# What makes an AI product feature worth building? > The economic case should follow a customer job, not a model capability or a competitor announcement. Canonical URL: https://marmation.com/review/ai-product-business-case/ By Vikram Roy · 2026-11-12 An AI feature earns its budget when customers have a reason to keep using it. The investment question is what changes for the customer and for the business once the initial curiosity wears off. 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. ## Start with demand, not a feature announcement 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. ## Put the cost inside the product case Define the user job, competing solution, business model, risk, operating cost and evidence required to validate demand. Include usage charges—the amount of text a model processes, measured in tokens—and the support work each use creates as variable costs. Include account permissions, workflow integration and release maintenance in the build estimate. ## An invoice feature still needs a reason to exist 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. ## A defensive investment can be legitimate 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.” ## Questions for the funding decision 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](). ## Sources - [The state of AI in 2026]() — McKinsey. AI adoption/agentic coding and build-vs-buy survey; 32% of respondents had foregone a product/feature purchase due to ability to build with coding agents. Limit: The findings apply to the survey’s questions and population. They do not establish that software-as-a-service products are obsolete. - [2026 Global B2B Buyer Experience Report]() — 6sense. A 2026 report of thousands of recent B2B buyers; buyers often form preferences before direct seller contact; websites, peers and other sources matter between cycles. Limit: This vendor-led survey covers defined purchasing journeys, not every buyer. An association in the research does not by itself show what caused the outcome. ## Related reading - [AI belongs inside a good product, not beside it](https://marmation.com/review/product-systems/) - [The demo answers. The product has to deliver.](https://marmation.com/review/ai-product-from-prototype-to-production/) - [The best AI interface is honest about uncertainty.](https://marmation.com/review/designing-ai-ux-for-uncertainty/)