Building an AI-Ready Workforce Starts Here

Before You Build AI Skills, Build the Capacity to Use Them

Every organization I speak with is investing in AI.

Some are rolling out enterprise-wide copilots. Others are experimenting with AI agents or introducing learning programs designed to improve AI literacy across their workforce. The ambition is clear: equip people with the skills they need to work differently and unlock the potential of AI.

Yet one question keeps surfacing in conversations with leadership teams.

“Why isn’t adoption happening as quickly as we expected?”

The latest Workera 2026 AI Skills Enterprise Benchmark Report, based on almost 89,000 verified skills assessments, offers part of the answer. It found a significant gap between what employees believe they can do with AI and what they can actually demonstrate in practice.¹

That finding matters. Yet it also points to a broader issue that many organizations have yet to address.

Developing AI capability starts with understanding the skills your workforce has today. Sustaining it depends on something equally important: creating the capacity for people to learn, practice and apply those skills in meaningful ways.

Skills Can Only Deliver Value When People Have the Capacity to Use Them

Many organizations are treating AI as a learning challenge.

They invest in training programs, track course completions and celebrate participation rates. These initiatives are valuable, yet they only tell part of the story. Completing a course is very different from confidently applying new knowledge in the flow of work.

Throughout my work with organizations, I’ve seen a similar pattern emerge whenever major change is introduced. Leaders ask people to embrace new technology, adopt new processes and build new capabilities, all while maintaining the same workload and pace.

That approach rarely creates lasting change.

When people are already operating in Time Poverty—moving from meeting to meeting, responding to constant interruptions and juggling competing priorities—learning becomes another demand competing for limited attention. There is little opportunity to experiment, build confidence or embed new ways of working before the next urgent priority arrives.

The challenge is not a lack of willingness. More often, it is a lack of capacity.

Understanding Your Starting Point Matters

One aspect of the Workera research particularly stood out to me. The report argues that organizations gain the greatest value when they begin with verified skills data rather than assumptions. That gives leaders a far clearer understanding of existing capability before deciding where to invest in learning and development.

Machine Learning Fundamentals averaged just 142 on Workera’s 300-point proficiency scale, well below the level associated with designing and building AI solutions. Emerging capabilities such as Agentic AI Fluency also remained within the “developing” range, suggesting many employees understand the concepts without yet applying them confidently in practice.¹

For leadership teams, that distinction is significant.

Awareness is an important first step. Organizational value is created when people can confidently translate knowledge into action.

Capability Needs to Be Distributed Across the Organization

Another finding deserves equal attention.

The report highlights the risk of advanced AI expertise becoming concentrated within a small group of employees. Whenever critical knowledge sits with only a handful of people, those individuals quickly become operational bottlenecks. Projects slow, decisions wait and innovation becomes dependent on limited capacity.

This is not unique to AI.

I’ve seen the same pattern in organizations where customer relationships, operational knowledge or decision-making authority rest with only a few individuals. Performance begins to suffer, not through lack of talent, but through the way that talent is distributed.

AI simply shines a brighter light on an existing organizational challenge.

Long-term resilience depends on building capability broadly across the business rather than relying on a small number of specialists.

Learning Creates Results

The report also provides encouraging evidence that targeted development programs make a measurable difference.

Participants improved Data Visualization and Storytelling skills by an average of 77 percent, while Generative AI Essentials scores increased by 51 percent. Responsible AI capability also improved dramatically following structured learning interventions.¹

These findings reinforce an important message for leaders.

People can develop new capabilities remarkably quickly when organizations provide the right environment for learning. That environment includes relevant training, opportunities to practice, supportive leadership and sufficient time to embed new behaviors into everyday work.

Without those conditions, even the strongest learning program struggles to deliver lasting impact.

A Better Question for Leaders

One example featured in the report describes how ServiceNow assessed employees across roles, established capability benchmarks and gave individuals visibility into their own skill levels before expanding development programs.²

That approach reflects a principle every leadership team should consider.

Before investing in another AI learning initiative, pause to understand where your organization stands today.

What capabilities already exist across the business?

Where are the most significant gaps?

Which roles require deeper technical expertise?

Where might capability shortages create operational friction?

Most importantly, have you created the time and space for people to develop those skills?

The future of AI adoption will not be determined solely by the sophistication of the technology. It will depend on whether organizations create the conditions that allow people to learn, adapt and apply it with confidence.

Technology continues to evolve at extraordinary speed. Organizations that thrive will be those that develop capability with intention while creating the capacity for people to use it well.

References

¹ Workera. 2026 AI Skills Enterprise Benchmark Report. Based on approximately 89,000 verified AI skills assessments.

² ServiceNow – A Playbook for HR and Business Leaders