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AI & automation

Draft faster. Review the promise.

How to use AI for proposals while protecting scope and accuracy

Consultant reviewing a proposal with a colleague beside a whiteboard

The decision

Proposal assistance should draw from approved service descriptions, scope rules, and client context. The main risk is an attractive draft that promises work the business has not agreed to deliver. Keep pricing, commitments, and exceptions under explicit human review.

In practice

A lead may request capabilities beyond the standard package. The draft should flag those requirements rather than quietly treating them as included. Review client-specific assumptions and distinguish verified information from questions that remain open.

Put it into practiceYour next checks
  1. Create approved inputs, a review checklist, and a version process.
  2. Test proposals containing unusual timelines or integrations.
  3. Require a named approver before sending and measure the effort to produce an accurate proposal, including review and correction.

When to take the next step

Introduce drafting support after service scope and approved claims are documented. Test a difficult client request to see whether the draft flags assumptions instead of inventing commitments.

Questions clients ask

Should proposals be sent automatically?

Only within a deliberately approved workflow; consequential commitments need the agreed review boundary.

What should reviewers check first?

Scope, price, deadlines, assumptions, exclusions, and claims about the business's capabilities.

Plan your next step.

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