AI Is Creating a Two-Speed Workforce. Leadership Must Close the Gap.

Why the biggest risk of AI transformation may not be technology, but the growing divide between people.

We spend enormous amounts of time discussing how quickly artificial intelligence is advancing. We measure adoption rates, productivity improvements, automation potential and returns on investment. We celebrate employees who experiment with AI, embrace new technologies and develop new capabilities. But there is another side to this transformation that receives far less attention.

Please note: The views expressed on this website are my own and reflect my personal professional experience, research and publicly available information. They do not represent the views, strategies or confidential information of my employer or its affiliated companies.

What happens to the people who aren't moving at the same speed?

PwC's Global Workforce Hopes and Fears Survey 2026 offers some uncomfortable answers.

Based on responses from nearly 50,000 workers across 48 countries and regions, the study reveals a workforce increasingly divided by access to technology, learning opportunities and confidence in the future. For me, this raises a fundamental leadership question.

Are we transforming our organisations, or unintentionally creating two different workforces inside them?

The numbers should make leaders uncomfortable

According to PwC, 64% of employees used AI at work during the past year. Daily generative AI usage increased from 14% to 22%.

At first glance, these numbers suggest encouraging progress. But look deeper.

PwC identifies a group of AI-enabled front-runners representing just 14% of the workforce. These employees are confident, adaptable and optimistic about their future.

Meanwhile, 56% belong to what PwC calls the engine room: the people delivering the everyday work that keeps organisations running. This group has significantly less access to meaningful AI opportunities and professional development.

The contrast is remarkable. Among front-runners, 51% use generative AI daily. Among the core workforce, that figure is only 11%. And perhaps most concerning, only 33% of the core workforce trust top management. We are building increasingly sophisticated technological capabilities while parts of our workforce are becoming less confident in their own future.

That is not only a people challenge. It is a transformation risk.

The problem with how we measure AI success

In many organisations, AI transformation is still measured primarily through technology indicators.

• How many employees have access to AI tools?
• How many licences are activated?
• How many use cases have been deployed?
• How many hours have we automated?

These measurements matter, but they don't tell us whether the organisation is actually becoming more capable. Imagine a company where software engineers use AI coding assistants daily, analysts work with intelligent agents and executives receive AI-generated insights.

At the same time, thousands of employees in operations continue working with fragmented systems, manual processes and limited opportunities to develop new skills. The company may report impressive AI adoption. But has it truly transformed?

Or has it simply accelerated the employees who were already best positioned to succeed?

AI adoption is not the same as organisational progress. Real transformation must improve how the entire organisation creates value.

The hidden danger: transformation without inclusion

There is another important finding in PwC's research. Access to learning and development resources declined from 59% in 2025 to 51% in 2026.

Think about what this means. At the very moment when technology is changing jobs faster than ever, employees report declining access to the resources they need to adapt.

We are asking people to reinvent themselves while giving them fewer opportunities to do so. This creates a dangerous contradiction. Organisations expect agility, curiosity and continuous learning. Yet their operating models frequently leave employees with little time to experiment, develop skills or challenge existing ways of working.

We cannot expect an AI-ready workforce to emerge simply because we provide AI tools. And we cannot expect employees to embrace transformation if they see no clear future for themselves within it.

Why middle management matters more than ever

I have previously written about how AI will fundamentally change the role of middle management. This survey reinforces my belief that managers will be central to whether AI transformation succeeds.

They are expected to deliver business performance, manage uncertainty, introduce new technologies and help employees adapt. Yet PwC reports that 24% of managers find it difficult to manage change.

We often underestimate this pressure. When managers lack clarity about future roles, organisational structures and expectations, they struggle to create confidence within their teams. If we want employees to embrace AI, managers need more than another transformation presentation. They need the authority, capabilities and capacity to redesign work. The manager of the future should not simply supervise people using AI.

Managers must become architects of how people and intelligent systems work together.

What I believe leaders should do differently

I see four priorities.

First, stop treating AI adoption as the destination.

Measure whether employees can make better decisions, solve more complex problems and deliver greater value. A high adoption rate means little if the underlying work remains unchanged.

Second, bring AI into the operational backbone of the organisation.

AI transformation should not be limited to technology teams, specialists and digitally confident employees. Look at the thousands of people handling customers, transactions, exceptions and operational decisions every day.

This is where redesigning work can create significant business value.

Third, make learning part of work itself.

Training cannot remain an additional responsibility employees must somehow accommodate after completing their daily tasks.

Organisations need to create opportunities for people to learn through real work, supported by relevant tools and redesigned processes.

Fourth, make workforce confidence a leadership KPI.

Alongside productivity, efficiency and automation, leaders should understand whether employees feel equipped for their future roles.

· Do people understand how their work will change?

· Do they have opportunities to build new capabilities?

· Do they believe there is a place for them in the organisation we are creating?

These questions should be part of how we evaluate transformation success.

The real competitive advantage

AI will continue becoming more accessible, more capable and increasingly embedded in everyday business activities. Technology itself will become less of a differentiator. What will distinguish organisations is their ability to turn technological progress into broader organisational capability. The companies that succeed will not necessarily be those with the highest percentage of AI users. They will be those that redesign work so that more people can contribute at a higher level. Because an organisation cannot become truly AI-native if only a small part of its workforce participates in that future.

The next generation of competitive advantage will belong to companies that scale human capability as deliberately as they scale artificial intelligence.

That is the leadership challenge I believe we should be discussing.

Sources:

Inspired by PwC's Global Workforce Hopes and Fears Survey 2026, published 29 September 2026.

Source: https://www.pwc.com/gx/en/1/issues/workforce/hopes-and-fears.html