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As companies continue to invest heavily in Generative AI (GenAI) and agentic AI, the keys to success are moving beyond pilots to end-to-end workflow transformation, measuring value with fit-for-purpose key performance indicators (KPIs) and applying consistent governance to scale what works.
GenAI and agentic AI continue to attract heavy investment, yet many organizations are discovering that the returns are slower, smaller or more uneven than expected. Leaders are working to build on pilots and proofs of concept, but the value they anticipate is not consistently appearing in the numbers. Instead, they find themselves caught in an AI ROI trap where experimentation accelerates faster than execution, governance and measurement can support.
This article draws on EY research, including two global surveys of technology industry executives conducted with Oxford Economics, to explore why ROI is often fragmentary and hard to prove — and what leaders can do to convert experimentation into sustained value.
EY and Oxford Economics conducted two surveys of global technology executives. The first online survey collected 1,500 unique responses from respondents spanning 28 countries across the Americas, Asia-Pacific and EMEIA. Participation was highest among chief technology officers and their direct reports (21%); chief information officers and their direct reports (12%); chief financial officers and their direct reports (9%); chief strategy officers and their direct reports (9%). The survey sample had equal representation across the hardware; software and software as a service (SaaS); internet and social commerce; and IT services and cloud subsectors. The second survey collected 1,000 unique responses from similarly populated sample groups.
Each survey presented a series of multiple-choice questions, agreement statements, and open-ended prompts to gauge respondents’ views regarding their business’s enterprise-wide AI strategy. Only representatives from companies with a deployed enterprise-wide AI strategy or concrete plans to adopt one were included in the survey, and all respondents had detailed knowledge of their company’s AI strategy. Respondents were asked about AI use cases and opportunities, governance frameworks, data strategy good practices, and investment approaches related to enterprise-wide AI value creation in the technology sector.
EY surveys found that about 16% of companies report generating zero ROI on GenAI-enabled Copilot initiatives, and fewer than half (43%) see substantial returns above 50%. This gap between expectation and outcome is starting to challenge GenAI’s ability to deliver meaningful business value.
The issue is broader than capability — it’s timing, readiness, and economics. GenAI's journey to value is often longer and more complex than expected, with transformation of workflows requiring significantly different investment timelines. While some offer quick wins, achieving sustainable value necessitates rethinking end-to-end processes and platforms.
Many organizations conduct pilots with immature and inconsistent governance that limit scaling beyond proof-of-concept, hindering ROI. The combination of high inference costs — the ongoing, usage-driven expense of running models in production — together with poor model-task alignment and limited change management can prevent even strong prototypes from delivering lasting returns. As AI capabilities advance from copilots to more autonomous agents, these limitations become more consequential — raising both opportunity and risk.
Chapter 1
Organizations take a pragmatic approach to AI, favoring speed, cost and external solutions. But without clear enterprise vision, these choices enforce pilots, create governance gaps and limit ROI.
As our survey results indicate, organizations are taking a wide array of approaches to decisions around AI architecture and delivery, with most choosing to work with external partners. Only 9% are building their own large language models (LLMs), while 18% are co-developing with third-party providers. By contrast, the majority are relying on external models — with 41% using closed models, 27% open and 26% hybrid approaches. Also, customization is largely incremental: 62% use standard external models, while 51% add retrieval-augmented generation (RAG) and 46% fine-tune with proprietary data. The latter two approaches are not mutually exclusive, so some organizations use both.
The high degree of pragmatism in companies’ AI architecture choices flows through to their vendor selection and solution design approaches, as shown in the chart below. On build-versus-buy, the most frequent choice is using a hybrid case-by-case strategy to balance customization with expediency. Out-of-the-box solutions come next, allowing for faster deployment and immediate functionality even if this results in higher upfront costs. Fewer organizations are relying on external providers or building solution-specific AI capabilities in-house. When engaging vendors, 61% of organizations prioritize best-of-breed over best-of-suite approaches.
Organizations also vary in how they balance rapid deployment of point solutions against comprehensive process redesign, typically aligning their approach to intended outcomes. Quick-to-implement solutions — such as AI-powered customer support systems — still remain prevalent compared to larger-scale objectives like integrating AI across all aspects of the finance function. This preference for rapid-deployment options is largely driven by immediate benefits such as implementation speed, cost efficiency, scalability and, to a lesser extent, ease of integration with existing processes. However, process redesign tends to outperform quick solutions in delivering greater flexibility and control, as well as strategic alignment.
The overall message is clear. In the absence of a clear enterprise vision for AI, deployment choices default toward speed, cost and risk containment — effectively reinforcing pilots and point solutions despite enterprise goals of process transformation and strategic alignment. Those pragmatic choices are rational — but they create measurement and governance gaps that prevent enterprise ROI.
Chapter 2
Several factors are currently limiting AI return on investment, including insufficiently defined key performance indicators, and inadequate or immature governance frameworks.
As companies navigate the difficult transition from AI pilots to enterprise-wide transformation, further factors limiting ROI on AI, include the fact that measurement of the benefits realized is largely qualitative rather than quantitative, and that the KPIs being used are often underspecified.
Some 61% of respondents believe that implementation of AI is creating more value than they are able to quantify accurately. And while companies point to gains in productivity and transparency, they lack clear KPIs to measure impacts at an enterprise level. This appears to reflect a lack of upfront definition of the desired outcomes and metrics.
These issues are compounded by governance that is both inconsistently deployed and still in its early stage of development. As the charts below show, companies are taking diverse approaches to the ownership of AI governance — with the most popular options being ownership by the CTO and distribution across various functions — and are also applying various strategies to embed AI leadership, with a fairly even split between a centralized approach and locating it at a functional level. Meanwhile, the vast majority of respondents have either established a cross-business council or are planning to do so.
The central issue is not the structure of governance, but that governance processes are applied unevenly. In a NAVI world — nonlinear, accelerated, volatile, interconnected — this inconsistency dampens ROI and heightens risk.
The same pattern appears in data management. Nearly half (43%) of respondents use hybrid data models that split control between centralized and decentralized teams, and another 20% use federated approaches where business units manage their own data under enterprise-wide standards. To manage these distributed efforts, more than 50% of respondents report formal processes to assess architecture, data, workforce, customer and business readiness for AI, yet application lags: only 38% say they apply these processes regularly for architecture readiness and just 26% for data readiness.
The takeaway is straightforward: there isn’t a single “preferred” governance approach, but there is a persistent execution gap. Even where governance processes exist, they’re not being used consistently. Closing that gap — by making roles, standards and readiness checks repeatable in practice — is what will move AI beyond pilots and make ROI demonstrable. Agentic AI amplifies the cost of that gap because autonomy increases both scale and consequence.
As organizations’ AI journey advances to investments in agentic AI, the effect can be to repeat the excessive focus on pilots experienced with GenAI — but at larger scale and risk.
For organizations, the step forward from GenAI to agentic AI compounds both opportunity and complexity. In immature governance environments, agentic deployments risk replicating GenAI’s pilot trap at larger scale — introducing more moving parts and more risk. As with GenAI, some stakeholders express confidence that agents are creating more value than they can capture with existing metrics, a view with which 33% say they agree. Also, respondents’ expectations for how agentic AI adoption will impact their current AI capabilities reveal diverging views – ranging from 40% expecting them to coexist and complement each other, to 12% anticipating minimal impact.
Chapter 3
By taking the following actions, organizations can boost the demonstrable value from their spending on AI and position themselves to scale up for an AI-powered future.
GenAI has the potential to be transformative, but realizing its value requires discipline. Success depends on treating AI as a strategic investment, anchored in readiness, rigor and measurable results. Ultimately, escaping the AI ROI trap requires disciplined capital allocation, strong foundations and portfolio rigor. GenAI is increasingly a competitive necessity, but the risk lies in investing without discipline, patience and a clear understanding of time-to-value.
A portfolio mindset helps leaders balance foundational “table stakes” investments with longer-term bets that may take time to mature but are critical for future growth, setting realistic expectations for value realization. Where appropriate, apply a private equity (PE)-like approach: implement and gear investments toward steady, predictable returns. For transformational impact, adopt a venture capital (VC)-like mindset: diversify across many small bets, accept that some will fail, and pursue those that succeed. The key lies in adopting a dual mindset — optimizing for predictability where needed and embracing risk where transformation is possible.
At the same time, successful AI deployment treats governance, risk and cybersecurity as enablers, not brakes. The key is to evolve these principles from static standards to dynamic, embedded pillars, ensuring they are applied consistently across the organization. Organizations that get this right will see stronger returns, not by slowing down, but by making innovation nimble, sustainable and repeatable. Early GenAI programs often favored tight central control; the next phase will reward disciplined scale with consistent guardrails.
A clear set of actions can increase and clarify AI ROI, supporting enterprise-wide scaling and value growth. Based on EY research and experience, five actions can help organizations boost demonstrable value from AI and set the scene for scaling:
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Leaders should redesign end-to-end workflows so value is captured at the process level rather than trapped in isolated applications. Start with the pilots and proofs of concept that demonstrate potential, then scale only what achieves tangible ROI and strategic fit.
Shift from qualitative narratives to fit-for-purpose KPIs tied to business outcomes such as growth, resilience, trust and cost-to-serve. Regularly assess both steady performers and high-risk ventures and be prepared to reallocate capital – doubling down on successes, pivoting or exiting underperformers.
Progress from inconsistent, function-level forums to enterprise-level decision rights that are integrated with deployment, operations and measurement. Establish a central charter for identity, policy and compliance, then give business units authority to design and implement incremental solutions, supported by a center of excellence to align innovation.
Hybrid data environments require stronger lineage, stewardship and quality checks; data readiness should become a gating criterion for scaling AI, supported by repeatable assessments. Introduce autonomy in stages with rigorous evaluation and human‑in‑the‑loop strategies, and provide shared services for data protection, anomaly detection and incident response as autonomy grows.
Balance and diversify investments by combining foundational “table stakes” initiatives with a portfolio of experimental projects. Gear core investments toward reliability, while spreading risk across innovative bets that could deliver breakthrough value and communicate openly about investment strategies, risk appetite, timelines and outcomes.
Organizations are heavily investing in GenAI and agentic AI, yet many remain trapped in pilots that fail to deliver meaningful ROI. EY and Oxford Economics research shows that unclear KPIs, inconsistent governance and immature data practices limit value capture. Most companies prioritize speed and point solutions over end-to-end transformation. To break the AI ROI trap, enterprises should redesign processes, strengthen governance, define measurable value, improve data readiness and use AI to support oversight.
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