EY, KPMG, and PwC are all betting on the same capabilities this year

AI is changing how organisations work. Routine tasks are increasingly being automated, accelerated, or completed with AI. But as technology takes on more of the work, a different question is becoming increasingly important:

What should people get better at?

Some of the world's largest professional services firms are already answering that question.

EY recently announced a $100 million bonus pool designed to recognise employees who demonstrate strong leadership, business acumen, judgment, collaboration, and other capabilities that help the organisation navigate disruption.

EY is not alone. KPMG has shifted elements of its audit internship training towards critical thinking and judgment, while PwC has introduced learning that combines AI skills with human capabilities such as empathy and creativity.

The pattern is clear: organisations are no longer treating AI skills and human skills as separate priorities.

As AI takes over more routine work, the ability to make good decisions, adapt, collaborate, and lead becomes even more valuable.

For HR and L&D leaders, this creates a new challenge: how do you develop and measure the human capabilities that become more important as AI adoption grows?

Here are five lessons HR and L&D can take from the shift happening across professional services.

1. AI adoption makes human skills more valuable

AI can process information, generate content, automate tasks, and support decision-making. But organisations still need people who can determine what should be done with those outputs.

That's where skills such as judgment, critical thinking, adaptability, and business acumen become increasingly important.

EY's decision to link these capabilities to financial rewards demonstrates how seriously organisations are beginning to take them.

The lesson for HR and L&D is that AI adoption should not only focus on teaching employees how to use new tools.

Employees also need to understand how to:

• Question AI-generated information
• Make decisions using AI outputs
• Apply business context
• Communicate effectively
• Adapt when workflows change
• Collaborate with colleagues and technology

The value of AI depends partly on the capabilities of the people using it.

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2. Judgment cannot be assumed

One of the most important skills receiving renewed attention is judgment.

As AI becomes better at completing technical and repetitive tasks, employees may have more information available to them than ever before. But more information does not automatically lead to better decisions.

Someone still needs to determine whether an AI-generated recommendation makes sense, whether the underlying information is reliable, and what action should follow.

This is particularly important for managers.

Managers are increasingly expected to lead teams through AI adoption while also deciding where AI should and should not be used.

For HR and L&D, this means judgment should become something that organisations actively develop and practise, rather than simply listing it as a competency.

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3. Adaptability matters more than experience alone

Experience is valuable. But experience without adaptability can quickly become outdated.

The way teams work today may look very different from the way they worked five years ago. AI is accelerating that change by altering workflows, responsibilities, and expectations across roles.

A manager who has been successful in the past may still need to learn how to lead an AI-enabled team.

That requires more than technical knowledge. It requires the ability to experiment, learn, respond to uncertainty, and change direction when necessary.

For L&D teams, the implication is significant.

Leadership development should not prepare managers for one fixed version of their role. It should help them continue learning as the role itself changes.

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4. AI adoption is a team challenge, not an individual one

AI implementation rarely succeeds because one employee becomes an expert in a new tool.

It succeeds when teams change how they work together.

Managers need to create space for experimentation. Employees need to share what works. Teams need to establish expectations around responsible AI use. Leaders need to connect AI adoption to business priorities.

This makes collaboration particularly important.

The parallels with high-performing teams are clear:

• Clear responsibilities - Clear AI roles: Teams need to understand who is responsible for adopting, managing, and evaluating AI.
• Open communication - Shared learning: Employees should be able to exchange experiences and lessons from using AI.
• Shared accountability - Responsible adoption: AI should not become an individual experiment disconnected from team or business goals.
• Strong leadership - Consistent adoption: Managers play a critical role in turning AI strategy into everyday behaviour.

For HR and L&D, developing individual AI skills is therefore only part of the equation.

The bigger challenge is helping managers create teams that can learn and adapt together.

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5. Human skills need to be developed, not just recognised

There is a difference between saying that human skills are important and actually building them.

Leadership, judgment, adaptability, empathy, and collaboration can easily become vague words on a competency framework. The challenge is turning them into observable behaviours that employees can practise and managers can assess.

This is where HR and L&D have an important role to play.

Organisations need to define what these skills look like in real situations, provide opportunities to practise them, and give managers practical ways to recognise development.

For example, instead of simply asking whether an employee demonstrates "good judgment," organisations could explore how they:

• Evaluate conflicting information
• Challenge an AI-generated recommendation
• Make decisions under uncertainty
• Consider different perspectives
• Explain and communicate their decisions

The same principle applies to adaptability, collaboration, and leadership.

If a skill is important enough to reward, it needs to be specific enough to develop and measure.