For estimators, bid managers and presales teams told to use AI on bids. Not a guide to choosing AI takeoff or estimating software.
AI belongs in the repeated part of a bid: company descriptions, method statements, compliance answers and past-project summaries that change little from one tender to the next. It does not belong in quantities, pricing or technical commitments, because those carry the liability and depend on judgment the model does not have. At CERAP Prévention, 50 to 60 percent of each response is repeated content, according to Eric Hénon. That share is where the hours go, and where AI earns its place.
Key takeaways
- AI bid writing works on repeated content, which CERAP Prévention puts at 50 to 60 percent of each RFP response.
- Quantities, pricing and technical commitments stay with the estimator because they carry the contract liability.
- None of the three top-ranking AI bid writing guides reviewed on 10 September 2026 mentions takeoff, liability or technical commitments.
- Buyers are adjusting: in September 2026 a UK procurement expert described a tender scored 40% on price and 10% on social value.
Where does AI belong in a bid?
AI bid writing is the use of language models to draft the text of a tender, RFP response or proposal from a company's existing material. The useful question is not whether to use it. It is which parts of the bid it should touch.
Our position is a line drawn by liability. Text that describes your company, your methods and your track record can be drafted by AI and checked by a person. Numbers and promises that will bind you in the contract are produced by the person who will answer for them.
Practitioners who use AI daily draw the same line. Jake Suthers, a chartered quantity surveyor and co-founder of The QS Company, wrote on LinkedIn on 7 September 2026: "AI is one of the best tools I've ever used. Not for estimating" (post). That is not resistance to AI. It is a boundary around the part of the job that carries the risk.
The repeated half of a bid: what AI should draft
A large share of every response is material the team has already written. Rewriting it by hand is where the hours go, and where copy-paste errors come in.
CERAP Prévention works in nuclear engineering and radioprotection. Its RFPs, technical memorandums and compliance dossiers often run past 200 pages. "About 50 to 60 percent of the content is always the same," says Eric Hénon, Director of Subsidiaries and Development. Before, the team had to reread everything, check that nothing had changed and copy it across by hand. A missing paragraph or an outdated clause could put the whole bid at risk. CERAP reports that a response that took 3 full days now takes 1 (CERAP Prévention's RFP case).
The repeated half is predictable. It usually includes:
- the company description, certifications and policies;
- standard method statements and quality, safety and environmental answers;
- CVs and team descriptions;
- past-project summaries and case studies;
- standard answers to compliance and security questions.
The quality of the draft depends on the material behind it. AI drafting from an outdated method statement reproduces the outdated clause faster. Before you automate, keep one approved version of each repeated block, with an owner and a review date. That is the risk CERAP described, multiplied across a 200-page response.
This is the part AI bid writing should cover, drafted from the company's own approved material, so a person reviews it instead of retyping it. In Cobl, agents draft from the company's Shared Drive and past bids, across the documents a full RFP response needs, and the output exports to editable Word and PowerPoint for the team to adapt: see agents that draft the repeated half of an RFP response from your past bids.
What stays with the estimator: quantities, pricing, technical commitments
Three things in a bid carry the contract risk. Each depends on judgment about this job, this site and this buyer, which is exactly what a model trained on other documents does not have.
Quantities
Quantities come from reading this project's drawings and specifications. A wrong quantity is a wrong price. Estimators are asking how to use AI around the takeoff without handing it the takeoff itself. On r/estimators, one asked for ways to use AI as a supplementary tool while keeping the takeoff manual (thread, 9 September 2026). Another asked whether AI takeoff software could realistically turn around a full landscape bid in four hours (thread, 1 September 2026).
Where AI helps around the takeoff is reading: summarising a long specification, listing which documents changed between addenda, or drafting the clarification questions to send the buyer. The count itself stays with the estimator.
Pricing
Rates depend on your crews, your suppliers, your overheads and local conditions. None of that is in a general model. In an r/estimators thread asking whether anyone had succeeded with an AI estimating tool, one user said the tools they tried gave inaccurate pricing and time estimates "about 70% of the time" (thread). That is one person's experience, not a measured rate. It still shows why estimators keep the price.
AI can still help with the words around the price: the pricing narrative, the list of assumptions, the format the buyer asked for. The numbers inside it come from your own rates and your own suppliers.
Technical commitments
Once submitted, a method, a programme or a performance level becomes a promise, and often a contract term. The person who signs it should be the person who can deliver it. AI can draft the description of a standard method. It cannot decide whether that method fits this site, this schedule and this budget.
The risk is not that AI writes badly. It is that it writes with confidence. A generated paragraph promising a 24-hour response time reads as well as a true one. Every commitment in the response should trace back to a person who confirmed it.
Scope is a judgment call, not a sorting task
AI is good at sorting documents. Deciding what is in scope is a different job. In an r/estimators thread about bid packages spread across too many files, one commenter noted that AI can help sort the pile, but "it cannot decide what is actually in scope" (thread).
Scope depends on addenda, exclusions, the conditions of contract and what the buyer means by a vague line. Getting it wrong costs money after award, not before. The tool for that work is a requirement-by-requirement check owned by a person: see a compliance matrix built to catch missed scope.
The same applies to exclusions and assumptions. AI can list candidates from the documents: items mentioned but not specified, quantities marked provisional, work by others. Deciding which ones to exclude, and how to word them so they hold after award, is a commercial decision.
The counterargument: takeoffs are about to be automated
Not every estimator agrees with keeping AI away from quantities. A thread on r/estimators titled "Takeoffs are soon to be gone" points to geometric models from design software as the reason manual takeoff will disappear (thread, early September 2026). Quantity extraction from models is improving, and the argument deserves a fair hearing.
The same thread also carries the counterpoint: doing the takeoff is how an estimator learns what gets installed where, and that knowledge feeds the price. Our hypothesis is that both sides can be right. Measurement may well automate. The judgment on top of it, and the signature on the price, will not move to the software, because the liability does not move with it.
Teams considering automated takeoff have a practical test available. Run it in parallel with a manual count on a few bids you have already priced, and compare. If the gap is small and explainable, the tool earns a place in the process. If it is not, you have your answer without risking a live bid.
Buyers are adjusting to AI-written bids
The other side of the table is reacting too. Gemma Waring, a UK bidding and procurement expert, wrote on 8 September 2026 that "The fight against AI generated tenders is on", describing a tender evaluated on 40% price and 10% social value (post).
Buyers evaluating tools ask the same question estimators do. On r/procurement, a buyer told to explore AI RFP tools asked whether they improve accuracy or just create more cleanup work (thread, 6 September 2026).
Our reading, stated as a hypothesis: as written answers get cheaper to produce, buyers will weight what AI cannot fake. That means price, verifiable references, interviews and specific commitments. Generic text will score less, and specific text backed by evidence will score more. That makes the human parts of a bid worth more, not less.
For bid teams, the consequence is to make every answer specific to the tender in front of you. Name the site, the buyer's stated priorities and the project references that match. An answer that could be pasted into any other bid is the answer most likely to read as generated, whoever wrote it.
A working rule for your team
The table below turns the line into a rule a bid team can apply task by task. It fills a gap: the three top-ranking guides on AI bid writing that we reviewed on 10 September 2026 (Responsive, AutoRFP.ai and Bid Solutions) did not mention takeoff, liability or technical commitments once.
Contractors can apply the rule as it stands, since most of the repeated rows reappear in every tender they answer. See bid documents for construction firms. Whatever tool you use, keep a person in the loop on every row, and keep quantities, pricing, programme, commitments and sign-off fully human.
Start small. Take one live bid, mark each section with its row in the table, and let AI draft only the rows marked yes. Record the hours saved and the corrections your reviewers made. After three or four bids, the team has its own evidence on where AI helps, which is more useful than any vendor's figure.
Where your bid data goes
Tender documents hold client information, pricing and technical detail. Putting them into a public chatbot raises a fair question about where that data ends up. Javier Escartin raised the same concern about proprietary information in language models back in March 2023 (Bidding Quarterly).
Ask any AI bid writing tool three questions: where the data is hosted, whether it trains models on your content, and what infrastructure it runs on. For Cobl, application data is hosted in the EU, in Dublin, customer data is not used to train models, and the service runs on SOC 2 Type 2 and ISO 27001 certified infrastructure. The details are on how Cobl handles bid documents and data.
