Consultant Evaluation
What Should an AI Consultant Deliver to Your Business?
Use this practical checklist to evaluate AI consulting deliverables, implementation scope, testing, documentation, ownership, and post-launch support.
1. A clear view of your current operation
A useful discovery phase should show you how the work happens today. It should cover the people involved, delays, failure points, data sources, existing tools, and the business result that matters. The people who perform or manage the work should be included so the plan reflects daily reality.
2. A prioritized opportunity map
Not every task in your business should use AI. Each opportunity should be ranked by value, difficulty, risk, dependencies, and effort. The final map should explain what to improve by hand, handle with standard automation, buy, build, test, or postpone.
3. Architecture, scope, and acceptance criteria
- A build-versus-buy recommendation with clear tradeoffs
- A system diagram showing inputs, decisions, outputs, and owners
- What is in scope, out of scope, and dependent on your team
- Clear acceptance criteria for normal and failure scenarios
- A rollout plan that names each approval and responsible person
4. Testing and failure handling
Your test plan should include realistic inputs, missing data, duplicates, unavailable services, unexpected responses, permission failures, and human escalation. A demo that only works with a prepared example does not prove that the system is ready for daily use.
5. Security, permissions, and ownership
You should know which systems the solution can access, why each permission is needed, where credentials are stored, and who can revoke them. Browser code and public repositories must not contain private API keys. Your business should own or control the production accounts unless another arrangement is clearly documented.
6. Training, handoff, and measurement
- Clear operating instructions for your users and administrators
- Documentation for the system, settings, dependencies, and recovery steps
- Known limits and a process for reporting problems
- A measurement plan tied to the business result you chose
- Support terms, maintenance responsibilities, and an exit path
Red flags to watch for
- The recommendation starts with a favorite tool instead of your business problem
- There are no written success criteria or failure tests
- Ownership, credentials, and handoff are vague
- ROI, savings, or performance are guaranteed without evidence
- Important software, data, or staffing needs are hidden