AI Agents are the new CIO priorityaccording to the CIO Playbook 2026 report (Guide for IT Managers), based on a study commissioned by Lenovo and carried out by IDC in the Europe and Middle East region.
The report ensures that large companies have decisively moved from AI pilots to scale implementations, “driven by demonstrated benefits and expectations of significant economic returns”. Nearly half (46%) of AI proofs of concept have now moved into production, with some organizations estimating average returns of $2.78 for every dollar invested. The results contrast with other reports that claim that 77% of Agentic AI projects fail when scaling.
This year’s annual study Lenovo CIO Playbook: The Race for Enterprise AI (Guide for CIOs: the race towards enterprise AI), is based on information collected from 800 IT managers and business executives in the Europe and Middle East region. The report reflects a regional turning point and reinforces the real and immediate value proposition of enterprise AI, urging IT decision-makers to act now to avoid falling behind the competition. The study observes a clear shift from AI experimentation to generating measurable value. Nearly all (93%) respondents plan to increase investment in AI over the next 12 months, with an average spending growth rate of 10%, and 94% expect positive returns.
Enterprise AI adoption in EMEA
AI is already widely recognized as a key driver of business reinvention and competitive advantage. However, the adoption of AI in markets is progressing at different speedswhich reflects the different levels of digital maturity, regulatory ease and investment capacity, and there is also excess confidence among IT managers. Although 57% of organizations in Europe and the Middle East are at an advanced stage of AI adoption, only 27% have a comprehensive governance framework for the technology. On the other hand, additional limitations in data quality, staff experience, integration complexity and organizational alignment are creating a gap between ambitions and readiness.
Now that AI Agents are overtaking Generative AI as a top priority For IT managers in 2026, these factors will become an obstacle for many organizations to take advantage of the full potential of AI and for this reason important benefits will not be reaped. 65% of organizations are focused on expanding the use of AI agents across their operations within 12 months, but only 16% say they are using it significantly today, while the majority are still testing it or actively exploring use cases.
Some more advanced markets, such as Scandinavia, Italy and the United Kingdom, are going beyond pilot tests. In these countries, most organizations are already systematically adopting AI and increasing focus on hybrid and edge deployments to sustain growth. In contrast, some regions in southern and eastern Europe are in less advanced phases of AI development. A high proportion of these organizations are still focused on the initial planning or development phase. At the same time, the Middle East market is growing rapidly, demonstrating strong adoption inertia and a notable year-on-year increase in interest in AI Agents and advanced AI.
In the region in general, hybrid deployment models predominate as organizations seek a balance between innovation with data sovereignty and operational control, while interest in AI Agents accelerates. This represents a shift from experimentation toward more autonomous and production-ready AI use cases, even as readiness levels vary from market to market.
“We are now seeing clear benefits from the AI pilots and proofs of concept that organizations have invested in, as AI is having a measurable impact across the region.”said Matt Dobrodziej, president of Lenovo’s European region. “However, many companies are not yet fully equipped with the capabilities, governance and readiness necessary to scale AI to its full potential. As priorities shift towards AI Agents and regulatory compliance becomes an imperative, as the EU AI Law demonstrates, trust and scale must be included among the basic considerations from the start. Those organizations that do not do so will run the risk of missing out on tangible benefits.”.
Hybrid AI is preferred enterprise architecture
The study reveals that real-world financial considerations are accelerating the shift toward hybrid AI. Factors such as data privacy, advanced security requirements, and the need to customize and optimize infrastructure are driving the adoption of this model, which combines computing capacity in public, private and on-premises clouds. Today, nearly three in five (58%) organizations prefer hybrid models for their core AI deployments.
High-performance, scalable AI infrastructure is a critical tool for enterprise AI success. Respondents in the region stressed the importance of efficient computing capacity both in terms of cost and energy consumption. This factor was the second in general terms, since many identified it as one of the keys when moving from AI pilot tests to a reliable production phase.
AI PCs and edge endpoints are essential for an effective hybrid AI strategy, capable of running AI workloads locally. For this reason, the deployment of devices with AI capabilities has emerged as the main IT investment priority in 2026.
Lenovo recently introduced Lenovo Agentic AI, an enterprise solution for the full lifecycle of creating, deploying and managing AI Agents, in parallel with Lenovo xIQ, a set of native AI platforms designed to simplify AI and make it more operational in organizations. Built on Lenovo Hybrid AI Advantage, these new offerings combine hybrid services, platforms and infrastructures that improve governance, integration and performance from day one. Supported by Lenovo AI Library’s proven use cases, IT leaders will be able to reduce risk, accelerate time to value, and scale their AI initiatives with confidence as they begin to move beyond experimentation.
To further facilitate real-world deployments, Lenovo ThinkSystem and ThinkEdge inference servers enable businesses to turn trained models into production-ready, low-latency AI applications in data center, cloud, and edge environments. By enabling faster, more efficient inference at scale, the company helps IT leaders close the gap between their AI ambitions and day-to-day business impact.
