The numbers come first. EY Global Vice Chair Julie Teigland says companies are missing out on up to 40% of AI productivity gains because of gaps in talent investment.
In a Fortune commentary, Teigland argues that AI is strongest at repetitive tasks and surfacing data, but humans still set the goal, provide context, and stay accountable for the result. She says the paradox of the AI era is that 'the more capable the technology becomes, the more the market rewards the other things.'
Teigland says AI fluency now has to mean more than operating tools. It also means evaluating outputs for relevance and knowing when to intervene when things go wrong. She says machine identities now outnumber human employees 82:1 inside the average organization, but that scale does not reduce human responsibility.
The EY executive cites a World Economic Forum survey saying analytical thinking is the most desirable core skill employers look for in potential hires, with roughly seven in ten employers calling it essential. She says leaders want employees who avoid blind delegation and can challenge AI when it matters.
Teigland also points to company examples she says show what happens when AI and human oversight are integrated. She says EY's work with beverage manufacturer Lion led to 75% faster customer response speed and roughly 30% improvement in operating costs across its people function. She says Daikin used hybrid AI and human teams to roll out a new enterprise resource planning system, with AI helping with code generation and automated testing while employees handled oversight for exceptions and high-risk scenarios.
According to Teigland, that approach accelerated delivery by approximately 30%, produced a 10% gain in counter efficiency, and a 20% faster financial close.
The larger point is not that AI removes the worker. It strips away work that never needed human judgment in the first place and leaves the decision-making where it belongs. Capital rewards clear rules, and AI is no exception: the technology can process, but it cannot own responsibility.
For companies, that means the real bottleneck is not software. It is whether management has the discipline to build a workforce that can use AI without surrendering judgment to it. The firms that get this right will turn technology into leverage. The ones that do not will keep paying for tools they never fully use.
