What problem does it solve?
For many business to business sellers, the formal response is the sale: a public tender, a request for proposal with hundreds of questions, a due diligence or security questionnaire from a customer's procurement team. Each one arrives with a deadline, a mandatory format and questions that have mostly been answered before, somewhere, by someone. Proposal teams spend their time finding the latest approved answer, chasing experts for the rest and reformatting, and sellers who are not bid professionals struggle to write a structured proposal at all.
The volume keeps rising. Loopio's 2026 benchmark of more than 1,500 companies reports that response teams now submit an average of 166 responses a year. The work does not scale by adding writers, and a wrong or outdated answer can become a contractual commitment.
- Loopio's 2026 RFP trends report, based on more than 1,500 companies, finds that response teams submit an average of 166 RFP responses a year.RFP Report: 2026 Trends & Benchmarks (2026)
How does it work?
- Read the request. The AI parses the RFP, tender or questionnaire (PDF, Word, spreadsheet or portal export) into individual questions, mandatory requirements, evaluation criteria and deadlines, and builds a compliance matrix.
- Find approved answers. For each question it retrieves the best matching answers from a curated answer library and past winning proposals, with the owner and the date each answer was last approved.
- Draft the response. It drafts answers tailored to the buyer's context and wording, marks which parts come from approved content and which are new, and flags questions it cannot answer with confidence.
- Route to experts. Security, legal, pricing and technical questions without an approved answer go to the right subject matter expert, and their approved answers flow back into the library.
- Review and submit. The proposal manager reviews the whole response, checks commitments and pricing, and submits it; the final version and the outcome are stored for the next bid.
- Audience
- Employee facing
- Autonomy
- Copilot
- Adoption
- Early adopters
- Channels
- Internal tools, Microsoft Teams
What is it worth?
Benchmarks are computed from the public deployments below: one data point per organization per KPI, with who made each claim.
| KPI | Median | Reported range | Data points | Claimed by |
|---|---|---|---|---|
| Cycle time reduction | Too few to pool | 80% | 1 | 1 vendor |
| Handling time reduction | Too few to pool | 75% | 1 | 1 organization |
| Hours saved | Not pooled | 93,000 hours | 1 | 1 vendor |
| Interactions handled | Not pooled | at least 200,000 | 1 | 1 vendor |
| Time saved per task | Too few to pool | 20 minutes | 1 | 1 vendor |
| Users served | Not pooled | 18,000 | 1 | 1 vendor |
Value drivers: Employee productivity, Revenue growth, Speed and cycle time, Compliance quality.
Indicative value
A business to business company that submits 150 RFP and questionnaire responses a year
USD 45,000 to USD 480,000
Proposal team time released per year
How this is calculated
Formula: responses * hoursPerResponse * timeReduction * hourlyCost. The low scenario uses every low input, the high scenario every high input.
| Input | Low | High | Basis |
|---|---|---|---|
| Responses submitted per year responses, responses per year | 100 | 200 | Around the Loopio 2026 benchmark of 166 responses a year cited on this page. Replace with your own volume. |
| Team hours per response hoursPerResponse, hours per response | 25 | 40 | Editorial assumption covering writers, reviewers and subject matter experts. Replace with a time study of your own bids. |
| Share of response time saved timeReduction, fraction of hours | 0.3 | 0.6 | Conservative against the evidence on this page (GroupeActive reports 75% less drafting time in a pilot, and Microsoft reports in its customer story that Copilot cut ICG's proposal response time by 80%), because both are small firms and review time does not shrink as much. |
| Fully loaded cost per hour hourlyCost, USD per hour | 60 | 100 | Editorial assumption for a blended proposal, sales and expert team. |
What it leaves out: Time value only. It leaves out the cost of building and curating the answer library, the software, and the upside that matters most: more bids answered and a higher win rate, which none of the organizations on this page has quantified.
Who already uses it?
4 public deployments, strongest evidence first. Grades: A regulator or audit, B the organization itself, C vendor case study, D anonymous or estimate.
Verdantas
United States · Professional services · 2026
Verdantas, an environmental science, engineering and consulting firm of about 2,500 professionals formed through acquisitions, built a technical resource agent in Microsoft Copilot Studio on data unified in Microsoft Fabric. For proposal teams the agent summarizes RFPs, retrieves previous and similar proposals, surfaces market specific marketing material and finds staff with the right skills and licences. Multi agent orchestration made the agent answer up to twice as fast; no proposal outcome is disclosed.
No outcome disclosed.
GroupeActive
France · Professional services · 2025
GroupeActive, a French SME that supports a network of about 80 independent experts, built the "GAIA Propale" agent in Microsoft Copilot Studio with the partner Witivio. The agent turns the expert's customer meeting notes into several chapters of a structured sales proposal, which the expert reviews and a proofreading committee checks. In the pilot a proposal takes about two hours instead of a day, and the time from proposal to signed contract fell by a factor of four.
- Handling time reduction: 75%, pilot, average drafting time per sales proposal
"Our members save an average of 75% on drafting time."
Claimed by: organization
Industrialized Construction Group
United States · Professional services · 2025
Industrialized Construction Group (ICG), a five person construction consultancy, uses Microsoft 365 Copilot to turn historical proposals into new proposals from a template instead of rewriting them for every customer, alongside marketing, onboarding and reporting tasks. Microsoft reports that Copilot reduced ICG's proposal response time by 80%.
- Cycle time reduction: 80%, proposal response time
"Already, Copilot has helped ICG reduce proposal response time by 80%, so the team can focus on customers instead of paperwork."
Claimed by: vendor
Microsoft
United States · Technology and software · 2024
Microsoft's Proposal Center of Excellence has run a Proposal Resource Library on the Responsive platform since 2020. Sellers and experts across the worldwide sales organization use its AI recommendations to find vetted answers for proposals, RFPs, RFIs and security, legal and compliance assessments, searching more than 18,000 question and answer pairs that the proposal team's knowledge managers and technical experts across the company keep current. The vendor reports 18,000 users and, counted over a wider pool of more than 20,000 resources, more than 200,000 uses of AI answers.
- Users served: 18,000, authenticated users of the library
"18K authenticated users leverage Responsive AI to quickly find proposal content and answers for security questionnaires, legal assessments, and highly technical bids"
Claimed by: vendor - Interactions handled: at least 200,000, uses of AI powered answers in proposals and assessments, cumulative
"The Field used AI-powered answers — drawn from over 20,000 resources — more than 200,000 times in sales proposals, RFPs, RFIs, and security, legal, and compliance assessments."
Claimed by: vendor - Time saved per task: 20 minutes, per search for proposal content
"The Field saves 20 minutes per search for proposal content, totaling more than $17M worth of time spent on customer relationships and building pipeline instead of searching for content."
Claimed by: vendor - Hours saved: 93,000 hours, cumulative seller hours, period not stated
"Sellers gained 93K additional hours to spend on customer relationships and building pipeline, instead of searching for answers and proposal content."
Claimed by: vendor
How do you implement it?
A model agnostic playbook: what to prepare, the order to build in, and what goes wrong.
Data you need
- A curated library of approved answers, with an owner and a review date per answer
- Past proposals and their outcomes, cleaned of client confidential details where needed
- Current product, security, certification and company fact sheets
- A list of statements that need legal or pricing approval every time
Systems to integrate
- Document storage such as SharePoint or Google Drive
- CRM for the opportunity, the account and the outcome
- Collaboration tools such as Microsoft Teams for expert routing
- Procurement portals or email for receiving and submitting responses
Complexity: Low
The technology is retrieval and drafting over documents the organization already owns. The real work is curating an answer library with owners and review dates, and agreeing who approves security, legal and pricing answers.
- 1
Curate the answer library before the model
Harvest answers from the last two years of bids, deduplicate them, and give every answer an owner and a review date. Microsoft's library was built as a curated, verified source so the AI output could be trusted.
- 2
Start with questionnaires
Security, due diligence and vendor questionnaires are repetitive and scored on accuracy, so they show value fastest. Move to narrative proposals once the library is trusted.
- 3
Show the source of every answer
Mark each drafted answer as approved content, adapted content or new text, with a link to its source, so reviewers spend their time on what is new.
- 4
Route the gaps to experts
Send unanswered or low confidence questions to named experts with a deadline, and feed their approved answers back into the library so the next bid starts further ahead.
- 5
Close the loop with outcomes
Store the submitted version with the win or loss and the buyer's feedback, and retire answers that keep losing or keep being rewritten.
Guardrails
- Answers drafted only from the approved library and named source documents, with citations
- Human approval for every statement about security controls, certifications, pricing, liability and service levels
- No client confidential information from past bids reused in a proposal for another client
- Disclosure of AI use when a buyer asks for it, as UK central government buyers may under PPN 017
KPIs to instrument
- Hours per response and elapsed days from receipt to submission
- Share of answers drafted from approved content without edits
- Number of bids answered per quarter and bids declined for lack of capacity
- Win rate on comparable bids, before and after
- Answers flagged as outdated or wrong in review
Human in the loop
The proposal manager owns the response and reviews every answer before submission. Subject matter experts approve new or changed answers in their area, and legal and pricing sign off commitments. The AI never submits a response.
Common failure modes
- Outdated answers
- The AI confidently reuses a security or certification answer that is no longer true, and it becomes a contractual commitment. Give answers review dates and let the draft show them.
- Generic proposals
- Drafts read the same for every buyer and lose on evaluation criteria. Make the buyer's own requirements and scoring the structure of the draft.
- Confidentiality leaks between clients
- Content from one client's proposal ends up in another's. Separate client specific material from reusable answers in the library.
- Nobody curates the library
- Without owners, the library fills with duplicates and the AI surfaces the wrong version. Budget time for knowledge managers, as Microsoft's proposal team does according to Responsive.
What are the risks and rules?
EU AI Act
Limited risk (transparency)
Drafting bid responses for staff to review is not listed in Annex III, and the buyer receives the seller's own document rather than interacting with an AI system, so the high risk tier and the Article 50(1) duty towards the buyer do not apply. Staff who chat with the agent must know it is an AI system, which an internal tool labelled as an AI assistant meets by design. Article 50(2) does apply to the drafting itself: the provider of a system that generates text must mark its output in a machine readable format as artificially generated, whether or not a person reviews the draft, unless the system only performs an assistive function for standard editing. A seller that uses a third party drafting tool relies on that tool's provider for the marking; a seller that builds its own agent that generates proposal text, as GroupeActive did with Witivio on Copilot Studio, can be the provider and then carries the duty itself. AI literacy under Article 4 applies in both cases, and the seller remains responsible for every statement in the submitted response.
Rules that apply
Guidance
- Regulation (EU) 2024/1689 (AI Act), Article 50, transparency obligations for providers and deployers of certain AI systems (European Union, Europe). Official text on EUR-Lex. Article 50(2) requires providers of AI systems that generate text to mark the output as artificially generated in a machine readable format, with an exception for systems that only perform an assistive function for standard editing. Article 50(1) covers systems that interact directly with people, here the staff who use the agent.
- PPN 017: Improving transparency of AI use in procurement (UK Cabinet Office, Europe). Published 17 February 2025. Does not prohibit suppliers from using AI to write bids, but lets UK central government departments, their executive agencies and non departmental public bodies ask suppliers to disclose it and do extra due diligence, because AI can introduce misleading statements through hallucination. Other public sector buyers may choose to apply it. For procurements commenced, or contracts awarded, before 24 February 2025, the earlier PPN 02/24 applies.
Controls to put in place
- An answer library with an owner, an approval status and a review date per answer
- Mandatory human approval of security, legal, pricing and service level statements
- Access controls that keep client confidential bid content out of other clients' drafts
- A record of which answers were AI drafted and who approved them, per submitted bid
When it went wrong elsewhere
- Incident 1193: Purportedly Taxpayer-Funded Deloitte Report for Australian Government Contains Alleged AI-Generated Citations and Fabricated Legal Quote. A consultancy report for Australia's Department of Employment and Workplace Relations contained nonexistent academic references and a misquoted court judgment; the firm acknowledged using generative AI, issued a corrected version and partially refunded the fee. It was a client deliverable rather than a bid, but it shows what unchecked AI drafted content costs when it reaches a client under the firm's name.
Frequently asked questions
- How much time does AI save on RFP responses?
- The public figures come mostly from small teams: GroupeActive reports that, in a pilot, its members save an average of 75% of drafting time on sales proposals, and Microsoft reports in its customer story that Copilot cut ICG's proposal response time by 80%. At scale, Responsive reports that Microsoft's sellers save 20 minutes per search for proposal content. Expect less on complex bids, where expert review dominates.
- Can AI write a whole tender response on its own?
- It can draft most of it from approved content, but a proposal manager should review everything, and security, legal and pricing statements need an expert's approval because they become commitments. Some public buyers may ask suppliers to disclose AI use in their bids: UK central government guidance (PPN 017) gives buyers example disclosure questions for this.
- What matters more, the AI tool or the content library?
- The library. AI retrieval and drafting are only as good as the approved answers behind them, which is why, as Responsive describes it, Microsoft relies on the proposal team's knowledge managers and on technical experts across the company to keep more than 18,000 question and answer pairs current.
How to cite this page
Blits.ai AI Use Case Library, "AI for RFP, tender and sales proposal response drafting", last verified 27 September 2026, https://www.blits.ai/ai-use-cases/rfp-and-proposal-response-drafting. Licensed under CC BY 4.0. Method: how we verify use cases.
Changelog
- 27 September 2026: First published