Practical guide
AI fluency for proposal managers: build a response matrix
Create a fictional proposal response matrix with traceable requirements, approved claims and visible exceptions.
A proposal manager can demonstrate AI fluency by turning a request into a complete, traceable response without inventing capabilities or hiding exceptions. This original exercise follows a fictional request for a limited service pilot. Its core artifact is a response matrix that connects each requirement to approved evidence, an owner and the final proposal location.
Extract requirements without changing their meaning
Separate mandatory requests, evaluation questions and background information.
The fictional request asks for a pilot scope, implementation responsibilities and support arrangements. It also describes a possible future expansion. Ask the participant to identify which statements require a response now and which are context. Do not treat the future expansion as part of the approved pilot simply because it appears in the same document.
AI can help extract candidate requirements, but inspect the wording and location of each. Preserve compound requirements when their parts need separate evidence. A matrix that contains every heading but misses a condition inside a paragraph is not complete.
Attach approved evidence to each response
A response should point to what the organisation is authorized to say.
The exercise includes an approved service brief and an informal internal message proposing a new feature. The brief can support the current offer; the proposal cannot be represented as an existing capability. Mark requirements that lack approved support rather than asking AI to produce plausible-sounding coverage.
Use fields for requirement, source location, proposed response, supporting evidence and owner. Keep conditional wording visible. The matrix should help the team resolve gaps, not conceal them under generic assurances. Actual commercial or contractual commitments require the appropriate authorized review.
Manage exceptions before polishing the document
An exception is a decision to resolve, not a writing defect.
In the fictional request, the buyer asks for a support window outside the approved pilot scope. The participant should identify the mismatch, describe the available scope and route the question to its owner. They should not silently accept the request or omit it from the response.
Ask AI to draft a clear clarification using only the approved materials. Review whether the draft adds a promise about availability or timing. If the owner has not decided, retain the unresolved status. A persuasive sentence cannot substitute for that decision.
Keep the proposal synchronized with the matrix
The final narrative must preserve the conditions recorded during analysis.
After drafting, trace each matrix row to the relevant proposal section. Check that exclusions, dependencies and unanswered questions remain visible. A condition included in the working matrix but removed from the executive summary may materially change how the buyer understands the offer.
Introduce an updated requirement and ask which rows and proposal sections need revision. Preserve a version note so reviewers can see what changed. Do not regenerate the entire response blindly if doing so risks losing previously approved wording or reopening settled scope.
Run a completeness and claims review
Review coverage and truthfulness as separate questions.
A proposal may answer every requirement while making unsupported promises, or remain accurate while omitting an important question. Check both. Use the matrix to find missing responses, then inspect concrete claims against the approved source pack. Flag uncertain commitments for the responsible owner.
Deliver the proposal with an internal exception summary and a clear review status. Evaluate the participant's extraction accuracy, evidence use, change control and handling of authority. This exercise does not measure likely sales outcomes. The revenue function guide covers account discovery and delivery handoffs, which are distinct from this requirement-to-response workflow.
Sources and scope
NIST addresses AI risk management. Skills England describes workplace AI foundations.
These sources provide background, not endorsement of this exercise. The worked example and suggested review method are original illustrative guidance. They are not customer results, validated benchmarks or evidence of a particular product capability. Adapt the exercise to the task and use qualified review where consequences require it.
Sources: [1] [2]
Sources
- 1.AI RMF Core · NIST
- 2.AI foundation skills for work benchmark · Skills England