What makes an AI product feature worth building?

A software team sees competitors adding AI to their product pages. It chooses a broad assistant feature because it seems necessary for parity. After months of work, customers try it once and continue their old workflow. The model performed well; the business case was weak.

The decision behind the technology

The strongest opportunity usually starts from a task customers already perform frequently, find expensive or value enough to pay for. AI should change the outcome or the work required. Adoption and revenue effects must be measured against the actual customer journey.

A better way to frame the work

Define the user job, competing solution, business model, risk, operating cost and evidence required to validate demand. Treat model tokens and support load as variable costs. Include account permissions, workflow integration and release maintenance in the build estimate.

Put the idea under realistic pressure

Consider an invoice review feature. It may reduce time for one user, but the product owner should also ask whether the feature creates a new paid tier, improves retention or merely increases the vendor cost base. Test both task value and willingness to use or pay for the new workflow.

When the recommendation changes

A product may need a low-margin feature to defend platform relevance. Not every capability must generate direct revenue. But the rationale should be explicit rather than hidden behind “strategic AI.”

What to decide before moving forward

Before funding production, ask: Who uses it? What task improves? What does the customer do today? What evidence proves demand? What changes in product economics? Who owns the feature after launch?

References and context

McKinsey, 2026 State of AI; 6sense B2B Buyer Experience 2026.