The adoption of AI has been revolutionary, not in terms of the technology’s capabilities, but in the sheer pace at which it has been rolled out. Around 78% of companies report using AI globally in 2025, up from 55% in 2023. Enterprises have now moved beyond tentative pilots and have woven AI directly into automation, decision-making, and day-to-day workflows across almost every business function. Yet the more AI becomes “business as usual,” the more apparent the gap becomes between AI’s potential and companies’ ability to capitalize on it. Put simply, the technology is scaling far faster than their ability to keep up. In a recent survey we carried out on enterprise AI adoption, nearly 80% of organizations described their pace of deployment as “fast” or “very fast.” At the same time, almost two-thirds (60%) say their biggest barrier isn’t the technology itself, but the inability to properly educate and upskill their teams.
This widening “readiness gap” is becoming a real point of friction in enterprise AI, and HR and L&D teams are often the ones shouldering the responsibility. They need to build experience and capability at a pace that mirrors AI’s growth – a task made increasingly harder by inconsistent training ownership, resource deficits, and a workforce unsure how AI will reshape their roles. The technology curve is steepening, and unless people can climb it, organizations risk undermining the very investments they’re racing to make. The AI investment ROI risk rises when viewed in the context of expectations for more than 30% productivity gains within 24 months.
The workforce readiness gap: A crisis in slow motion?
While the speed of AI deployment is grabbing the headlines, the real story sits beneath the surface. A growing number of organizations are openly acknowledging that their people are nowhere near ready for what has already arrived. According to our survey, more than half expect the majority of their workforce will require significant reskilling within the next three years. This is a structural risk that is already reshaping hiring strategies, performance expectations and even morale. Teams are being asked to make decisions with tools they do not fully understand, and leaders are discovering that adoption metrics can rise long before capability does.
For HR and L&D teams, this is likely to feel less like a future challenge and more like a pressure system building in real time. Employees want clarity about how AI will change their jobs, yet many organizations cannot answer that question convincingly. Skills audits are patchy. Training pathways are inconsistent. In some cases, the enthusiasm for rapid AI adoption has outpaced the communication needed to help people feel confident and equipped. The result is a slow but steady increase in anxiety, hesitation and uneven performance across teams. AI may be transforming the business, but without a confident workforce behind it, that transformation will never deliver its full value.
Training is stuck behind the technology curve
If the skills gap is widening, the training investment gap is widening even faster. Most organizations agree that AI literacy has become essential, yet the resources devoted to building that literacy tell a very different story. Our survey reveals that 68% of companies spend less than $1,000 per employee per year on upskilling. That figure might have been workable in a world of periodic training cycles and static tooling. But it’s far less viable in an environment where AI capabilities evolve monthly and employees are expected to make high-stakes decisions with systems they barely understand. Even more concerning, fewer than four in ten companies expect those budgets to grow in the years ahead.
The picture becomes even more complicated when looking at who actually owns the mandate for AI training. Responsibility is split across HR, L&D and CTO-level leadership, and that fragmentation is slowing progress. Some teams assume others are driving the agenda. Others are waiting for clearer business cases or governance frameworks before allocating funding. It’s a patchwork approach, with pockets of sophisticated AI adoption in some parts of the business, and deep capability gaps in others. Until organizations align ownership and treat workforce AI readiness as a strategic investment rather than an optional extra, the pace of technological change will continue to outstrip the talent needed to capitalize on it.
Progress doesn’t always mean progress
Our research shows that 93% of organizations are already augmenting at least one core business process with AI, and many expect that footprint to roughly double over the next two years. On paper, this looks like progress. In practice, however, it means employees are being asked in many cases to rely on systems they have not been properly trained to use, let alone question or quality-check. HR leaders are already seeing the consequences – inconsistent performance, rising levels of workplace hesitation, and an over-reliance on a small group of “AI-confident” employees who become de facto troubleshooters for everyone else.
And when workers lack confidence or clarity around AI tools, shadow practices creep in. Teams quietly revert to manual workarounds, or they use unapproved tools because they feel more intuitive. Quality control becomes harder to maintain. Governance becomes harder to enforce. And because employees feel the pressure to “keep up,” without the support to actually do so, burnout begins to rise. In many cases, the technology is not the problem; the human experience around it is. AI only delivers real value when people trust it, understand it and feel empowered to use it. Without that foundation, the gap between the technology curve and the skills curve will grow wider and wider until businesses essentially find themselves “priced out” of AI because they lack the talent to keep it in check and use it effectively.
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