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.
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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.
Many teams wrongly focus on the latest models and frameworks. True improvement comes from classic product development: talking to users, preparing better data, optimizing workflows, and writing better prompts.
The path to adopting AI is not subscribing to a suite of tools, which leads to 'AI overwhelm' or apathy. Instead, identify a single, specific micro-problem within your business. Then, research and apply the AI solution best suited to solve only that problem before expanding, ensuring tangible ROI and preventing burnout.
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.
Leadership often imposes AI automation on processes without understanding the nuances. The employees executing daily tasks are best positioned to identify high-impact opportunities. A bottom-up approach ensures AI solves real problems and delivers meaningful impact, avoiding top-down miscalculations.
Instead of focusing on AI features, understand the two mental shifts it creates for customers. It either offers a superior method for an existing, tedious task ("a better way") or it makes a previously unattainable goal achievable ("now possible"). Your product must align with one of these two thoughts.
Teams that become over-reliant on generative AI as a silver bullet are destined to fail. True success comes from teams that remain "maniacally focused" on user and business value, using AI with intent to serve that purpose, not as the purpose itself.
Companies racing to add AI features while ignoring core product principles—like solving a real problem for a defined market—are creating a wave of failed products, dubbed "AI slop" by product coach Teresa Torres.
The promise of AI shouldn't be a one-click solution that removes the user. Instead, AI should be a collaborative partner that augments human capacity. A successful AI product leaves room for user participation, making them feel like they are co-building the experience and have a stake in the outcome.
To build an effective AI product, founders should first perform the service manually. This direct interaction reveals nuanced user needs, providing an essential blueprint for designing AI that successfully replaces the human process and avoids building a tool that misses the mark.
