Why the brief matters so much in AI development
AI development projects fail for many reasons, but a poorly written brief is behind more of them than most people acknowledge. A brief that's vague about what the system needs to do, optimistic about timelines, or silent on the constraints that actually matter produces a development process full of misaligned expectations, scope creep, and rework.
A good brief doesn't need to be long. It needs to be specific about the right things and honest about what isn't yet known. Getting it right before development starts saves significant time and money during it.
What to put in
The problem, not the solution. Start with what you're trying to solve, not with how you think it should be solved. "We need a chatbot" is a solution. "Our support team spends 60% of their time answering the same 20 questions, which delays resolution of complex queries" is a problem. The distinction matters because the best technical solution often isn't the one the client had in mind — and a developer who understands the problem can propose something better than one who's been told to build a specific thing.
The current process in detail. Walk through exactly how the process works today — step by step, including the manual workarounds, the exceptions, and the edge cases. AI systems that are built without a thorough understanding of the current process tend to automate the easy parts and leave the hard parts exactly as they were, which rarely delivers the expected value.
Who uses it and how. Describe the end users of the system — their technical comfort level, how frequently they'll interact with it, what context they'll have when they do, and what a good experience looks like for them. A system designed for a technically confident analyst is different from one designed for a frontline customer service agent. Both are different from one designed for occasional use by a senior executive.
The data that's available. What data will the system need to work with? Where does it live? What format is it in? How often is it updated? Who owns it? Questions about data are the most common source of late-stage surprises in AI projects. Surfacing them in the brief prevents them from becoming problems mid-build.
The systems it needs to connect to. List every system the AI tool will need to integrate with — CRM, ERP, document management, communication platforms. Include who manages each system, whether APIs exist, and any known restrictions on data access or integration.
What success looks like, specifically. Not "it saves time" but "it reduces the average handling time for this process from 45 minutes to under 10 minutes." Not "it improves accuracy" but "it produces output that requires fewer than two rounds of human review before approval." Measurable success criteria make it possible to know whether the project has delivered what it was supposed to.
The constraints that aren't negotiable. Budget. Timeline. Data residency requirements. Regulatory constraints. Security requirements. Tools or vendors that are excluded for any reason. Getting these on paper early prevents them from surfacing as blockers after significant work has been done.
What to leave out
Specific technical approaches. Unless you have a strong technical reason to specify the underlying technology, leave the technical choices to the developer. Briefs that specify the model, the architecture, or the tech stack before the problem is fully understood constrain the solution space unnecessarily. Describe what the system needs to do, not how it should do it.
Aspirational features that aren't requirements. It's tempting to include everything you'd like the system to eventually do. Resist it. A brief padded with nice-to-haves produces scope discussions that slow down delivery and inflate cost estimates. Focus on what the system must do to solve the core problem. Additional features can be scoped as a second phase once the core is working.
Implementation detail you haven't validated. If you're not certain how a particular aspect of the current process works — how an edge case is handled, what happens when data is missing, who makes a specific decision — don't guess in the brief. Flag it as something to confirm during discovery. Assumptions that turn out to be wrong are expensive to unwind mid-project.
The discovery conversation
A written brief is a starting point, not a complete specification. The best AI development projects begin with a discovery phase — a structured conversation between the developer and the people closest to the problem — that turns the brief into a genuine shared understanding of what needs to be built.
If a developer is willing to start building without that conversation, that's worth noting.
A good AI development brief is specific about the problem, honest about the constraints, and disciplined about leaving the technical choices to the people building the system. It's not a long document — but it's a precise one. The time invested in getting it right before development starts pays back many times over during it.