Running the selection · Chapter 14
How to compare AI automation proposals
Proposals are easy to misread. They differ in scope, price and detail. This chapter shows how to compare them fairly.
“The art of being wise is knowing what to overlook.”
What a proposal should contain
Compare like for like. A complete proposal covers these points.
- Scope and clear deliverables.
- Timeline and key milestones.
- A fixed price, or a clear basis.
- Who owns the code and data.
- Support and what happens next.
Normalize the scope first
Proposals rarely cover the same work. One includes discovery and support. Another covers only the build. List what each includes and leaves out. Only then are the prices comparable.
Weight what actually matters
Not every criterion counts the same. Weight fit and ownership highest. Features should carry the least.
Adjust the weights to your situation. But keep fit and ownership near the top.
Score proposals side by side
Use one scorecard for every proposal. Rate each criterion, then apply the weights. See the evaluation chapter for the full scorecard.
| Criterion | Weight | Score 1–5 |
|---|---|---|
| Fit for your problem | 30% | __ |
| Ownership terms | 25% | __ |
| Total cost | 20% | __ |
| Delivery process | 15% | __ |
| Support & SLA | 10% | __ |
Compare total cost, not headline price
The quote is not the whole cost. Add subscriptions, AI usage and support. Compare the full first-year cost instead. See the pricing chapter for the hidden costs.
Don't buy a feature matrix
A long feature list looks reassuring. But features are not outcomes. Judge each proposal on the result it promises.
Red flags in a proposal
Some proposals signal trouble early. Watch for these.
- Vague scope with no clear deliverables.
- No mention of ownership or exit.
- A lowball price with no detail.
- A feature dump with no outcomes.
Comparing proposals by company size
For small teams
Two or three proposals are enough to compare. Focus on scope, price and ownership clarity.
For enterprises
Use a formal scorecard across stakeholders. Bring procurement and legal into the ownership and cost review.
Common mistakes comparing proposals
Key takeaways
- Normalize every proposal to the same scope.
- Weight fit and ownership above features.
- Score each proposal on one scorecard.
- Compare total cost, not the headline price.
Gathering proposals?
Browse the directory to shortlist agencies to quote.
Browse the directoryFrequently asked questions
How do I compare AI automation proposals?+
Normalize the scope so each covers the same work. Weight what matters — fit, ownership and total cost. Score every proposal the same way. Compare the full cost, not the headline price. The best proposal is rarely the cheapest or the longest.
What should an AI automation proposal include?+
It should state the scope and deliverables clearly. It should give a timeline and a fixed price. It should confirm who owns the code and data. It should cover support and what happens next. A vague proposal is a warning sign.
Should I choose the cheapest proposal?+
Not on price alone. A cheap quote often skips discovery, testing or support. Compare the full first-year cost instead. Weigh fit and ownership above the number. The cheapest proposal can cost the most later.
How do I compare proposals with different scopes?+
Line them up against the same checklist first. Note what each includes and leaves out. Ask agencies to quote the same core scope. Only then are the prices comparable. Comparing different scopes is how buyers overpay.
What are red flags in an agency proposal?+
Watch for vague scope and no clear deliverables. Be wary if ownership or exit terms are missing. A lowball price with no detail is risky. A long feature list with no outcomes is noise. Push back until the proposal is specific.
How do I score agency proposals fairly?+
Use one scorecard for every proposal. Rate each criterion from one to five. Weight fit, ownership and cost the most. Add the weighted scores to rank them. This keeps the choice objective, not a gut call.