Four Signs AI Is The Wrong Tool For Your Product

**Hook:** The rush to embed artificial intelligence into every product feature is creating a wave of overengineered, underused tools that frustrate users and drain budgets.

**Lead:** In a June 2026 Forbes Tech Council article, product strategist and tech council member John A. De Goes outlines four unmistakable signs that AI is the wrong tool for a product, warning founders and product managers that AI-for-AI's-sake can kill user adoption and inflate costs without delivering real value.

**Context:** The AI boom has spurred a gold rush mentality: startups and enterprises alike are layering machine learning onto everything from to-do lists to coffee machines. Yet a growing body of evidence suggests that many AI features go untouched. Gartner reported in 2025 that nearly 40% of AI-enhanced product features are rarely or never used by customers, often because they solve problems customers didn't have. The Forbes piece taps into this disillusionment, offering a practical checklist for when to dial back the ambition.

**Key Details:** De Goes identifies four red flags. First, if the core user need is simple and deterministic (e.g., a calculator or a calendar reminder), adding AI introduces brittleness and latency. Second, when the cost of training, deploying, and maintaining a model exceeds the marginal benefit—especially for small teams—AI becomes a liability. Third, if the product's data is sparse, biased, or unlabelled, any model will perform poorly, eroding trust. Fourth, when users explicitly want control and transparency (e.g., in financial or medical contexts), a black-box AI recommendation undermines confidence. The article cites examples from failed 'smart' features in consumer apps that were quietly removed after user backlash.

**Analysis:** The piece reflects a maturing industry mindset. Venture capitalists and product advisors are increasingly asking 'Should we use AI?' rather than 'Can we use AI?'. The caution echoes earlier waves of blockchain and IoT hype, where technology-driven product decisions led to market failures. Industry analysts note that the most successful AI products—like Grammarly or Spotify—solve clear pain points with narrow, well-scoped models. The four signs framework helps teams avoid the trap of 'solutionism' where technology chases a problem.

**Outlook:** As generative AI becomes cheaper and more commoditized, the differentiation will shift from *having AI* to *using AI intelligently*. Product teams that rigorously audit their AI product fit will outperform those that treat AI as a default. Expect more frameworks and thought leadership around AI pruning—removing features that don't pass the smell test. The lesson from De Goes: sometimes the best feature is the one that does not need AI at all.