Only 10% of Companies Are AI Future-Ready

Only 10% of Companies Are AI Future-Ready. Here's the Workforce Gap That's Holding the Other 90% Back.

The Adecco Group surveyed 2,000 C-suite leaders across 13 countries and 17 industries in May 2025 and asked whether their organizations have structured plans to support workers, build skills, and lead through AI's disruption.

Only 10% said yes.

That means 90% of organizations — most large enterprises globally — are investing in AI without a coherent plan for the human infrastructure required to realize that investment.

This is the workforce gap. And it is not a soft, cultural problem. It is a hard financial one.

The Confidence Illusion

WalkMe's 2025 State of Digital Adoption Report, drawing on a survey of nearly 4,000 enterprise leaders and employees, documents the gap between how executives perceive their AI readiness and what employees are experiencing.

79% of executives express confidence that their organizations will achieve AI transformation goals. Only 28% of employees feel adequately trained to use their company's AI tools. Only 25% can use AI to work more efficiently.

This is not a measurement lag. AI spending at large enterprises grew from $14 million to $23 million in 2025, a 64% increase. That investment produced genuine capability in the tools being deployed. The execution failure is occurring at the human adoption layer: the technology is in place, but the workforce is not yet equipped to use it.

WalkMe's data puts a direct cost on this friction. Employees waste an average of 36 working days per year dealing with technology frustrations — the equivalent of nearly two full months of productive time lost annually to digital friction. At an enterprise of 5,000 employees, that is not a rounding error. It is a measurable drag on the P&L.

Why the C-Suite Is Misaligned on the Solution

The Adecco research identifies a second layer of the problem: the C-suite itself is fragmented on what is needed.

When asked to identify the core barriers to AI progress, different executives gave fundamentally different answers:

• 42% of COOs cite lack of necessary data infrastructure
• 41% of CEOs say they don't yet see the value
• 49% of CHROs cite a lack of the right internal skills
• 27% of CFOs say the budget is available but not being effectively deployed

These are not wrong answers. They are correct diagnoses of the same underlying problem, seen through different functional lenses. The data infrastructure problem the COO identifies is the same workforce enablement problem the CHRO describes, because without data infrastructure, skills training cannot be targeted to the right gaps. Without visible value, the CEO cannot champion the cultural change the CHRO needs to drive adoption.

53% of CEOs say their leadership teams struggle to align on AI and talent strategy in a timely way. The misalignment is not accidental. It is structural — the product of a C-suite that does not have a shared intelligence layer connecting what each function sees to a common picture of what the organization needs.

What the 10% Are Doing Differently

Adecco's research is not only a documentation of failure. It identified the specific organizational characteristics that define the 10% of enterprises that qualify as AI future-ready.

These organizations share four commitments.

1. A structured and accountable approach to AI. This includes formal policies, defined governance, and explicit accountability for AI outcomes. The remaining 90% largely operate without this: 34% of companies have no policy on AI use, despite 60% expecting employees to update their skills for it.

2. Facilitated adaptability and career mobility. Future-ready organizations create pathways for employees to grow into AI-augmented roles rather than simply mandating adoption of new tools. 65% of future-ready organizations have adopted skills-based workforce planning, moving away from rigid job structures.

3. Committed workforce skills development. Not training programs as a compliance exercise, but continuous investment in building AI capability across all levels. Only 33% of companies are investing in data to understand and close skills gaps — the most basic requirement for targeted workforce development.

4. Prepared leaders. Adecco found that only about a third of leaders worked to develop their own AI capabilities over the last 12 months. The research is direct on the consequence: leaders who have not developed AI fluency cannot credibly model the behavior they are asking their organizations to adopt.

The financial difference is visible. 64% of future-ready organizations say their leadership team's use of AI is improving decision-making, compared to 49% of all companies. 71% are very confident in their AI implementation strategy, compared to 58% overall.

The Paradox of Employee AI Confidence

Adecco's Global Workforce of the Future 2025 report, drawing on 37,500 workers across 30 countries, offers a data point that complicates the standard narrative. More than 70% of employees now say nothing is holding them back from using AI tools, up from just 19% the year before.

Employee AI confidence has surged. The adoption problem is not that employees are resistant. It is that organizational systems — including governance, training, workflow redesign, and accountability structures — have not kept pace with employee readiness.

Workers report saving an average of two hours per day due to AI tools. But Adecco's research notes that this perception of efficiency does not consistently translate into measurable productivity gains at the organizational level. The time freed by AI is not being redirected into higher-value work at scale, because the workflows, incentives, and organizational design have not been redesigned to capture it.

This is the difference between AI adoption and AI integration. Adoption is deploying tools. Integration is redesigning the work around them.

41% of future-ready workers are actively involved in designing the workflows that AI is reshaping, compared to 24% of mainstream workers. This co-design approach is not a cultural nicety. It is an operational effectiveness strategy: the people who know most about how work gets done are the ones most capable of designing how AI should fit into it.

The Leadership Modeling Problem

The Adecco data surfaces a dynamic that most organizations are not addressing: employees take their cues on AI from how their leaders use it.

Organizations that expect employees to adapt to AI while their own executives have not developed AI capabilities are asking for a behavior that the organizational culture does not model. Adecco found that only a third of leaders developed their own AI skills over the last twelve months, in the same year that AI adoption among their workforces was expected to accelerate significantly.

Deloitte's 2025 Tech Exec Survey documents what the executives who are ahead of this curve are doing. 45% of tech leaders rank generative AI skills as the most urgently needed within their organizations. 70% plan to expand their headcount specifically because of generative AI — not because AI is replacing workers, but because AI fluency is creating new roles that require human expertise: AI ethicists, governance translators, compliance officers, and cross-functional AI strategists.

The enterprises building the workforce for this moment are not waiting for a training program to cascade through the organization. They are building the intelligence infrastructure that makes AI capability visible, measurable, and actionable at every level, starting with the leaders who model it.

What Workforce-Ready AI Actually Requires

The path from the 90% to the 10% is not primarily a training budget decision. It is an organizational design decision. Five requirements emerge from Adecco's research and Deloitte's findings.

1. An AI policy that covers all employees, not just IT or leadership. With 34% of companies having no AI use policy, the baseline governance that enables safe employee AI adoption is absent at most organizations.

2. Skills-based workforce planning. Replace role-based job structures with capability maps that make skills gaps visible and targetable. This is what the 33% of companies investing in skills gap data are actively building.

3. Workflow co-design with employees. Involve frontline workers in designing how AI fits into their work rather than mandating adoption of decisions made by IT. Adecco's data shows this is the single most differentiating practice of future-ready organizations.

4. Leader AI fluency development. Make executive AI capability-building a formal organizational commitment, not an optional personal development activity.

5. Cross-functional intelligence infrastructure. This is the layer that connects what AI tools across the organization are producing to the decisions each function needs to make. Without it, skills development, workflow redesign, and governance operate in the same fragmented silos they were designed to break down.

The Compounding Cost of Waiting

Adecco's research identified workforce strategy that cannot keep pace with AI disruption as the number one talent risk to business growth in 2025. Not regulatory risk, not technical risk, not competitive risk. Workforce strategy.

The organizations that close this gap now — building the governance, the skills infrastructure, and the intelligence architecture that connects AI capability to human work at scale — are not just managing a talent risk. They are building the organizational capability that determines whether their AI investments produce the returns McKinsey, BCG, and MIT have documented as achievable for the organizations that get this right.

The 90% that have not yet built a structured plan for AI workforce readiness are not behind because they lack ambition. They are behind because the structural requirements for AI-ready organizations — including governance, data infrastructure, workflow redesign, and leadership modeling — are harder and more cross-functional than buying the next tool.

That structural work is exactly what separates the 10% from the rest.

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