders. These outperform peers by treating AI as an enterprise transformation, embedding revenue-focused ROI discipline and making early strategic bets on both generative and agentic AI.

The five practices outlined below set these leading organizations apart:

Together, these practices demonstrate how quick wins come from well-chosen use cases, while longer-term transformation relies on leadership and a culture that fosters adoption.

Architecture and partner choices as foundations for scale

Careful architecture decisions are critical to scaling AI, with interoperability between systems essential to avoid silos and inefficiencies. Survey findings show IT and cybersecurity are the leading AI use cases, reflecting focus on infrastructure.

One in four cite inadequate infrastructure and data as a barrier to ROI, while improving data foundations is among the top reasons for budget increases. Beyond specific tools, grounding choices in Trustworthy AI principles and business objectives helps ensure technology supports long-term goals.

Generative AI vs agentic AI: Different tools, different timelines

Generative AI refers to models that can create new content – such as code, designs, images or text – based on patterns learned from existing data. Fifteen percent of respondents using generative AI report their organizations already achieve significant, measurable ROI, and 38 percent expect it within one year of investing.

Agentic AI refers to autonomous systems managing complex, multi-step processes with minimal human input. Only ten percent currently see significant, measurable ROI, but most expect returns within one to five years due to higher complexity. The inherent autonomy of agentic AI and the need to manage intricate, multi-step processes with minimal human oversight present significant challenges.

Organizations are adjusting how they measure ROI across these two technologies. Nearly half said they use different timeframes or expectations for generative and agentic AI initiatives. For generative AI, ROI is most often assessed on efficiency and productivity gains. For agentic AI, measurement is likely to focus on cost savings, process redesign, risk management and longer-term transformation.

In practice, this means that successful organizations will not treat generative and agentic AI as competing priorities. Instead, they will leverage generative AI to deliver short-term impact and build momentum, while laying the foundations – change management, data quality and governance frameworks – for agentic AI’s more ambitious transformation.

Measuring what matters

AI is forcing organizations to rethink what counts as value. Traditional ROI models are too narrow. Some 65 percent now say AI is part of corporate strategy, recognizing that not all returns are immediate or financial. This signals a shift: executives increasingly accept that returns may take years to materialize, and that not all benefits can be captured in traditional financial terms.

Leaders also recognize that AI’s success depends on a mature ecosystem, including integrated data platforms, reskilled workforces, scalable infrastructure and strong governance frameworks. As adoption accelerates, successful organizations will strike a balance between short-term wins and long-term ambition. ROI will be redefined – not only as cost savings, but as an indicator of innovation, resilience and sustainable growth.