Chief technology officers report sharply lower confidence in their organizations’ ability to scale artificial intelligence. New data from Akkodis shows confidence fell to 48 percent in 2026 from 82 percent in 2024, even as spending on the technology keeps rising. The third edition of the firm’s What CTOs Think report draws on responses from 500 CTOs within a broader survey of 2,000 C-suite executives across the Adecco Group.
The drop marks the third consecutive year of decline. Earlier Akkodis surveys captured similar erosion: confidence stood at 62 percent in 2025 and had already slipped from 69 percent in 2024. Investment continues without pause, yet execution hurdles now dominate conversations in boardrooms and engineering teams.
Staffing Industry Analysts covered the latest release in detail. The research makes clear that technology access itself is rarely the bottleneck. Instead, organizations struggle with integration across legacy systems, workflows, and decision processes. Akkodis President and CEO Jo Debecker put it directly: Organizations are moving beyond experimentation and encountering the reality of scaling AI across complex environments. The challenge is no longer deploying AI; it’s integrating it into how work gets done.
Agentic AI systems that plan, decide, and act independently top the list of influential trends for 2026, cited by 40 percent of CTOs. More than half say they already use AI to sort tasks between humans and machines, yet clarity on allocation remains elusive. Only 44 percent of respondents believe their leadership teams possess sufficient understanding of the technology. Just 46 percent report established frameworks for responsible AI use. Workforce trust sits even lower, with only 36 percent satisfied on that front.
Barriers pile up in predictable places. Thirty-two percent point to gaps in in-house technical skills. Thirty-one percent cite uncertainty over return on investment. Twenty-seven percent note a lack of urgency from business leaders. These figures align with patterns seen in prior years, where the gap between ambition and operational readiness widened rather than closed.
TechNewsWorld reported the findings this week and highlighted how data quality, governance structures, legacy infrastructure, and workforce preparation now stand as primary obstacles. The publication noted that models themselves are not the issue. The real friction arises when enterprises attempt to embed autonomous systems into daily operations at meaningful scale.
Broader industry signals reinforce the picture. An IBM Institute for Business Value study released in June found that two-thirds of CIOs and CTOs feel accountable for AI systems they do not fully control. The same research projects a 38 percent rise in deployed AI agents by 2027, with only 11 percent of respondents believing their organizations are fully prepared for that volume. Governance models built for slower, more predictable environments are straining under continuous, autonomous operation.
CIO magazine surveyed leaders earlier this year and captured similar sentiment. Barracuda CIO Siroui Mushegian described the core worry: scaling enterprise-wide without losing control amid a flood of departmental requests. IDC analyst Rajan warned that by the end of the decade, inadequate controls could trigger lawsuits, fines, and leadership changes. Logicalis’s 2026 CIO Report echoed the theme, with two-thirds of respondents doubting their ability to move AI beyond initial deployments and 62 percent admitting they compromise on governance due to limited knowledge.
The Akkodis data also tracks a subtle but important shift in priorities. For the first time, innovation rather than efficiency drives digital investment decisions among CTOs. Marginal efficiency gains from earlier automation waves appear to be diminishing, pushing organizations toward differentiation and new business models. Industry variations stand out: aerospace focuses on workforce development, life sciences on accelerating innovation, and energy on resilience.
Workforce effects remain measured rather than dramatic. Half of CTOs report changes in required skills. Nearly as many note shifts in day-to-day activities. Only 21 percent have seen net workforce reductions tied directly to AI. The emphasis falls on redesigning roles and responsibilities to support hybrid human-AI teams instead of outright displacement.
Debecker described three broad organizational archetypes emerging from the data. Task Automators lean on AI mainly for efficiency. Pilot Operators experiment but stall at scale. Enterprise Orchestrators embed AI across workflows, decision-making, and teams, aligning technology with human expertise and governance. Progress correlates strongly with movement toward the third category.
Recent commentary on X from industry observers echoes the operational focus. One post noted that the architecture challenge now outweighs model access, with agentic workflows demanding new systems design rather than simple deployment. Another highlighted governance, trust, and integration as the decisive factors.
Analysts at Gartner and elsewhere flag parallel pressures. Scaling generative AI responsibly requires adaptive governance that tracks rising risks, costs, and regulatory demands. Visibility into spend across AI and cloud environments has become essential, along with unified FinOps and real-time accountability between IT and business units. Security modernization must incorporate AI-specific risks as autonomous attacks accelerate.
Companies that treat governance as an afterthought accumulate technical debt faster than adoption can advance. Those redesigning operating models from the ground up show clearer paths forward. The pattern holds across sectors: success hinges less on acquiring the latest models and more on building the structures that let those models operate reliably at enterprise scale.
The message from multiple surveys converges. Adoption has accelerated. Confidence has not followed. The next phase of AI progress will be defined by how effectively organizations close that gap through deliberate changes in leadership alignment, skill development, governance design, and workflow integration.
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