Before You Build an AI App, Define the Business Problem

**Part 1 of 3 · The business responsibility behind AI-built apps** Before you build an AI app in an afternoon, consider the long-term commitment it may create for your business. Before you celebrate how quickly it works, be clear about the problem it solves and what you are taking on. This article was inspired by the discussion in Ctrl Alt Dev Episode 38, hosted by [Sean Sale](https://www.linkedin.com/in/seansale/) and [Paul Rhodes](https://www.linkedin.com/in/ukwebdevelopment/). Credit to both hosts for exploring the responsibilities behind AI-built software. The business recommendations here are Smart AI Studio’s perspective. [Listen to Episode 38 — AI Wrote the App: Now Who’s Responsible For It?](https://directory.libsyn.com/episode/index/show/8e658e4a-8560-4204-9ebd-6f1db1b6ade2/id/43072413) ## A useful opportunity for business owners If you have ever waited months to test a business idea, the attraction of AI-assisted app building is easy to understand. You can describe a problem, create a working prototype and put something tangible in front of the people who will use it. That creates an opportunity for SMEs. A booking process, an enquiry tracker or a tool that pulls scattered information together can be explored without committing immediately to a large development project. You can learn whether the idea makes sense while there is still time to change direction. The danger is that the speed of building starts to drive the decision. A polished screen feels like progress. More features feel like more value. Neither tells you whether the business has improved. ## Build the AI app business case around the work At Smart AI Studio, we start with the problem. Where is time being lost? What causes rework? Which decisions are delayed because information is missing? Who experiences the problem every day? Consider a business owner who wants a custom quotation app. The team currently copies details between emails and spreadsheets, so an app seems an obvious answer. But a conversation with the people doing the work might reveal that the main delays come from incomplete enquiries, unclear approval limits and inconsistent pricing rules. An app could help collect the information and route approvals. It will still need the business to agree what information matters, who makes decisions and which pricing rules apply. Automating an unclear process can spread its confusion more quickly. Before you build an AI app, that is the start of a sound business case. Write down the intended improvement before choosing a builder. For example: reduce the time spent chasing missing enquiry details, while keeping pricing approval with the appropriate manager. That gives you something useful to test. ## Use a prototype to learn A minimum viable product, or MVP, is an early version that lets you test the core idea. Its value comes from what you learn. Can staff use it without extra explanation? Does it remove work? Where does it create another task? Test with a small group and appropriate sample data. Watch how people actually use it. If they keep returning to the spreadsheet, find out why. They may need a feature you missed, or the app may be solving the wrong part of the problem. Keep the trial bounded. Agree who can use it, what it can access and when you will review the results. An experiment can quietly become a business system when people begin depending on it, even if nobody formally approved that change. ## Look beyond the initial build cost A cheap build does not establish that a custom app is the best investment. Your AI app business case should include the alternatives. Compare it with improving an existing system, buying a suitable product or simplifying the process itself. Include the time needed to test and support it, platform subscriptions, specialist review and future changes. Also ask what happens if the person who built it is unavailable. These costs affect whether the app releases capacity or adds another responsibility to an already stretched team. A custom app may still be the right choice. It can fit a specific workflow that standard software handles poorly. But that choice should follow a clear business case, with someone responsible for its continued usefulness. ## What the direction of travel means for SMEs My view is that easier app building will give more business owners the ability to test ideas for themselves. That makes good judgement more valuable: understanding the process, involving staff and knowing when experienced help is needed. The sensible next step is to choose one problem, define a measurable improvement and test the smallest version that could deliver it. Decide what evidence would justify investing further, and what would persuade you to stop. None of this is meant to put you off building an app or tool with AI. The right tool can solve a real business problem and make work easier. The point is to be clear about the value it should deliver, what you are committing to beyond the first build, and who will support and maintain it as your needs change. Before you build your next app, can you explain the business problem it will solve and how you will know it has helped? Next in the series: [AI App Production Readiness: From MVP to Launch](/insights/ai-app-mvp-to-production/) explores what needs to happen before staff or customers rely on it. ## The business responsibility behind AI-built apps 1. [Before You Build an AI App, Define the Business Problem](/insights/ai-app-business-decision/) 2. [AI App Production Readiness: From MVP to Launch](/insights/ai-app-mvp-to-production/) 3. [AI App Maintenance: Who Looks After It?](/insights/ai-app-ownership-maintenance/) If you are weighing up a custom app, [talk to us about the business problem](/contact/) before committing to the build. ## FAQs ### Do I need a custom AI-built app for my business? Not necessarily. Start by defining the problem and checking whether a simpler process, your existing software or a suitable product could solve it first. ### How do I make an AI app business case? Describe the work you want to improve, set a measurable outcome, include testing and ongoing support costs, then compare a small trial with the alternatives. ### Can a prototype use real customer data? Prefer appropriate sample data during early testing. Before using real information, agree who can access it and seek the right review for the consequences of a mistake. ### When should we stop testing a prototype? Set a review point and evidence for proceeding or stopping. If staff still need the old process or the app adds work, revisit the problem before adding features.

This article was written by Peter Lowe. The ideas and opinions are his own; AI was used to assist with drafting and editing.