Imagine two companies that receive the same request for a quote on Monday morning. The first responds by Thursday: a manager spent hours reading volumes off the drawings, sending price requests to suppliers, compiling a comparison table by hand, and rewriting the text for the client. The second sends a complete, polished proposal 30 minutes after the call — and walks into the negotiation first.
The difference isn't that the second team works harder. They simply stopped spending working hours assembling the document and handed that part to AI.
Preparing quotes, bids, and tenders is one of the most expensive and least visible processes in B2B sales. A single quality proposal takes anywhere from 4 to 6 hours of work, and complex projects — reading drawings, coordinating with suppliers — take a full working day or more. For some companies, one set of documents runs 8 to 20 hours. And almost all of that time goes not into strategy or the client relationship, but into calculations, formatting, and hunting for the right data.
Why quotes and tenders are the first candidate for automation
Not every process is worth automating. But preparing quotes and tenders meets every criterion at once.
It's repeatable. Most proposals follow the same structure, where only the variables change: scope of work, price items, deadlines, terms, client details.
It runs on data you already have. Price lists, past deals, specifications, case studies, supplier responses — it's all already sitting in your CRM, on your drive, and in your inbox. AI just needs access to it.
It's measurable. Time per proposal, response speed to a request, tender win rate — all of it is easy to measure before and after.
That's why proposal preparation is almost always one of the first processes companies automate: it delivers a fast, clear result without rebuilding the entire sales department. We've written in detail about how to choose the right first process to automate in our article on choosing your first AI project.
How it works in practice
Automated proposal preparation is a series of connected steps where AI takes on the routine and a person keeps the decisions.
Step 1. Gathering the source data. The system reads the incoming materials — the brief, PDF drawings, specifications — and automatically extracts the parameters it needs: volumes, items, requirements. What takes a specialist hours of manual reading is done in minutes.
Step 2. Drafting. Based on your templates, current price lists, and past projects, the system produces a ready draft — with the correct structure, calculated line items, and the case study best suited to this specific client. When needed, it consolidates supplier prices into a comparison table.
Step 3. Review and send. The manager doesn't write the proposal from scratch but reviews and refines a ready document: sharpening the emphasis, adding an observation from the negotiation, checking the figures.
Put end to end, this is how a process that used to fill most of a working day collapses into roughly half an hour.
AI doesn't replace the salesperson. It removes the assembly and calculation work, so people can focus on what actually requires judgment — strategy, client relationships, and closing the deal.
What the numbers show
The time a manual quote takes is only half the picture. The more telling number is how much of that work — pulling data from documents, compiling tables, coordinating across people — disappears once AI handles it. And that same mechanic shows up across many processes, not just quotes.
In a workshop with LTH Baas, a marine engineering and shipbuilding company, analysis showed that document searches, supplier communications, and manual coordination between departments consumed around 280 hours per month — roughly 1.5 full-time employees. These are the same building blocks that go into preparing quotes and tenders.
In a project with Verston, a road construction company, the team identified 34 potential AI use cases and selected the 4 with the highest impact. The estimated savings from just those four came to €750,000 per year, and most of the solutions required no significant development.
And in sales that time converts directly: responding first, with a sharper proposal, is often what wins the deal.
Where to start: a safe 4-week implementation path
To get this result, you don't need to automate everything at once or assemble a dedicated AI team. One focused pilot project on a single type of proposal is enough.
Week 1. Measure your current baseline. Record how many hours proposal preparation takes today, and pick one document type for the pilot — ideally the one that recurs most often.
Week 2. Prepare the source materials. Gather current data: an up-to-date price list, 3–5 strong case studies, standard phrasings, answers to common objections. The quality of the draft depends directly on the quality of these materials.
Week 3. Launch in a secure environment. Set up generation based on your templates and test the result on real requests. Assign an owner who tracks quality and adjusts how the system works.
Week 4. Compare the result. Measure time per proposal, response speed, and client reaction. This becomes your proof of value — and the basis for scaling the approach to other document types and tenders.
We break down how to properly calculate the return on a pilot like this, step by step, in our article on measuring AI ROI.
Speed matters, but data security is non-negotiable
Quotes and tenders contain sensitive information: prices, terms, and client and supplier data. So this process should be automated as responsibly as any work with confidential data.
Enterprise tools such as Microsoft Copilot and ChatGPT Enterprise comply with SOC 2, ISO 27001, and GDPR, which simplifies legal review. It's important to understand what data the system bases its calculation on, and to make sure client information and pricing data are handled correctly — particularly given the EU AI Act's transparency requirements, which take effect on 2 August 2026.
The human role matters just as much. AI prepares the draft, but the final decision stays with the specialist. Teams that send a generated document without review risk sending the client a depersonalized proposal with errors in the calculations. The best result comes from combining the efficiency of AI with professional judgment and an understanding of the specific deal.
Key takeaways
- Preparing quotes and tenders is 4–20 hours of routine work — an ideal candidate for automation: repeatable, built on your data, and measurable.
- AI takes on data gathering, calculations, and the draft. The person keeps the review, the emphasis, and the decision.
- The savings are measurable: in our projects, automating this kind of work freed up hundreds of hours per month and hundreds of thousands of euros per year.
- Start with one pilot on a single proposal type — in 4 weeks, with no dedicated AI team.
- Data security and human review are requirements, not options.
The hardest part here isn't the technology — it's the first step. The easiest way to work out where to begin in your case is with a concrete example.