Should you use AI to write your tender?
Generative AI has changed bid writing.
A model can take a tender question and produce a fluent answer in seconds.
That sounds like a major advantage.
But there is a problem.
If the model does not have the right evidence, it can produce something that sounds convincing without giving an evaluator anything useful to score.
That is why there is an important distinction between:
AI that writes your bid
and
AI that checks your bid.
The problem with AI-generated bid copy
Ask an AI system to write a persuasive tender response and it will naturally try to make the answer sound strong.
That can produce phrases such as:
- proven methodology
- industry-leading expertise
- robust processes
- exceptional service
- extensive experience
The problem is that these phrases are easy to generate and difficult to score.
AI can make weak evidence sound impressive.
It cannot turn an unsupported claim into evidence.
Scoring is a different task
Scoring a tender response is less about invention and more about comparison.
Does the response address the criterion?
Does it cover every sub-criterion?
Is the claim supported?
Does the answer meet the scoring descriptor?
Are there gaps?
Is there a threshold?
Those are structured questions.
They are much closer to the kind of consistent analysis AI can perform well.
BidReview's AI bid scoring approach is designed around this principle: assess the finished response against the tender's criteria and weightings, identify why it could be marked down and prioritise the gaps.
AI doesn't get tired on answer 40
A bid may contain dozens of quality responses.
Human reviewers get tired.
They start with the strongest answers and gradually lose attention.
AI can apply the same checking process to every response.
That makes consistency one of its most useful characteristics.
It can ask the same questions repeatedly:
Is this evidenced?
Does it answer the criterion?
What band does it appear to meet?
What is missing?
But AI should not invent the evidence
This is critical.
If the bid says you delivered 97% SLA performance, the figure should come from your business.
AI should not invent a more impressive number.
If the tender requires a comparable case study, AI can identify that your case study is weakly matched.
It should not manufacture a better one.
The value of AI scoring is therefore not that it creates proof.
It identifies where proof is missing.
The better workflow
A sensible AI-assisted bid workflow looks like this:
1. Understand the tender
Identify the requirements, criteria and scoring framework.
2. Draft
Use your normal team, existing content or AI assistance where appropriate.
3. Score
Assess every answer against the actual evaluation framework.
4. Diagnose
Identify missing evidence, incomplete coverage and scoring risks.
5. Fix
Use people inside the business to find the real proof.
6. Re-score
Check whether the changes improved the predicted score.
7. Proofread
Only then polish the final document.
This turns AI from a copy generator into a quality-control system.
The writer should not be the only judge
There is another problem with AI-assisted drafting.
The author becomes attached to the answer.
They know what they meant.
They know the background.
They know the project.
The evaluator does not.
That is why independent review remains important, whether it is performed by a person or supported by AI.
BidReview's independent proposal review explores the same principle: the evaluator sees only what is on the page.
AI should expose weak bids, not disguise them
The strongest use of AI in tendering may not be writing more words.
It may be helping bidders discover that their existing words are not good enough.
That is a much more valuable insight.
