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. Discovery and an operational assessment
The consultant should document the current process, the people involved, delays, failure points, data sources, existing tools, and the business result that matters. Discovery should test assumptions with the people who actually perform or manage the work.
2. A prioritized opportunity map
Not every task should use AI. Opportunities should be ranked by expected operational value, feasibility, risk, dependencies, and effort. The output should also explain what should be improved manually, handled with standard automation, purchased, built, piloted, or postponed.
3. Architecture, scope, and acceptance criteria
- A build-versus-buy recommendation with tradeoffs
- A data-flow or system diagram showing inputs, decisions, outputs, and owners
- What is in scope, out of scope, and dependent on the client
- Observable acceptance criteria for normal and failure scenarios
- A rollout plan that identifies approvals and responsible people
4. Testing and failure handling
Testing should cover realistic inputs, missing data, duplicates, unavailable services, unexpected responses, permission failures, and human escalation. A demo that only works on a prepared example is not production evidence.
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. The business should own or control the production accounts unless another arrangement is explicitly documented.
6. Training, handoff, and measurement
- Operating instructions for users and administrators
- Architecture, configuration, dependencies, and recovery documentation
- Known limitations and a process for reporting problems
- A measurement plan tied to the original operating result
- Support terms, maintenance responsibilities, and an exit path
Red flags in a proposal
- The recommendation starts with a favorite tool instead of the 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
- Critical software, data, or staffing dependencies are hidden