AI for advisory proposals: reuse evidence, keep the judgement

Before drafting, identify the client's requirements, the decision to pursue the work and who owns the proposed scope. A polished response cannot settle availability, fees, conflicts or permission to name a previous client. Those decisions need accountable people.

Separate reusable evidence from the new offer. Credentials and CVs describe work and experience already supported by records. Scope, staffing, approach and price describe what the firm is prepared to do now. Review both, but do not let a reused paragraph silently create a new promise.

Create a maintainable library of approved credentials, CVs, service descriptions and case material. Record who owns each item, when it was checked and where it may be used. Keep confidential client details and internal-only documents outside a general proposal pool unless their use has been authorised.

Flowcase describes searchable CV and project-reference content, with tools for selecting and tailoring material. QorusDocs describes drafting from approved content and managing proposal contributions within Microsoft 365. These vendor descriptions show existing approaches to evaluate; they do not establish that either product fits your firm. Flowcase: AI-assisted CV and project content. QorusDocs: proposal software for Microsoft 365.

Try the current method, a permitted existing tool and any proposed alternative on the same representative material. Use an old brief only when the firm has permission to reuse it, or prepare a synthetic brief that reflects the work without exposing client information.

Ask the reviewers to check source support, relevance, completeness and the effort needed to reach a usable proposal. Include a requirement the firm cannot evidence. A system that leaves that gap visible is more useful than one that invents a credential to fill it.

Keep proposal quality and response effort visible alongside the result. A win rate depends on the opportunity, price, relationships, competition and delivery fit as well as the document. Do not attribute a change in wins to AI without a comparison that can support that conclusion.

NIST's voluntary AI Risk Management Framework provides a general structure for defining context, responsibilities, measurement and ongoing management. For a proposal process, the useful question is whether those responsibilities are visible when the team selects evidence, reviews a draft and approves the offer. NIST: AI Risk Management Framework Core.

• Time spent finding and checking evidence.
• Drafting, formatting and review effort, measured separately.
• Unsupported claims, stale credentials and missed requirements.
• Material revisions needed before the proposal is ready for approval.
• Client feedback and the eventual outcome, with other influences recorded.

Advice can clarify which problem matters and whether the current tools already cover it. Implementation can connect content ownership, templates and review with the team's daily work. Consider a build where a specific, valuable gap remains and somebody will maintain the result.

CURN can help with those decisions and delivery. This is a guide to evaluating an approach, not a report of a completed CURN proposal-automation engagement.