Enterprises worldwide have crossed a decisive threshold in their use of artificial intelligence, moving beyond experimentation and into large-scale deployment with clear expectations of financial return. That is the central conclusion of the newly released Lenovo CIO Playbook 2026, developed with research insights from IDC and published this week.
Based on input from 3,120 IT and business decision-makers across major global markets, the report paints a picture of AI becoming a core engine of business reinvention – while also exposing significant gaps between ambition and operational readiness.
According to the study, 46% of AI proof-of-concepts have already transitioned into production environments, a sharp signal that AI is no longer confined to innovation labs. Some organizations report projected returns of up to $2.79 for every dollar invested, reinforcing the view of AI as a strategic investment rather than a speculative bet. Nearly all respondents – 96% – say they plan to increase AI spending over the next 12 months, with average investment growth projected at 13%. Meanwhile, 93% anticipate positive returns, underscoring the widespread belief that AI is now fundamental to competitiveness.
Yet beneath this momentum lies a growing tension. While 60% of organizations describe themselves as being in the late stages of AI adoption, only 27% report having a comprehensive AI governance framework in place. The gap suggests a level of overconfidence among CIOs, particularly as AI systems become more autonomous, interconnected, and operationally critical. Challenges related to data quality, integration complexity, skills shortages, and organizational alignment continue to slow progress from pilot to enterprise-wide scale.
The timing of the report is significant. Agentic AI – systems capable of autonomous decision-making and action – is overtaking generative AI as the top priority for CIOs in 2026. Despite this shift, readiness remains limited. Three in five organizations say they are more than 12 months away from being able to scale Agentic AI across their operations, and only 21% report significant usage today. Most remain in pilot phases or early exploration, risking delayed returns as competitors move faster.
Lenovo executives argue that the next phase of AI adoption will not reward experimentation alone. Ken Wong, President of Lenovo’s Solutions & Services Group, said organizations are increasingly deploying AI across business functions but often lack the governance, skills, and infrastructure needed to scale responsibly. As AI priorities evolve toward autonomous systems, he noted, success will depend on the ability to operationalize AI with trust, resilience, and measurable outcomes across hybrid environments.
Hybrid AI architectures are emerging as the preferred model for this transition. The research shows that 62% of organizations now favor hybrid AI – combining public cloud, private cloud, and on-premises infrastructure – as their primary deployment approach. Practical considerations are driving this preference, including data privacy requirements, regulatory compliance, security strategies, and the need for customization and performance optimization. Infrastructure efficiency was cited as a top success factor by 21% of respondents, with scalable, high-performance, and energy-efficient compute ranked among the most critical enablers of AI success.
Ashley Gorakhpurwalla, President of Lenovo’s Infrastructure Solutions Group, described the current moment as a turning point. AI is moving rapidly from experimentation to enterprise priority, particularly in areas such as inferencing and autonomous systems. While the upside includes major gains in productivity, automation, and efficiency, he cautioned that many organizations lack the foundational elements needed to operate AI at scale. Secure, energy-efficient infrastructure, flexible hybrid architectures, and governance frameworks that build trust are now prerequisites rather than options.
The report also highlights a shift in where AI value is being delivered. AI-capable PCs and edge devices are becoming central to hybrid AI strategies, enabling workloads to run locally while maintaining security and performance. As a result, deploying AI-ready endpoints has emerged as the top IT investment priority for 2026. Lenovo President of Intelligent Devices Group Luca Rossi said enterprises increasingly view devices and edge endpoints as the frontline of AI, where intelligence is delivered directly to employees and workflows.
To address these challenges, Lenovo has expanded its enterprise AI portfolio. The company recently introduced Lenovo Agentic AI, a full-lifecycle solution designed to help organizations create, deploy, and manage AI agents at scale. This is complemented by Lenovo xIQ, a suite of AI-native platforms aimed at simplifying integration, governance, and operational management across hybrid environments. Built on the Lenovo Hybrid AI Advantage, these offerings are designed to reduce risk, accelerate time-to-value, and provide a consistent operational framework from day one.
At the infrastructure level, Lenovo’s ThinkSystem and ThinkEdge inferencing servers are positioned to help enterprises move trained models into production, delivering low-latency AI applications across data center, cloud, and edge environments. By focusing on efficient inference at scale, Lenovo aims to bridge the persistent gap between AI ambition and day-to-day business impact.
The findings underscore a broader industry reality: AI’s value is no longer theoretical, but capturing it requires discipline, governance, and architectural clarity. As enterprises race toward Agentic AI, those that fail to address readiness and trust may find that early enthusiasm does not translate into sustained advantage.
Executive Insights FAQ
Why are enterprises accelerating AI investments now?
Proven returns, competitive pressure, and maturing technologies are pushing AI from experimentation into core business operations.
What is the biggest risk highlighted in the report?
A mismatch between AI ambition and readiness, particularly around governance, skills, and data quality.
Why is Agentic AI a priority for 2026?
Because autonomous systems promise greater efficiency and automation but also require higher levels of trust and control.
Why are hybrid AI architectures gaining favor?
They offer flexibility, data sovereignty, security, and performance optimization across cloud and on-premises environments.
What role do devices and the edge play in enterprise AI?
AI-capable PCs and edge endpoints are becoming critical for delivering intelligence directly where work happens.
