AI Product Strategy: Identifying Real Problems Before Building AI Solutions
Start with customer pain points, not AI technology. Solve meaningful problems first, then determine whether AI creates measurable value.
One of the most common mistakes in AI initiatives is starting with the technology instead of the problem.
The excitement around AI often leads teams to ask questions like, "Can we build an AI agent for this?" or "How can we apply AI to this process?" While well-intentioned, these questions can lead organisations down the wrong path. The result is often a technically impressive solution searching for a problem to solve.
A more effective approach is to start with the customer, user, or business pain point.
What is causing friction today? What task consumes unnecessary time? Where are decisions delayed because information is difficult to access? Which processes rely heavily on manual effort or tribal knowledge? These are often the opportunities worth exploring before discussing models, prompts, or agents.
In my experience, successful AI projects rarely begin with an AI objective. They begin with a clearly defined problem. AI simply becomes one of several tools available to address it.
For example, if customers struggle to find relevant information, the problem is not the absence of AI. The problem is poor knowledge accessibility. If finance teams spend hours processing documents, the problem is operational inefficiency. AI may help solve these challenges, but it should not define them.
Before investing in any AI initiative, I find it useful to ask three questions:
What specific pain point are we solving? How does this problem impact customers, employees, or business outcomes? Why is AI the most appropriate solution compared to process improvements or traditional automation?
The answers create clarity and alignment across product, engineering, and business stakeholders.
The strongest AI strategies are not built around technology trends. They are built around real problems, measurable outcomes, and meaningful customer value.
Key learning: Avoid "AI-first" thinking. Start with customer pain points, understand the root cause, and then determine whether AI is the right tool to create measurable impact.