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Enterprise AI Vendor Selection Checklist
Most enterprise AI vendor selections fail for the same reason — buyers evaluate demo quality instead of implementation depth.
This checklist is for evaluating AI consulting firms and implementation partners — not AI software platforms. The questions that matter at vendor selection are different from the questions that matter at software evaluation.
A consulting partner will shape your architecture, your governance posture, your team’s capabilities, and your ability to scale. The cost of selecting the wrong one extends well beyond the contract value. Use this framework in every vendor conversation before you commit.
Save or print this page as your evaluation guide — or reference it during vendor calls.
Implementation Depth
Do they have demonstrated experience with your specific use case type — not just AI in general?
Can they show architecture diagrams and technical documentation, not just case study summaries?
Who are the actual delivery resources, and are they the same people you met in the sales process?
Do they have a defined post-launch support model, and what does handoff to your team look like?
Governance & Security
What is their risk framework based on — can they name it and explain how they adapt it?
How do they handle data security in your specific environment — cloud, on-prem, hybrid?
What audit documentation do they produce, and in what format?
Have they previously worked within your regulatory context?
Vendor Neutrality
Do they have revenue-sharing or partnership agreements with any AI platforms or model providers?
Will they recommend a different model if it performs better for your use case?
How do they evaluate new models as the landscape changes?
Can they articulate the trade-offs between the major model providers without defaulting to one?
Delivery Model
What does a typical engagement look like week by week — not just at the milestone level?
Who specifically will be working on your account, and what are their relevant backgrounds?
What is the plan for knowledge transfer and internal ownership by the end of the engagement?
How do they handle scope changes when — not if — requirements shift during implementation?
Measurement Framework
How do they define and track ROI — and when in the engagement is that defined?
What KPIs do they commit to measuring, and do they help establish baselines before deployment?
How do they handle underperformance — is there a remediation process or just a report?
Red Flags — Watch For These
Guaranteed ROI numbers stated before they understand your environment
Platform-first recommendations before any requirements gathering
Vague "we work with your team" delivery models with no specifics on who does what
No governance conversation before scoping — or governance treated as a phase 2 problem
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