Decide what matters before comparing proposals
Vendor proposals rarely use the same structure. One may lead with features, another with service levels, and a third with a low introductory price. Comparing them by overall impression can reward the clearest presentation rather than the best fit. A decision matrix gives your team a shared set of criteria and a place to record the evidence behind each judgment.
Start with the job the vendor must do. List requirements that are non-negotiable, such as a necessary integration, an acceptable data-handling arrangement or a delivery constraint. Treat these as eligibility checks rather than points a vendor can recover elsewhere. Then choose a short list of comparison criteria: total cost, implementation effort, support, usability and relevant capabilities might all matter, depending on the purchase. Define each criterion in one sentence so reviewers know what they are scoring.
Agree on priorities before reading the proposals closely. You can assign weights that add up to 100, but the exact numbers are less important than documenting why one criterion matters more than another. If two team members have different priorities, keep both views visible instead of letting an AI tool settle the disagreement.
Use AI to organize evidence, not to invent it
AI is useful for turning differently formatted proposals into a common comparison table. Give it your criteria and ask it to extract the relevant statements from each proposal. For every entry, request the proposal name, a page or section reference, a short description of the evidence, and a status such as confirmed, unclear or not addressed. Ask it to leave a field blank when the proposal does not answer the question.
Check the resulting table against the original documents. AI can overlook a qualification, confuse a planned feature with an available one, or treat marketing language as a firm commitment. Pay particular attention to pricing conditions, usage limits, exclusions and what happens after an introductory period. If a claim will affect the decision, find the exact language yourself or ask the vendor to clarify it in writing.
Before uploading proposals, consider whether they contain confidential prices, personal information or sensitive business details. Follow your organization’s data-handling rules and the tool’s applicable settings. Where needed, remove sensitive material or work from a manually prepared summary instead. The convenience of automated extraction does not remove your responsibility for how proposal data is shared.
Score consistently and keep uncertainty visible
Choose a simple scoring scale and define its meaning before applying it. On a zero-to-three scale, for example, zero could mean the requirement is not met, one could mean partial support, two could mean it is met, and three could mean it is met with a useful additional benefit. Use the same definitions for every vendor. A score should reflect evidence in the proposal or a verified follow-up, not confidence in how persuasive the writing sounds.
Keep uncertainty separate from performance. “Not addressed” is not necessarily the same as “cannot do it.” Mark missing information and create a question for the vendor rather than assigning an optimistic score. You can ask AI to suggest provisional scores and explain which evidence it used, but a person should review each one. Record a brief rationale alongside the final score so someone who was not in the meeting can understand it later.
Multiply scores by weights if a weighted total helps your team compare options. Treat that total as a discussion aid, not an automatic decision. A high score cannot resolve a failed non-negotiable requirement, and a small numerical difference may mean little when several answers remain unclear.
Test the result and make a decision you can explain
Once the matrix is filled in, ask what would change the outcome. Would a modest change in cost priority reverse the ranking? Is the leading vendor ahead because it answered more questions, or because its offer better fits the work? Which unanswered question could alter the choice? AI can help identify these patterns and draft a common set of follow-up questions, but it should not present uncertain inputs as a settled recommendation.
Before selecting a vendor, review the highest-impact claims against the proposal and any written clarifications. Consider a small trial or demonstration using your own realistic tasks when that is practical. Document the chosen option, the main reasons for choosing it, the trade-offs accepted and the questions that still need resolution. This leaves you with more than a ranked list: it creates a decision record your team can revisit if needs or proposals change.
