How to Evaluate AI Vendors for Enterprise Projects
Enterprise AI vendor evaluation is harder than evaluating other technology vendors. The technology is newer, the outcomes are less predictable, and the gap between marketing claims and delivery capability is wider than in most technology categories.
We've been on both sides of this process - as the vendor being evaluated and as advisors helping enterprises run evaluations. Here's a structured approach that produces good decisions.
Why Standard Vendor Evaluation Doesn't Work for AI
Most enterprises have an established vendor evaluation process. It typically involves an RFP, a scoring matrix, reference checks, and a procurement review. This process works for buying known commodities - cloud hosting, CRM systems, network equipment.
AI is different in three important ways.
Capability claims are hard to verify. Every AI vendor says they can build what you need. Few have actually done it. Unlike buying a software product where you can run a trial, you can't test an AI vendor's capability without engaging them.
Past performance is less predictive. An AI vendor who built a great document processing system doesn't necessarily have the skills to build a great predictive maintenance system. AI spans multiple disciplines, and expertise in one area doesn't guarantee expertise in another.
The vendor matters less than the team. In a large consultancy, the difference between their A-team and their B-team is enormous. The company's brand and client list don't tell you much about the specific people who'll work on your project.
This means you need a more nuanced evaluation approach.
The Evaluation Framework
We recommend evaluating AI vendors across seven dimensions. Each dimension gets a score from 1-5, with specific criteria for each score.
Dimension 1 - Relevant Experience (Weight: 25%)
This is the most heavily weighted dimension because it's the strongest predictor of success.
| Score | Criteria |
|---|---|
| 1 | No relevant AI project experience |
| 2 | Has built AI prototypes or PoCs, no production deployments |
| 3 | Has deployed AI to production in a different industry or use case |
| 4 | Has deployed AI to production in a similar industry or use case |
| 5 | Has deployed AI to production in the same industry and use case, with verifiable references |
How to assess this:
- Ask for 3-5 case studies relevant to your project
- Request references for at least two production deployments
- Ask specific questions about challenges encountered and how they were resolved
- Verify that the people on those case studies are the people who'll work on your project
Dimension 2 - Technical Capability (Weight: 20%)
Assessing technical capability without being a technical expert yourself is challenging. Here's what to look for.
| Score | Criteria |
|---|---|
| 1 | Team appears to be using AI APIs without deeper understanding |
| 2 | Can discuss technical approaches but limited to one method |
| 3 | Demonstrates knowledge of multiple approaches and can articulate tradeoffs |
| 4 | Deep technical expertise with evidence of solving hard problems |
| 5 | Team includes recognised AI practitioners; demonstrates thought leadership |
How to assess this:
- Ask the technical team to explain their proposed approach and why they chose it
- Ask what alternative approaches they considered and why they rejected them
- Ask about a technical challenge they faced on a previous project and how they solved it
- Have an independent technical expert attend the vendor presentation if possible
Dimension 3 - Team Composition (Weight: 15%)
The specific people matter more than the company brand.
| Score | Criteria |
|---|---|
| 1 | Team not identified; generic resource descriptions only |
| 2 | Named team with limited AI experience |
| 3 | Named team with relevant AI experience, some production experience |
| 4 | Strong team with deep AI and production experience, relevant to your project |
| 5 | Exceptional team with proven track record on comparable projects, confirmed availability |
How to assess this:
- Request CVs or detailed bios for all key team members
- Verify experience claims (LinkedIn, publications, GitHub contributions)
- Meet the actual delivery team, not just the account manager
- Ask about team availability and what percentage of their time your project will get
- Ask what happens if a key team member leaves the project
Dimension 4 - Approach and Methodology (Weight: 15%)
How does the vendor plan to deliver?
| Score | Criteria |
|---|---|
| 1 | No clear methodology; ad hoc approach |
| 2 | Generic software methodology applied to AI without adaptation |
| 3 | Structured approach with AI-specific practices (data assessment, iterative model development) |
| 4 | Clear methodology with defined phase gates, risk management, and knowledge transfer |
| 5 | Mature methodology proven across multiple projects, with evidence of continuous improvement |
Key elements to look for in their approach:
- Data assessment phase before committing to a technical approach
- Iterative development with regular checkpoints and course corrections
- Phase gates with go/no-go decisions
- Testing strategy including model validation, integration testing, and user acceptance testing
- Deployment plan including rollback procedures
- Post-launch monitoring and model performance tracking
- Knowledge transfer plan for your internal team
Dimension 5 - Communication and Transparency (Weight: 10%)
AI projects require more ongoing communication than standard software projects because of the inherent uncertainty.
| Score | Criteria |
|---|---|
| 1 | Poor responsiveness; vague answers to direct questions |
| 2 | Adequate responsiveness but limited proactive communication |
| 3 | Good communication; willing to discuss risks and challenges |
| 4 | Proactive communication; raises issues before they become problems |
| 5 | Excellent communication throughout the evaluation; honest about limitations and risks |
How to assess this during evaluation:
- Note response times during the evaluation process
- Assess the quality and honesty of their RFP responses
- Pay attention to how they handle difficult questions
- Do they acknowledge what they don't know?
- Are they willing to say "this might not work" when appropriate?
The evaluation process itself is a preview of how they'll communicate during the project.
Dimension 6 - Commercial Terms (Weight: 10%)
Price matters, but it's not just about the total number.
| Score | Criteria |
|---|---|
| 1 | Pricing is unclear or seems unreasonably low/high for the scope |
| 2 | Clear pricing but inflexible terms; no phased approach |
| 3 | Reasonable pricing with some flexibility; phased structure |
| 4 | Competitive pricing with risk-sharing elements; clear phase-gated structure |
| 5 | Well-structured commercial terms that align incentives; transparent cost breakdown with flexibility |
Evaluate:
- Is the pricing model appropriate? (Time and materials for uncertain scope, fixed for well-defined phases)
- Are there phase gates where you can stop if results aren't satisfactory?
- What's included and what's extra? (Common gotcha: post-launch support not included)
- How do they handle scope changes?
- What are the IP ownership terms?
- What are the exit terms if you need to change vendors?
Dimension 7 - Cultural Fit and Partnership Potential (Weight: 5%)
Less tangible but still important for long-term success.
| Score | Criteria |
|---|---|
| 1 | Transactional relationship; no interest in understanding your business |
| 2 | Professional but limited engagement beyond the project scope |
| 3 | Genuine interest in understanding your business and industry |
| 4 | Partnership mindset; invested in your long-term success |
| 5 | Strong cultural alignment; feels like an extension of your team |
This is harder to score objectively. Pay attention to:
- Do they ask questions about your business beyond the project scope?
- Do they offer insights and suggestions proactively?
- Do they feel like people you'd want to work with for 6-12 months?
- Is there mutual respect, or is it purely transactional?
The Evaluation Process - Step by Step
Step 1 - Define Your Requirements (2 weeks)
Before talking to any vendors, document:
- The business problem and success criteria
- Technical constraints and requirements
- Budget range
- Timeline expectations
- Evaluation criteria and weightings (use the framework above or adapt it)
Step 2 - Create a Long List (1 week)
Identify 6-10 potential vendors through:
- Industry referrals
- Professional network recommendations
- Industry analyst reports
- Previous vendor relationships
- Online research (with healthy scepticism about marketing claims)
Step 3 - Request Information (2 weeks)
Send a brief information request (not a full RFP yet) asking for:
- Company overview and relevant experience
- Team available for your project
- High-level approach to your type of problem
- Two relevant case studies with references
This lets you narrow the list without the overhead of a full RFP process.
Step 4 - Shortlist (1 week)
Reduce to 3-4 vendors based on the information requests. Eliminate any with obvious disqualifiers:
- No relevant production experience
- Can't name the team
- Clear misalignment with your technical environment
- Pricing that's wildly outside your range
Step 5 - Detailed Evaluation (3-4 weeks)
For the shortlisted vendors:
Issue a focused RFP covering your specific project requirements. Keep it under 15 pages. Give vendors 3-4 weeks to respond. (See our guide on how to write an AI RFP for more detail.)
Hold vendor presentations (90 minutes each). Require the technical lead and project manager to present. Structure the session:
- 30 minutes: Vendor presents their approach
- 30 minutes: Technical deep-dive with your team asking questions
- 30 minutes: Commercial and logistics discussion
Conduct reference checks. Call at least two references per shortlisted vendor. Ask specifically about:
- Accuracy of the vendor's claims during their evaluation
- How they handled problems during the project
- Communication quality
- Whether the project delivered the expected value
- Whether they'd hire the vendor again
Step 6 - Score and Decide (1-2 weeks)
Score each vendor against the seven dimensions. Have multiple evaluators score independently, then compare scores and discuss differences.
Create a simple decision matrix:
| Dimension | Weight | Vendor A | Vendor B | Vendor C |
|---|---|---|---|---|
| Relevant Experience | 25% | 4 | 3 | 5 |
| Technical Capability | 20% | 4 | 4 | 4 |
| Team Composition | 15% | 3 | 4 | 4 |
| Approach | 15% | 4 | 3 | 4 |
| Communication | 10% | 5 | 3 | 4 |
| Commercial | 10% | 3 | 4 | 3 |
| Cultural Fit | 5% | 4 | 3 | 4 |
| Weighted Total | 3.85 | 3.45 | 4.15 |
If the top two vendors are within 0.3 points of each other, the quantitative scores are effectively tied. In that case, go with your gut on which team you'd rather work with for the next 6-12 months. Trust and rapport matter.
Common Evaluation Mistakes
Over-Weighting Price
The cheapest proposal is rarely the best value. A vendor who underprices to win the deal will either cut corners during delivery or come back with change requests that erode the cost advantage.
Compare total cost of ownership, including post-launch support and the cost of failure (having to redo the project with another vendor).
Under-Weighting the Actual Team
The brand on the proposal matters far less than the people who'll do the work. A Tier 1 consultancy staffed with recent graduates will deliver worse results than a specialist AI firm with experienced practitioners. Always evaluate the team, not the brand.
Not Checking References Thoroughly
Reference checks feel like a formality, but they're one of the most valuable evaluation tools. Prepare specific questions. Ask about problems, not just successes. The reference's enthusiasm (or lack of it) is the most telling signal.
Evaluating Based on the Pitch, Not the Process
Some vendors are excellent at pitching and average at delivering. Pay more attention to how they engage during the evaluation - the questions they ask, how they handle your questions, the quality of their written proposal - than to how polished their presentation is.
Ignoring Red Flags Because of a Good Price
If something feels wrong during the evaluation - the team seems junior, the timeline seems unrealistic, the claims seem exaggerated - trust that instinct. A low price doesn't compensate for a failed project.
Involving the Right People in the Evaluation
For an enterprise AI evaluation, you need representation from:
- Business owner: The person who owns the problem being solved. They evaluate whether the vendor understands the business context.
- IT/Technology: Assesses technical capability, architecture fit, and integration feasibility.
- Procurement: Manages commercial terms, contracts, and compliance.
- Data/Analytics: If you have a data team, they should assess the vendor's data methodology.
- Security/Compliance: Evaluates data handling, privacy, and regulatory alignment.
Don't let any single function dominate the decision. A vendor that scores well on price but poorly on technical capability isn't a good choice. Neither is a vendor that's technically brilliant but commercially unreasonable.
Getting Started
A structured evaluation process takes effort, but it dramatically improves your odds of selecting the right AI partner. The cost of a thorough evaluation is a fraction of the cost of a failed project.
If you're beginning an AI vendor evaluation, Team 400 welcomes the opportunity to be part of your process. We're confident in our team, our track record, and our ability to deliver.
Learn more about our AI development capabilities, explore our services, or contact us to discuss your project.