6 minBusiness
EY Executive Says AI's Biggest Paradox Is What It Cannot Do
A senior EY leader argues that as AI becomes more capable, the market increasingly rewards human judgment, and companies that treat AI fluency as a leadership skill will capture the productivity gains others miss.
Companies are leaving as much as 40 percent of potential AI productivity gains on the table because they are not investing enough in the human skills that determine whether an AI strategy succeeds, according to a senior executive at the professional services firm EY.
The problem, the executive argues, is a paradox at the center of the AI era: the more capable the technology becomes, the more the market rewards the things it cannot do. The skills that employers now prize most are intrinsically human, and they come down to judgment, context, and accountability.
Many corporate training programs focus narrowly on technical experience with AI tools while skipping the critical skills that decide whether those tools pay off. That gap helps explain why rising investment in AI has not translated into the expected gains.
Technology excels at repetitive tasks and at surfacing data. But people remain responsible for defining goals, setting purpose, and providing the context that gives information meaning. Humans decide which objectives are worth pursuing, recognize when circumstances change, and stay accountable for results. Without that direction, data and insights may never be applied effectively or reach their full value.
This reframes what an AI-orchestrated workplace actually looks like. It does not remove the person from the job. Instead, it removes the parts of the job that never required human judgment in the first place. What remains is the portion only a human can perform.
The executive, who leads alliances and ecosystem work at EY, says AI fluency must become more than a specialty skill. It should blend operation of AI tools with evaluation of their outputs for relevance, and with knowing how and when to intervene when something goes wrong.
The scale of the shift is striking. Machine identities outnumber human employees by roughly 82 to 1 inside the average organization. That ratio does not dilute human responsibility; it concentrates it. As AI systems become more autonomous, human judgment becomes more important, not less.
Employers appear to agree. Analytical thinking is the most desirable core skill employers seek in potential hires, according to a World Economic Forum survey, with roughly seven in ten employers calling it essential. Leaders want workers who avoid blind delegation and know how to challenge AI when it matters. The bottom line is to use AI to make a job more efficient, not to hand over the judgment calls that remain the most important part of the work.
Few organizations are solving AI workforce integration alone. Isolated approaches can be time-consuming and costly, as companies waste cycles rebuilding workstreams that others have already solved. That is where alliances and ecosystems come in, helping organizations build the right foundations to scale technology efficiently.
One example cited is the beverage manufacturer Lion, where collaboration with ecosystem partners led to a 75 percent faster customer response speed and roughly a 30 percent improvement in operating costs across its people function. Rather than building every layer of an AI-enabled people function from scratch, Lion worked with partners who had already solved pieces of the problem elsewhere.
Workflows themselves also need redesign so that humans stay at the center of decision-making and trust is maintained. When hybrid AI-human workflows are built around rigid handoffs, expecting AI to finish its tasks before passing results to a person, the underlying assumption is that tasks can be divided cleanly. In practice, that structure collapses opportunities for meaningful judgment calls and can add more iterative work.
A more durable model organizes work around shared tasks in a truly integrated process, with humans and AI contributing at every stage rather than in sequence. Daikin, a multinational heating, ventilation, and air conditioning company, illustrated this by using hybrid AI and human teams working in tandem to roll out a new enterprise resource planning system. AI assisted with code generation and automated testing, while employees kept oversight for exceptions and high-risk scenarios, reinforcing that judgment is something no algorithm can absorb. The approach accelerated delivery by about 30 percent, and the pilot produced a 10 percent gain in counter efficiency and a 20 percent faster financial close.
Technical skills tied to a specific model version have a short shelf life, the executive notes, while the judgment to know when and how to intervene does not expire. Judgment is also essential to establishing 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. Done well, AI adoption clarifies that role rather than shrinking it.
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