A company can say almost anything about its priorities in a press release. The bonus plan is where it has to choose. Compensation is one of the few places in a business where a stated value becomes arithmetic, which is why I pay attention when a company moves a subject into that calculation. Telling executives that AI matters is one thing. Making it part of how they get paid puts the subject among the decisions they are expected to take seriously. Ralph Lauren has done that, and I think the most useful way to read the decision is as an investment in getting people ready for a different way of working.
An October 15 Reuters report brought renewed attention to the policy, though the plan itself was already public; an Equilar review of AI performance metrics noted it months earlier. The company’s 2026 proxy statement describes an AI scorecard replacing citizenship and sustainability metrics in the strategic modifier for its fiscal 2026 annual incentive plan. Financial measures remained central. The AI measures covered training completion, including information security training that incorporates AI, and employee surveys about training and enablement. The modifier excluded Ralph Lauren himself.
The company reported exceeding its targets and approved a 10% upward modifier. For applicable executives, the formula moved from 197% to 216.7% of target, subject to a 200% cap. Those details matter because this was a bounded part of the compensation system, and the scorecard measured preparation rather than demonstrated AI productivity gains. Ralph Lauren is also early rather than alone. Reuters, citing Conference Board and ESGAUGE data, reported that about 12% of S&P 500 companies disclosed AI-related goals in executive incentive plans this year, up from 5% in 2024. A broader Pearl Meyer review of 2,500 proxy statements put the share across public companies at about 2%.
I do not see the focus on preparation as a reason to dismiss the policy. Implementation takes time. Before a company can reasonably expect people to redesign their work, it has to give them an opportunity to understand what the tools can do. Compensation can turn that preparation into a leadership priority rather than an optional lunch-and-learn. What happens after the training is where the larger business question begins.
People take the same introduction in different directions
I have sat down with employees and shown them ways to use AI, and the responses vary. Some are interested but unsure where to start. Some learn the specific thing I demonstrate and stay there. Others resist the idea because they see it as a threat to their employment. Giving everyone access to the same tool does not produce the same willingness or ability to use it.
What I look for is a sign of initiative. If I show someone how to generate an image, I want them to become curious about what else that capability can do. How could it fit into their work? What are its limitations? Can they develop an application I had not thought to show them? That is the difference between understanding a demonstration and beginning to capitalize on the opportunity.
I have found peers elsewhere in the company who share that appetite for exploration. We compare problems, exchange discoveries, and help each other understand what works. Sometimes I describe a problem and someone has already solved it. Sometimes they have never encountered it, but the conversation gives them a useful warning for later. The learning continues because we want to keep pushing.
I have not yet seen that same level of independent expansion within my own department, and I want to be clear about it. I am describing what I am looking for, not claiming that training has already transformed the team’s productivity. I also cannot reasonably expect everyone to share my intensity. I spend time building automations and exploring what agents can do because I enjoy it, which is not a reasonable job requirement. For someone else, a much smaller improvement in a recurring task could be a meaningful beginning.
Leadership has to make the possibilities visible
I do not know how to manufacture curiosity. My approach is to lead through examples and give visibility to useful things other people create: show what is possible, explain how it helps, and make the first step approachable. A presentation announcing that the company is embracing AI, by contrast, leaves employees with a great deal to figure out on their own.
I usually encourage people to begin by using AI regularly as one source of information among several, then compare its answers with research and evidence. They need experience with where it performs well and where it falls short. As that judgment develops, they can start involving it in parts of their everyday work and expand from there.
That is how I have approached my own use. I have gradually brought in assistance with catching up on email, prioritizing the day, preparing responses, and organizing project assignments. Familiarity creates opportunities to do more. It also gives me a better sense of which tasks still require close attention.
This is why I support rewarding leaders for creating the conditions for adoption. They can demonstrate, provide access, encourage experimentation, and recognize useful progress. They cannot guarantee that every individual will develop the same curiosity. I would judge whether a leader has made a serious effort to enable people, while recognizing that each employee has a part to play in what follows.
The person still needs to judge the work
The kind of dependency that worries me is when someone delegates work they cannot evaluate. In creative work, an image can look impressive while failing to communicate the intended idea. The person directing it needs enough understanding of design, the audience, and the brand to recognize the problem and explain what should improve.
That judgment is part of what the employee contributes. AI can give someone more options, reduce production effort, or help explore an approach. The person still needs to know what good looks like. Otherwise we have only made it easier to produce work without establishing whether the work deserves to be used.
This is also how I think about productivity. More output becomes valuable when it is useful output, and the time spent checking, correcting, and integrating the result belongs in the comparison. If a process produces twice as much material and creates twice as much cleanup, I want to understand what the company has actually gained. A persuasive demonstration is a reason to test the workflow, not the final accounting of its value.
Clear gains change the expectations of a job
I would evaluate adoption case by case. Someone can produce excellent work in an area where I do not see a useful role for AI, and that performance can be enough. Most jobs, though, contain several kinds of work. Creative judgment may be difficult to accelerate while a recurring administrative step is relatively easy to improve.
If we establish that a task can be completed in less time with equivalent quality, including the effort required to review it, I would question a refusal to use that method. The company is paying for someone’s time and ability. Reducing unnecessary effort gives that person more capacity to apply their ability elsewhere. Once the benefit is demonstrated, continuing the slower process needs an explanation.
That can create uncomfortable comparisons. Another employee may bring similar expertise while using AI to handle more work reliably, and from a business perspective that difference matters. I tell people to learn these tools because I want them to strengthen what they can offer. Someone who uses AI well may become a more compelling choice for the same role. Pretending that possibility does not exist would not help the people I am trying to prepare.
The reward should follow the value created. If an employee develops a reliable way to increase useful capacity enough that the team can handle growth without another hire, that deserves recognition and potentially a bonus. The employee has contributed something beyond completing a course. They have improved what the company can accomplish with its resources.
In Bigger, Smarter, Stronger Brands, I describe incentives as a form of organizational nutrition. The argument goes beyond compensation: people also need opportunities to develop mastery, autonomy to apply what they learn, and recognition when that work creates value. That helps explain both the promise and the limit of Ralph Lauren’s approach. Rewarding executives for training and enablement can establish the conditions for progress. Whether those conditions produce useful capability depends on what employees are able and encouraged to do afterward. Completing the training is a beginning; the organization still has to make room for the learning to become useful work.
Ralph Lauren’s scorecard places an incentive at an earlier stage of that progression, and I think that is a reasonable place to begin. Training gives more people a chance to discover useful applications, and executive attention can help make the opportunity real. The eventual test will be what employees learn to do with it, how reliably they do it, and whether the company becomes more capable as a result. A bonus plan reveals what a company is willing to pay for. For now, Ralph Lauren is paying for readiness. The companies that get the most from AI will be the ones whose incentives keep moving, from preparation to useful work to measurable capacity, as their people do.