AI Implementation Requirements Document
This is a practical guide to building a requirements document for an ai implementation project — the documented needs the solution must meet, adapted to the realities of delivering an AI/ML capability from data to production value.
What a Requirements Document is
A requirements document is the documented needs the solution must meet. For the full concept and how it works in general, see Requirements Document. On an ai implementation project it plays the same role, tuned to this kind of work.
Why it matters for an AI Implementation project
AI Implementation projects live or die on delivering an AI/ML capability from data to production value. A well-built requirements document gives the team a shared, explicit reference for exactly that — reducing ambiguity, aligning stakeholders, and making problems visible early enough to act. Skipping it, or doing it generically, is how ai implementation projects drift into avoidable delay and cost.
What to include
- Business requirements
- Functional requirements
- Non-functional requirements
- Acceptance criteria
- Traceability
AI Implementation-specific considerations
Tailor the requirements document to the risks that most often derail ai implementation projects:
- Poor or insufficient data
- Models that don’t generalise
- Unclear ROI and adoption
Example
On a real ai implementation project, the requirements document would be shaped by delivering an AI/ML capability from data to production value. In particular, it should explicitly account for the project’s biggest risks — poor or insufficient data, models that don’t generalise, unclear ROI and adoption — rather than treating them as afterthoughts.