What AI Incentives Reveal About a Company’s Strategy

Every board is talking about AI. The harder questions are more specific: What is the company trying to accomplish with it, who must deliver that value and should executive pay reinforce the effort?

A technology company developing foundational models faces different challenges than a manufacturer using AI to improve operations, a financial institution introducing AI into customer service or a health care organization redesigning administrative work.

Therefore, boards need to resist treating AI as a single strategy or assuming that a compensation practice adopted by one type of company will translate easily to another. The right incentive approach begins with understanding how AI creates value for that organization.

Our recent research on AI talent markets and executive incentive practices connects three issues boards are often grappling with: how AI investments create value, which talent is required to deliver it and whether executive incentives should reinforce that work.

One Technology, Three Value-Creation Models

Our research suggests that companies generally approach AI strategy and talent through one or more of three models.

Frontier AI creators build foundational models, platforms and infrastructure, relying on scarce researchers and technical leaders whose work can materially affect enterprise value. They may look to technical breakthroughs or platform performance as measures of progress and may rationally concentrate extraordinary compensation on a small number of critical talent.

AI product builders apply AI to products, services and customer experiences, requiring people who can translate technical capability into commercially useful solutions. They may emphasize development milestones, customer adoption or commercialization, and selectively pay premiums for applied AI and product talent.

AI-enabled operators use AI to improve the enterprise itself through productivity, efficiency, workforce leverage, service and decision-making. They are more likely to focus on deployment, workforce adoption, process improvement and, eventually, measurable productivity or cost outcomes. These companies may derive more value from transformation leaders and existing employees who can translate AI capabilities into better products, processes and financial outcomes.

This framework gives directors a more useful starting point than asking whether the company is using AI.

What Early Adopters Are Measuring

Our proxy review of 2,500 companies found that only 65 (roughly 2.6%) disclosed incorporating AI into executive incentive plans. Among the 65, only 14% used an explicit AI metric. Most included AI within a broader strategic goal or an individual executive assessment.

The more revealing finding emerges when those 65 companies are viewed through the three value-creation models. In that framework, AI-enabled operators are emerging as an important source of formal AI incentive measures, with disclosures tending to emphasize transformation, adoption and efficiency. Executives are being held accountable for enterprise-wide change, rather than solely for AI-specific revenue or commercialization.

That pattern may also indicate where incentive design is headed. Our updated analysis also found that roughly 60% of the companies with AI-related incentive objectives have initiatives that could reasonably be connected to identifiable financial or operating outcomes. They may not measure AI return on investment today, but this suggests that, as AI strategies mature, many incentive measures could evolve from adoption and implementation toward measurable business impact.

From Activity to Value

That evolution—from implementation to outcomes—is where incentive design becomes more difficult. Before adding explicit AI-related metrics, boards should first consider whether AI’s benefits are already captured elsewhere in the incentive plan. If AI improves revenue, margins or earnings, a separate measure may reward the same outcome twice. In other cases, a targeted measure may be useful when the organization must build capabilities today to produce financial value later.

AI performance measures may need to evolve with the strategy itself: from capability building to adoption to business impact and, ultimately, to measurable financial outcomes. Early in the journey, companies may appropriately focus on deploying tools, establishing governance, training employees, redesigning workflows and encouraging adoption.

The challenge is that implementation does not necessarily demonstrate impact. A company can deploy an AI platform without employees using it meaningfully, or increase utilization without producing better customer outcomes, lower costs or greater productivity. Measures that appropriately reward early capability-building may therefore become less useful as the strategy matures.

There is no universal definition of successful AI performance. Over time, it is important to align incentives more closely to value creation. An AI-enabled operator might emphasize operating leverage, productivity, cycle-time improvement or increased capacity. An AI product builder may focus on product milestones, customer adoption or commercialization. A frontier AI creator could place more weight on technical progress, platform performance or critical talent outcomes.

The risk is allowing an incentive to continue rewarding activity after the organization should be demonstrating impact.

Five Questions Every Board Should Ask

Taken together, the research suggests that compensation committees need to understand how their company is actually using AI and whether their incentive design supports the company’s strategy, before adding AI-specific metrics to the incentive plan. Directors can use these questions to guide the discussion:

1. How is AI expected to create value for this company?
2. Who is accountable for delivering that value?
3. Are we rewarding capability-building, adoption or measurable business impact?
4. Can AI-related performance be measured credibly?
5. Are AI-related results already captured through existing financial, operational or strategic metrics?

AI should not appear in an executive incentive plan simply because it dominates boardroom discussions or is popular in the market. The decision must follow from a clear view of how AI will create value, who is accountable for delivering it and whether current measures already capture the expected results.

For boards, ultimately, the incentive plan should reinforce the value the company expects AI to create, whether through a specific AI measure or existing business metrics.

A version of this article originally appeared on www.directorsandboards.com.