With two decades spent inside a professional services firm where human judgment underpins the business model, an EY Global Vice Chair argues in a Fortune commentary that what clients seek from EY — not just analysis, but the experience, insight, and perspective needed to navigate uncertainty and make better decisions — is exactly the skill AI is now forcing into every job, whether the title on the door says analyst, technician, or machine operator.

The paradox of the AI era, the executive contends, is that the more capable the technology becomes, the more the market rewards the other things. The most sought-after skills are intrinsically human — it all comes down to what AI cannot do.

The talent gap is material. Even as AI investment climbs, companies are missing out on up to 40% of AI productivity gains due to gaps in talent investment, according to an EY survey. Many training programs focus on technical AI experience, skipping over the critical skills that will determine whether an AI strategy pays off. The upshot: the constraint on AI returns may sit in people strategy as much as in the technology itself.

What AI still cannot do

Technology thrives at repetitive tasks and at surf data. But it falls to humans to define the goal, set the purpose, and provide the context that gives information meaning. People decide which objectives are worth pursuing, recognize when context changes, and remain accountable for the results. Without that human direction, data and insights may not be applied effectively or generate their full potential value.

This reframes what an AI-orchestrated workplace looks like. It does not remove the person — it removes the parts of the job that never required human judgment in the first place. What remains is the part only a human can do.

Training AI fluency as a leadership skill

To take full advantage of an AI-enabled world, AI fluency must become more than a specialty skill — blending the operation of AI tools with the evaluation of outputs for relevance, and knowing how, and when, to intervene when things go wrong.

The scale involved is striking: machine identities outnumber human employees 82:1 inside the average organization, according to a CyberArk report, which ties that proliferation to exponential identity-security threats where machine identities are managed in fragmented ways. Scale like that does not dilute human responsibility; it concentrates it. As AI systems become more autonomous, human judgment becomes even more important.

Analytical thinking is the most desirable core skill employers search for in potential hires, according to a World Economic Forum survey, with roughly seven in ten employers calling it essential. Leaders want an employee who avoids blind delegation and knows how to challenge AI when it matters.

The bottom line: use AI to make the job more efficient, not to take over the judgment decisions that remain the most important part of the work.

Don't navigate alone

In practice, no single organization is solving AI workforce integration alone. Isolated approaches can be time-consuming and costly, as organizations waste cycles rebuilding workstreams others have already solved.

That is the alliances and ecosystems work the author leads at EY, helping organizations create the right foundations to scale technology efficiently without rebuilding what someone else has already solved.

Recent work with the beverage manufacturer Lion evidenced this, as collaboration with ecosystem partners led to 75% faster customer response speed and roughly a 30% improvement in operating costs across its people function. Rather than trying to build every layer of an AI-enabled people function from scratch, Lion worked with partners who had already solved pieces of that problem elsewhere — a reference point for organizations weighing an in-house build against a partner-led route.

Redesign workflows so humans stay at the center

Workflows must be reimagined to keep humans at the core of decision-making and maintain trust. When hybrid AI-human workflows are built around rigid handoffs — expecting AI to complete its tasks before passing the result to a human — the fundamental assumption is that tasks can be divided cleanly. In practice, this structure collapses opportunities for meaningful judgment calls and can add more iterative work.

The more durable model organizes work around shared tasks in a truly integrated process, where humans and AI contribute at every stage rather than in sequence.

Daikin, a multinational company specializing in heating, ventilation and air conditioning, illustrated this by leveraging hybrid AI and human teams working in tandem to roll out a new enterprise resource planning (ERP) system. While AI assisted with code generation and automated testing, employees maintained oversight for exceptions and high-risk scenarios — reinforcing that judgment is something no algorithm can absorb. Per EY's account of the engagement, the approach accelerated delivery by approximately 30%, and the pilot produced a 10% gain in counter efficiency and a 20% faster financial close.

The skill that doesn't expire

With AI model capabilities in constant flux, the upskilling conversation falls behind if it only tracks technical updates. Technical skills tied to a specific model version have a short shelf life; the judgment to know when and how to intervene does not.

Judgment is essential to establish secure and trusted AI.

None of this happens by accident. It takes fluency at every level, partners who share the building burden, and workflows designed to keep humans in the loop at every stage to provide appropriate oversight.

The organizations getting AI scale right are making deliberate choices about which parts of the job stay human. And AI adoption done well clarifies that role rather than shrinking it.

The views reflected in this article are the views of the author and do not necessarily reflect the views of the global EY organization or its member firms. The opinions expressed in Fortune.com commentary pieces are solely the views of their authors and do not necessarily reflect the opinions and beliefs of Fortune. This story was originally featured on Fort.com.