The right AI partner must fit your company’s needs, capacity, and constraints. Evaluate tangible evidence and working practices, not just speed promises or technology names. Use this guide to support a discussion with business and IT teams.
7 evaluation criteria
1. Relevant evidence
Ask for implementations similar to your processes, scale, and constraints. Clarify the partner’s actual role, measurement period, baseline, and whether references may be shared with permission. A recognizable logo alone does not establish the capability you need.
2. A path from pilot to production
Separate experimentation, user validation, security review, and launch. Ask for acceptance criteria and the approver at each stage. The schedule should identify data and integration prerequisites, not just an end date.
3. Costs you can understand
Request a breakdown of development, model, infrastructure, integration, and support costs. Early estimates can reasonably rely on assumptions, provided those assumptions and cost-change triggers are explicit. Separate one-time and recurring costs when comparing offers.
4. Team and responsibilities
Meet the technical lead and delivery owner. Ask who handles integration, testing, documentation, and support. In-house, partner, or mixed teams can work well when ownership, quality, and coordination are clear.
5. Knowledge transfer
Agree documentation, training, access rights, code ownership, and handover processes at the start. Your team should understand quality evaluation, failure handling, and updates. Make dependencies on the provider explicit.
6. Data handling and risk
Map data categories, access rights, processing location, model providers, and retention. Involve legal, compliance, and security teams in assessing organizational requirements. Compliance claims need scope and evidence, not just a list of acronyms.
7. Operations after launch
Ask what is monitored, how quality degradation is detected, how escalation works, and what response targets apply. Changes to documents, processes, or models can affect outputs. Clarify included support and who authorizes changes.
5 warning signs
- Outcome or compliance guarantees without a baseline, scope, and evidence.
- An impressive demo without evaluation, integration, and failure-handling details.
- Unexplained model, infrastructure, or support costs.
- Unclear technical ownership and post-launch responsibilities.
- No stop criteria or scope-change process when a pilot falls short.
10 questions for an initial conversation
- Which use case is most similar to ours?
- What evidence can be shared and how were results measured?
- Who is the technical lead and delivery owner?
- What data and access are needed before starting?
- How do you distinguish pilot success from production readiness?
- What is included and excluded from the cost?
- How are data access, retention, and model-provider use managed?
- What will our team receive at handover?
- What happens if pilot targets are not met?
- How is post-launch support delivered?
Compare scope, not just the total price.
Build an evaluation table covering deliverables, assumptions, integrations, recurring costs, delivery team, and support. Ask for clarification when a proposal lacks detail. A focused pilot with agreed measures can help test fit before expanding investment.