EY's human-skills bonus rewards AI adoption

“Bonuses for skills AI can't do” is a clean headline. It is not a clean description of EY's new $100 million rewards programme.

EY's August 31 release says the programme recognises business acumen, judgment and adaptability. It also says, just as plainly, that it rewards “technology adoption” and people who harness advanced technologies. Its three categories progress from learning and experimentation, to measurable results through innovation or technology, to long-lasting material impact.

So this is not a human-versus-AI bonus. It is an adoption incentive with a human acceptance layer: use the technology, apply judgment, then make the result matter. That is a much more useful pattern for an enterprise AI programme than the headline it produced.

The headline cut the programme in half

I opened EY's release expecting to correct another employer paying people to preserve an anti-AI moat. The words “technology adoption” changed the article. They sit in the programme description, not in a footnote, and the second award category explicitly includes contributions through technology.

The human skills still matter. Accounting work needs context, judgment, explanation and responsibility precisely because a generated answer can be fluent and wrong. But EY is not rewarding those skills instead of AI use. It is rewarding the pairing: experimentation without judgment is noise; judgment without trying the new tool preserves the old throughput.

That pairing also fixes a common adoption mistake. Klaviyo's L3 mandate made agent fluency non-optional, but fluency alone cannot show whether accepted work improved. EY's categories at least point from behaviour toward outcome. The public release does not disclose how those outcomes will be measured, which is where the programme will either become an operating system or a nomination campaign.

A $100 million pool is not a unit price

The headline number looks precise while leaving the economics mostly unknown. EY does not state the programme period, US headcount, expected number of recipients or distribution across the three categories. It also does not say whether the full amount is incremental spend or replaces an existing rewards budget.

Fortune reported, citing The Wall Street Journal, spot awards up to $500 and awards up to $25,000 for material impact. Those bands are useful context, but they do not appear in EY's own release. Dividing $100 million by an estimated workforce would create a per-employee number the primary source cannot support—and would hide a deliberately skewed discretionary pool anyway.

The pool therefore tells us commitment size, not incentive strength. To understand the signal an employee receives, we would need the probability of winning, the evidence required and whether teams that build reusable infrastructure are rewarded alongside the visible person who uses it.

Reward the conversion, not the activity

For this article, I ran EY's three categories through the same ledger I use when reviewing enterprise AI adoption: adoption act → human judgment → accepted outcome. The progression is sensible. What remains missing is the receipt at the last arrow.

Steal this for an internal recognition programme. Require every nomination to name four things:

1. the workflow and the technology behaviour that changed;
2. the decision where human judgment altered or rejected the machine's output;
3. the accepted business unit—closed case, approved analysis, retained code change or won proposal;
4. the before-and-after cycle time, rework, quality or risk result.

An earlier AndyMental Shortlist shows the failure mode at full volume: a reported Meta token-usage leaderboard made consumption the target, while its CTO later said token usage alone was not impact. This clip is a companion on incentive design, not evidence about EY's rollout.

Do not pay for prompts written, sessions opened, tokens consumed or artifacts generated. Those are activity counters, and bonuses will make people optimise them. Reward the team that encodes the acceptance standard, the reviewer who catches the costly failure and the operator who turns the experiment into repeatable work. Otherwise the visible adopter captures the award while the invisible control plane remains volunteer labour.

EY's programme is interesting because it refuses the false choice the headline creates. Human judgment is not the shelter workers retreat to as AI advances. It is the mechanism that converts adoption into work an organisation can accept—and it should be measured at that conversion.

Reward technology adoption and human judgment as a pair; pay only when the pair produces accepted work.