Most AI Features Fail Because They Solve No Real Problem, Experts Warn

A product engineering analysis published on DEV Community argues that most AI features added to products are removed within a year because they are built to appear modern rather than to address genuine user needs. The piece advises teams to first identify where users lose time or get stuck, then assess whether a language model can meaningfully help, rather than starting with the technology and searching for a use case. Strong candidates for AI integration include tasks involving unstructured text, tolerant-of-error outputs, and repetitive manual work such as summarisation, drafting, or data extraction. The article cautions against using AI for tasks requiring exact, guaranteed answers, noting that simpler tools like regular expressions or database queries are faster, cheaper, and more reliable for deterministic work. It recommends a tiered approach — starting with basic prompting before considering retrieval-augmented generation or fine-tuning — and states that recognising when AI is unnecessary is itself a mark of engineering maturity.