EY
The better AI gets, the more valuable distinctly human skills become, according to Julie Teigland, EY’s Global Vice Chair for Alliances & Ecosystems, who argues the labor market has already started pricing that shift in. “The paradox of the AI era is that the more capable the technology becomes, the more the market rewards the other things. Sought-after skills are intrinsically human — it all comes down to what AI can’t do,” she said.
Teigland’s core argument is that “AI fluency” needs to be redefined. It isn’t enough to simply operate AI tools competently; the real skill is “blending the operation of AI tools with evaluation of outputs for relevance and knowing how (and when) to intervene when things go wrong.” In other words, fluency means knowing when to trust the machine’s output and, just as importantly, when not to.
She pushes back on the fear that AI is simply coming for people’s jobs wholesale. “It doesn’t remove the person, but instead the parts of the job that never required human judgment in the first place,” Teigland said. “What remains is the part only a human can do.” Under that framing, AI strips out the rote, mechanical portions of a role, leaving behind exactly the judgment-heavy work that made the job valuable in the first place, provided workers and companies actually reskill toward that remaining core rather than treating it as an afterthought.
That reskilling gap is costing real money. Teigland points to research showing companies are still missing out on up to 40% of potential AI productivity gains, a shortfall she ties directly to underinvestment in talent development rather than any limitation in the technology itself. Buying the tools, in other words, is the easy part; building a workforce that knows how to supervise and correct them is where most organizations are falling short.
Her broader point cuts against a common assumption that more autonomous AI means less need for human oversight. Teigland argues the opposite: as AI systems take on more decisions independently, the judgment calls about when those systems are wrong, and what to do about it, become more consequential, not less. For leaders, that means AI fluency belongs alongside core leadership skills rather than sitting off to the side as a specialist technical competency, since the ability to evaluate and intervene on AI output is quickly becoming central to what leadership itself looks like.
