Don't let the novelty of GenAI distract you from product management fundamentals. Before exploring any solution, start with the core questions: What is the customer's problem, and is solving it a viable business opportunity? The technology is a means to an end, not the end itself.
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Before launch, product leaders must ask if their AI offering is a true product or just a feature. Slapping an AI label on a tool that automates a minor part of a larger workflow is a gimmick. It will fail unless it solves a core, high-friction problem for the customer in its entirety.
Don't evaluate your team's AI readiness as a standalone capability. True AI strategy requires a deep understanding of customer problems and unique value. Without strong core product competencies, AI adoption is merely tactical, not strategic.
While customer feedback is vital for identifying problems (e.g., 40% of 911 calls are non-urgent), customers rarely envision the best solution (e.g., an AI voice agent). A founder's role is to absorb the problem, then push for the technologically superior solution, even if it initially faces resistance.
In AI, low prototyping costs and customer uncertainty make the traditional research-first PM model obsolete. The new approach is to build a prototype quickly, show it to customers to discover possibilities, and then iterate based on their reactions, effectively building the solution before the problem is fully defined.
The traditional SaaS method of asking customers what they want doesn't work for AI because customers can't imagine what's possible with the technology's "jagged" capabilities. Instead, teams must start with a deep, technology-first understanding of the models and then map that back to customer problems.
Beyond just using AI tools, the fundamental process of product management is evolving. For every new initiative, PMs must now consider the appropriate level of AI, automation, or customization. This question is now as critical as "what problem are we solving?" and addresses rising customer expectations for adaptive products.
In the rush to adopt AI, teams are tempted to start with the technology and search for a problem. However, the most successful AI products still adhere to the fundamental principle of starting with user pain points, not the capabilities of the technology.
A simple but powerful framework for any product initiative requires answering four questions: 1) What is it? 2) Why does it matter (financially)? 3) How much will it cost (including hiring and ops)? 4) When do I get it? This forces teams to think through the full business impact, not just the user value.
The rapid evolution of AI makes traditional product development cycles too slow. GitHub's CPO advises that every AI feature is a search for product-market fit. The best strategy is to find five customers with a shared problem and build openly with them, iterating daily rather than building in isolation for weeks.
A common marketing mistake is being product-centric. Instead of selling a pre-packaged product, first identify the customer's primary business challenge. Then, frame and adapt your offering as the specific solution to that problem, ensuring immediate relevance and value.
