AI is on my company's strategic agenda. How on earth do I build that into our bonus plan?

Short answer - carefully and in a well thought through way.

Longer answer - so that the bonus plan drives AI adoption and business value, not just more activity or “box-ticking.”

Because this is the real risk. A lot of organisations are rushing to “include AI” in their bonus plans without really thinking through what they are trying to achieve. Which means that instead of accelerating meaningful change, the bonus plan can end up rewarding noise over impact.

The best approach is to link a small part of the bonus to clearly defined AI outcomes, with strong guardrails around quality, risk, and fairness.

In some ways, AI is just another objective, and all the expertise that the Reward Heads team have on designing robust bonuses comes into play. But there are some specifics around how AI is evolving, how you measure the success of AI, and how you target when you have no history.

In practice, if you treat AI like any other metric without adapting your approach, you risk setting measures that are either meaningless or unachievable.

Is anyone actually bonusing on AI?

Well yes. Almost half of FTSE100 companies now have Chief AI Officer (or similar) roles. How are they being remunerated? And scanning company Annual Reports shows more and more strategies include AI, and we are starting to see it in bonus scorecards.

So, AI is no longer a “future concept”. It is already shaping how performance is being defined and rewarded at the most senior levels. And once it appears at Exec level, it is unlikely to stick just there. It will likely cascade down the organisation over time.

Where to start - begin with the end in mind

Start by defining what AI means for your business: Is it efficiency, growth, service improvement, better decision-making, or new products? Then translate that strategy into a small set of measurable objectives that can sit in the bonus plan, such as use-case adoption, time saved, revenue created, cost avoided, or customer impact.

If you skip this step, this carries substantial risk. You end up incentivising activity without clarity on what success actually looks like. And this is the problem - teams can hit their “AI targets” while the business sees little to no real benefit.

As with all bonus schemes, the company, team and individual measures need to align, but may not be the same. For example:

A company-wide measure tied to AI progress and overall business performance.
A team or function measure tied to delivery of agreed AI use cases (e.g. launching a compliant AI workflow that reduces handling time by 15% and improves customer satisfaction, rather than simply “using AI more”).
A limited individual measure tied to role-relevant behaviours, skills, or contribution.

That distinction matters. Because rewarding volume instead of outcomes is one of the fastest ways to undermine both performance and trust.

What to measure

Good measures in the AI space usually fall into three buckets:

Adoption: trained users, active users, approved use-case completion, certified capability.
Value: productivity gains, cost savings, revenue uplift, quality improvements, cycle-time reduction.
Risk control: policy compliance, governance, accuracy, audit results, responsible-use checks.

Avoid rewarding raw usage alone. Why? Because people respond exactly to what you measure.

If you pay for usage, you will get usage, regardless of whether it adds value. Which means you can inadvertently encourage low-value activity, duplication, or even risky “shadow AI” behaviour.

“Incentives work best when they combine adoption with evidence of business impact and good governance”

Design choices

Keep the AI element modest at first, especially if the organisation is still learning. A phased approach works well:

Start with recognition, spot awards, or pilot bonus pools. Then move to harder performance measures once the data and governance are reliable.

You also need to decide whether AI sits inside the existing bonus plan or as a separate initiative. This is more important than it sounds. Trying to force AI into an already complex scheme can create confusion.

In real terms, this results in employees becoming confused about what they are being paid for. And when that happens, the motivational value of the bonus drops significantly.

Key risks

The main risks are gaming, unfairness, and unintended consequences - as with any measure. If people think the bonus is based on opaque assessments, the plan can quickly lose credibility. This is particularly prevalent in professional or knowledge-work settings.

And once trust in the scheme is lost, it is incredibly hard to get back.

There are also legal and ethical considerations:

Discrimination risk if measures disadvantage certain roles or working patterns
Data privacy concerns if usage is tracked too heavily
Regulatory and reputational risk if incentives encourage unsafe AI use
Manager bias if subjective judgments are not well calibrated

This means AI bonuses are not just a reward question, they are a governance and culture question too.

Practical checklist

Before launching, check that you have:

A clear AI strategy and use-case roadmap
Defined, measurable KPIs for adoption, value, and risk
A governance process for approved tools and acceptable use
A communication plan so employees understand what is being rewarded
A review cycle to test whether the plan is changing behaviour in the right way

Miss one of these, and the scheme can quickly become performative rather than effective.

The real question

For most companies, the right question is not “How do we pay people for using AI?” It is “How do we reward people for creating value with AI appropriately?”

That framing usually leads to a much better bonus design. Because successful plans reward outcomes, not just activity, and include guardrails for quality, compliance, and fairness.

Common pitfalls

The biggest pitfall is rewarding usage instead of value. Which means you get more AI… but not necessarily better outcomes. In some cases, you may even increase risk while believing you are driving innovation.