There is a widening disconnect between the drive to implement AI initiatives and the operational readiness required to execute them successfully.

This is according to a study from Akkodis, which drew on insights from 500 global chief technology officers, highlighting critical AI trends that shape enterprise transformation.

Although enterprise AI investment continues to accelerate, confidence in the strategies guiding this transformation is falling. C-suite confidence in AI strategy dropped from 69% in 2024 to just 58% in 2025.

The sharpest declines were reported by CTPS and CEOs, down 20% and 33% respectively, an indication of mounting concerns over AI scalability, implementation delays, and the tangible return on AI investments are yielding meaningful results.

CTOs also pointed to a leadership gap in AI understanding. Just over half (55%) believe their executive teams have the fluency needed to fully grasp the risks and opportunities associated with AI adoption. Among employees, that figure falls to 46%, signalling a wider AI trust gap that could hinder successful AI implementation and long-term success.

This lack of fluency is not simply a communications issues – it reflects limited technical understanding and inconsistent alignment at the leadership level. Without clear, informed leadership, organisations can risk stalling AI progress and weakening the credibility of AI-driven initiatives.

While technical expertise remains foundational, as half (51%) of CTOS cite specialist IT skills as the top capability gap, the report emphasises the broader, human-centric skills are becoming equally critical for effective AI integration.

The essential competencies include soft skills, such as creativity (44%), leadership (39%), and critical thinking (36%).

These skills are increasingly vital for interpreting AI outputs, driving innovation and adapting AI systems to diverse business context. As AI becomes embedded in daily operations, organisations require not only deep technical skills but also the cognitive and strategic agility to apply these tools effectively.

Despite growing investment in employee learning and development, many organisations still lack the infrastructure to ensure training is targeted and impactful.

Only 20% of CTOs report using data tools to assess current workforce skills or monitor learning progress. As a result, training often occurs in silos, disconnected from business goals or role-specific requirements.

The report recommends embedding capability development directly into enterprise systems—linking learning to operational workflows and performance objectives. This shift would enable continuous, in-context skill development that evolves alongside digital transformation goals.

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While many enterprises continue to focus on external hiring to address capability gaps, the report cautions that this approach may fall short for scaling AI. External hires often lack the organisational knowledge and cross-functional relationships needed to implement AI at scale.

Leaders are encouraged to pursue a more balanced strategy: combining targeted recruitment with structured internal development pathways to build sustainable, enterprise-wide AI capabilities.

As organisations transition from AI experimentation to deep AI integration, CTOs are increasingly seen as pivotal enterprise transformation leaders. Their responsibilities now extend beyond infrastructure and operations to include cross-functional AI strategy, executive enablement, and systems design that supports both technical and human capacity.

By aligning skills strategy with enterprise architecture and fostering greater AI fluency across leadership teams, CTOs can help close the gap between AI ambition and execution—building organizations that are not just AI-ready, but AI-confident.