This is not a post about whether to use AI in bid writing. That question is settled - the tools are in most bid teams already, and used well they widen your pipeline and cut the cost of a compliant draft. This is a post about where they cost you marks, because in the finished bids we audit, AI-assisted drafting fails in six specific, repeatable ways. All six are fixable before submission. None is fixable after.
1. Invented figures and references. A model asked to make an answer stronger will oblige - with a percentage, a client name, or a case-study outcome it made up. In a formal procurement document that is not padding, it is misrepresentation, and a buyer can verify references. A factual error about the buyer’s own service is worse again: it tells the evaluator you do not know their organisation, and it craters the credibility of everything around it. The fix: every number, name and date in the draft gets traced to a source document before submission. Anything unverifiable comes out.
2. Generic ceiling content. The default AI answer is structurally complete and evidence-empty: methodology described, governance asserted, nothing an evaluator can verify. Scored against a typical band scale it sits mid-band, because the top bands demand demonstration and the model was never given any. The fix: load each answer with named contracts, dated outcomes and real figures from your delivery record. The generic ceiling does not move for better prose.
3. Drafting residue. Assistant scaffolding left in body text, unfilled placeholders, boilerplate carrying another client’s name or the wrong county. Mostly not scored directly - but it proves the passage was never read by its author, and evaluators draw the obvious conclusion about the rest. The fix: a line-by-line read of the final document, by a human, after the last edit. The residue patterns worth searching for are listed separately.
4. Contradictions between sections. Different prompts on different days produce a methodology that promises weekly reporting in one answer and monthly in another, or a team structure that changes between the quality response and the CVs. Evaluators read the whole submission; models never did. The fix: one person reads the complete bid end to end for consistency - team, timeline, commitments, pricing - before sign-off.
5. Hedged and hollow commitments. Generated text defaults to “we would hope to”, “subject to”, “endeavour to” - and to empty intensifiers such as world-class and industry-leading. Hedges read as reservations; buzzwords carry no scoreable information. The fix: commitments in plain future tense with an owner and a number. Delete every claim a competitor could paste into their own bid unchanged.
6. Brief drift. The pack names data points, sub-groups, appendices and the buyer’s own terminology. A model working from your prompt rather than the full RFT drifts off them - and answers that ignore the named material read as non-responsive however fluent they are. The fix: check each answer against the question as asked, in the buyer’s own words, with every named item demonstrably used.
The pattern behind all six
Each failure is a reading failure, not a writing failure. The model wrote; nobody read - not the source documents, not the buyer’s pack, not the finished bid as a whole. AI has made drafting nearly free, which means the value in your bid process has moved to the two ends the model cannot do: the evidence only your firm holds, and the sceptical final read an evaluator will certainly perform.
Six risks, one audit. BidReview scores your finished bid the way the panel will - residue, contradictions, unverifiable claims and brief drift flagged with fixes, before submission. Run the free scorecard →
