The structural imbalance in AI investment
In a recent interview with Fortune, Deloitte CTO Bill Briggs highlighted a striking imbalance: organisations are estimated to allocate roughly 93% of their AI transformation budgets to technology and only 7% to people.
The infrastructure is funded. The workforce is expected to adapt.
This imbalance becomes even more concerning when viewed through the lens of broader transformation research. McKinsey has consistently reported that only around 30% of transformation initiatives achieve sustainable performance impact. In other words, most large-scale change programs fail to fully deliver on their ambitions.
Prosci’s research adds an important nuance: organisations that apply structured change management are up to 7 times more likely to meet their objectives compared to those that neglect the human side of transformation.
When these insights are applied to AI, the conclusion is difficult to ignore. Technology alone does not transform organisations. Sustainable value emerges when people change how they work.
Why AI initiatives lose momentum
Across both enterprise and mid-sized organisations, we observe a similar pattern. AI tools are successfully deployed from a technical standpoint. Governance and security are addressed. Use cases are identified, and early adopters begin experimenting. Initial enthusiasm often runs high. Over time, however, momentum slows.
This slowdown is rarely caused by model performance or infrastructure limitations. Instead, it reflects uncertainty and friction at the level of daily work. Employees may be unsure when AI adds value and when it does not. They may lack confidence in formulating effective prompts. They may struggle to integrate AI outputs into existing processes or question the reliability of generated results.
AI introduces new possibilities, but it also reshapes roles, responsibilities and workflows. Without deliberate guidance and structured support, that shift remains incomplete. Capability does not automatically translate into productivity. Without structured AI adoption, technology remains underutilised.
The real gap in AI transformation
The core constraint in AI transformation is not access to tools. Most organisations today can acquire advanced AI capabilities relatively quickly. The more challenging question is whether employees are equipped to use those capabilities effectively and responsibly.
AI changes how work is prepared, executed and reviewed. Reports can be drafted in minutes. Data can be analysed more rapidly. Repetitive tasks can be automated. Knowledge can be accessed conversationally. However, realising these gains requires more than a software license. It requires clarity about how AI affects specific roles, practical skills in prompting and evaluation, and the confidence to embed AI into everyday workflows.
This is where many initiatives falter. The technology evolves rapidly, but workforce readiness evolves more slowly. Closing that gap requires deliberate investment.
From awareness to measurable adoption
At Xylos, we approach AI transformation as an organisational journey rather than a purely technical rollout. Building a secure and scalable digital foundation remains essential, as does embedding AI into processes to improve efficiency. Yet neither of these efforts will reach their full potential without a workforce that understands how to translate capability into value.
That is why we place strong emphasis on AI and Data Literacy.
AI literacy is not a one-time training session. It is a structured progression from awareness to measurable adoption. Within our programs, we begin with a baseline measurement to assess the organisation’s starting point. Learning journeys are then tailored to roles, ensuring relevance and practical application. Pulse checks throughout the program make progress visible, and a final impact report highlights adoption levels and remaining improvement areas.
Measurement matters. Without visibility on adoption, organisations cannot determine whether AI is truly influencing productivity.
To make learning tangible, we complement structured programs with interactive formats such as the Copilot Escape Game, where teams apply AI in realistic scenarios, and the mAIndset Prompting Game, which strengthens prompting skills across different experience levels. These formats reduce hesitation and build practical confidence.
Learning must connect directly to daily work. Confidence must be earned through experience. Impact must be demonstrated, not assumed.
AI will reward workforce-ready organisations
Enterprises require scale, governance and consistency. Mid-sized companies often prioritise speed and focus. Despite these differences, both face the same strategic imperative: aligning technology investment with workforce readiness.
The organisations that will outperform in the coming years will not necessarily be those that deploy the most advanced models. They will be those that systematically prepare their workforce to use AI thoughtfully, effectively and responsibly.
AI may begin as a technological innovation, but its long-term value is determined by human adoption. AI success is a workforce strategy. And organisations that recognise this early will be the ones that truly work smarter.
