AI as Product Strategist

We taught AI how to build. Now we are beginning to teach it what is worth building.

For much of the current AI wave, the focus has been on execution. Can AI write the code? Create the image? Analyse the spreadsheet? Draft the copy? Research the market? Connect the systems? Complete the workflow? Increasingly, the answer is yes. The technical problems are by no means finished, but the direction is clear. AI is making execution dramatically cheaper and faster across software, design, research, content and operations. A capable person with the right tools can already produce things that would once have required a much larger team.

That creates an interesting consequence. As the cost of making things falls, the value shifts upstream towards deciding what should be made in the first place. The harder questions become: which problem is actually worth solving? Which opportunity should we pursue? What should the product do? Which version should we back? What should we change? What should remain untouched? What trade-offs are acceptable? And perhaps most importantly, what should we not do? These are not primarily execution questions. They are product strategy questions.

There is a tendency to describe product management as coordination: gathering requirements, maintaining roadmaps, writing tickets and moving work through a development process. But that misses the most valuable part of the job. A strong product strategist connects a vision to something that can actually be built. They translate an ambition into objectives, product architecture and priorities. They consider alternative routes. They balance evidence against instinct, short-term opportunity against long-term position, customer requests against product coherence, speed against quality, commercial potential against technical cost. And then they make choices.

Good product strategy is therefore not simply a plan. It is a continuous decision system: vision leads to objectives, objectives to architecture, architecture to options, options to trade-offs, trade-offs to decisions, decisions to execution, execution to outcomes, and outcomes back to learning. The loop matters as much as the initial decision. A good product leader does not simply decide once and assume they were right. They watch what happened, understand why, update their model of the problem and make a better decision the next time. Looked at this way, product strategy already resembles the architecture of an agentic AI system. And that may be where the next important frontier lies.

Most current AI interaction still begins with an instruction. Write this. Build this. Summarise this. Analyse this. Fix this. The human has already made the fundamental decision about what needs to happen. AI performs the work. A product-strategy agent requires a very different level of reasoning. Instead of being told to build Feature X, it might be given an objective: increase activation without materially increasing onboarding complexity. Now it has to understand the current product, available evidence, user behaviour, technical constraints and company strategy. It may generate several possible interventions. Then it has to compare them.

Perhaps one creates more upside but introduces substantial engineering work. Another is easy to ship but treats a symptom rather than the cause. A third improves activation but damages retention elsewhere. A fourth requires no new feature at all — only removing friction from something that already exists. The difficult part is not generating those four ideas. The difficult part is deciding which one deserves to happen. That requires judgement.

Generative AI has conditioned us to associate intelligence with producing more. More concepts. More alternatives. More code. More content. More possibilities. Product strategy often requires the opposite. Sometimes the intelligent decision is to preserve almost everything and change one thing. Sometimes it is to reject a superficially attractive opportunity because it pulls the product away from its strategy. Sometimes a customer request should not become a feature. Sometimes an underperforming experience requires a major rethink. Sometimes it requires nothing more than allowing the current idea enough time to produce evidence. And sometimes the correct answer is simply: do not build this.

That is a much harder form of AI than generation. A useful product strategist has to reason about what is fixed, what is flexible and what is actually causing the problem. It needs to understand that improving one objective can damage another, and that the highest numerical optimisation is not always the strongest strategic choice. In other words, it has to understand trade-offs.

This is where things become particularly interesting. Once an AI system is making decisions rather than merely executing instructions, there may no longer be one objectively best version of it. Imagine two highly capable AI product strategists looking at exactly the same opportunity. One has a strong bias towards rapid experimentation. It recommends shipping the smallest reversible test and learning from real behaviour. Another prioritises product coherence and believes the proposed feature creates architectural debt. It recommends waiting and solving the underlying problem properly. Neither is necessarily wrong. They have different philosophies.

One AI might systematically favour differentiation over market conformity. Another might favour margin over growth. One might tolerate significant technical debt in pursuit of speed. Another might be unusually conservative about platform architecture. One could be excellent at zero-to-one product creation because it places more weight on novelty and learning. Another might be exceptional at mature-product optimisation because it prioritises statistical confidence and incremental efficiency. Another might be particularly suitable for regulated environments because its threshold for uncertainty and risk is much higher. At that point, what we currently think of as model behaviour begins to resemble product leadership style. We may end up talking about AI product strategists in remarkably similar ways to human ones: that one is excellent at 0→1; that one is commercially aggressive; that one sees second-order effects incredibly well; that one over-optimises; that one protects the long-term product better; that one is brilliant at identifying when we should stop building.

There may never be one best AI. It would have been reasonable to expect AI to become winner-takes-all. Intelligence benefits enormously from scale, so perhaps eventually one model would simply become better than everything else and everyone would use it. Yet that is not how the market currently feels. People already move between different general-purpose models. Developers use different coding agents for different kinds of work. Creative professionals switch between image, video and design systems. And sophisticated users sometimes use several competing systems in parallel even when their headline capabilities overlap considerably. That is interesting because AI was supposed, in one sense, to commoditise capability. Instead, it may be creating another kind of differentiation.

Two systems can both be extremely capable yet feel meaningfully different in how they reason, communicate, prioritise and respond to ambiguity. That difference may become even more important as AI moves into strategy. Execution often has a relatively observable answer: the code works or it does not. Strategy rarely does. It involves values, time horizons, uncertainty, opportunity cost, organisational appetite and competing definitions of success. Different systems can therefore reach different conclusions without one of them necessarily being defective.

This is perhaps the deeper point. Once an AI is balancing several legitimate objectives, its judgement necessarily reflects priorities. Suppose a company is considering a new product. The evidence suggests strong demand, but the idea weakens brand differentiation. It could generate substantial revenue, but it creates operational complexity. It serves current customers, but it moves the company away from the market it wants to own in five years. What is the "correct" answer? There isn't one until someone determines how those objectives should be valued. That is strategy. An AI can help make those priorities explicit. Eventually it may learn them from an organisation's history: what leaders approved, what they rejected, which departures succeeded, where they repeatedly regretted compromising, and which decisions ultimately created value. But even then, the machine is not discovering some universal objective truth. It is developing a model of what good judgement means in this particular context. That is why I suspect the future of product-strategy AI will be more pluralistic than people expect. Different companies may need different strategic intelligences. And the same company may want different ones for different decisions.

None of this means that the CPO or product strategist disappears. If anything, it clarifies what their highest-value work actually is. Humans increasingly move from producing every artefact themselves towards defining the system within which decisions should be made. What are we trying to become? What are we unwilling to compromise? What constitutes success? Which risks are acceptable? Where should AI have autonomy? Where should it abstain? Which decisions require human judgement regardless of confidence? And when the evidence contradicts the organisation's existing assumptions, who has authority to change them? Those are governance questions, but they are also product questions. The senior product leader becomes partly the architect of the organisation's decision intelligence.

We are already moving from copilots that help people perform tasks towards agents that take responsibility for outcomes. The next step is not difficult to imagine. AI systems will increasingly help organisations decide what initiatives to pursue, which products to build, where products are weak, what interventions have the highest leverage, which bets deserve investment and which should be stopped. And crucially, they will be able to observe what happened afterwards. That closes the loop. A system that remembers the context, recommendation, human decision, intervention and eventual outcome can learn something much more valuable than a static best practice. It can begin to learn which kinds of decisions work here.

That is where AI stops being merely an execution engine and begins to resemble a product strategist. And it may also be where some of the most defensible AI products are built. Because when execution becomes abundant, judgement becomes scarce. The first era of generative AI was dominated by the question: how much of the work can machines do? The next may revolve around a harder question: how much of the judgement can they help us improve? We have spent the first era of AI teaching machines how to execute our decisions. The next era may be about teaching them how to help us decide what is worth doing in the first place.

And if that happens, the defining question may not be which AI is smartest. It may be: which AI has the right product judgement for the problem we are trying to solve?