Best 5 RFP Software for Insurance Companies (2026 RANKED)

A 300-question insurance RFP can quickly become a coordination exercise across legal, compliance, security, underwriting, operations, and sales. The writing itself is often not the hardest part.

The real challenge is finding the right information, knowing whether it is still approved, and getting the right people to review it before the deadline.

We compared five RFP software platforms that help insurance teams reduce that back-and-forth and run a more structured response process.

5 Best RFP Software for Insurance Companies in 2026: At a Glance

For insurance companies, the biggest distinction is not simply which platform can produce a first draft. It is whether that draft can move safely through legal, compliance, information security, product, and executive review while preserving the evidence behind sensitive statements.

AutoRFP.ai brings RFPs, security questionnaires, and DDQs into one governed workflow, with source-grounded answers, Trust Scores, human routing when approved evidence is missing, and zero library maintenance.

Want to test that against your own insurance RFPs and questionnaires? Book a demo with AutoRFP.ai and run a two-week proof of concept using your real response material.

1. AutoRFP.ai: Best for Defensible RFP, DDQ, and Security Questionnaire Response Automation for Insurance

AutoRFP.ai is the accuracy-first AI platform for RFPs, security questionnaires, and DDQs: every answer is source-grounded and citable, with zero library maintenance.

That model is particularly relevant to insurance companies because responses may move through proposal teams, underwriting or product specialists, security, legal, compliance, risk, and executive reviewers before they reach a buyer. The challenge is therefore not only generating content, but proving where sensitive statements came from and controlling who can approve them.

AutoRFP.ai drafts from approved company material, exposes the evidence behind each response, and leaves unsupported questions for human review rather than filling gaps with an unverified answer.

Key Features

1. Source-Grounded Answers With Trust and Feedback Scores

AutoRFP.ai generates responses from approved company content rather than relying on unrestricted model knowledge. A multi-model workflow handles retrieval, re-ranking, drafting, redrafting, and checking before the answer reaches a reviewer.

Every answer includes source citations and a Trust Score showing the strength and freshness of the supporting evidence. A separate Feedback Score evaluates whether the response fully addresses what was asked.

When approved information cannot support a response, the platform leaves the answer unresolved and routes it for human review instead of guessing.

2. Sequential Approval and Audit Controls

Insurance RFPs often involve contributors with different approval authority. AutoRFP.ai supports named editors and reviewers, sequential approval workflows, requirement-level comments, version history, and audit trails.

Project dashboards show progress, unresolved responses, comments, deadlines, and ownership.

This keeps the decision history attached to the response itself. Teams can see who supplied information, who changed it, and who approved the final customer-facing answer without reconstructing the process from email threads.

3. Current-Source Governance With Zero Library Maintenance

AutoRFP.ai connects approved knowledge from systems such as SharePoint, Confluence, Google Drive, OneDrive, Notion, Box, Seismic, Zendesk, and previous submissions.

Semantic search finds information by meaning. The system can also identify conflicting or superseded sources and prioritize current approved material.

Approved responses feed future work automatically. Insurance teams retain ownership, renewal schedules, and freshness controls without continually maintaining folders, snippets, tags, and taxonomies manually.

4. Complex Document and Portal Handling

AutoRFP.ai can import Word, PDF, and Excel questionnaires, including nested tables, hidden tabs, macros, dropdowns, and validation fields.

The Portal Agent also helps teams complete questionnaires inside online procurement and security portals.

Finished responses can be exported back into the buyer’s required Word or Excel structure, preserving the original formatting rather than creating another reformatting step.

5. Enterprise Security and Data Residency

AutoRFP.ai is ISO 27001 certified and SOC 2 Type II audited annually. Customer data is not used to train AI models, is isolated by the tenant, and does not leave the AutoRFP.ai environment during model inference.

Insurance companies can choose regional data residency in the US, EU, or AU. Private and single-tenant deployment options are also available for organizations with stricter isolation and governance requirements.

6. Enterprise Integrations and Connected AI Workflows

AutoRFP.ai connects with the systems insurance teams already use, including Salesforce, SharePoint, Confluence, Google Drive, OneDrive, Slack, Microsoft Teams, Okta, and Microsoft Entra.

Its MCP server also connects approved AutoRFP.ai knowledge with AI tools such as ChatGPT, Claude, Microsoft Copilot, and Google Gemini. This lets teams access governed RFP knowledge from the tools they already work in while preserving source grounding and access controls.

Pricing

Where AutoRFP.ai Shines

• Defensible customer responses: Important statements remain tied to visible approved evidence rather than unexplained AI output.

• Governed insurance workflows: Sequential reviews, permissions, versions, and audit trails support formal approval structures.

• One response platform: RFPs, security questionnaires, and DDQs can be handled without maintaining separate systems for each workload.

• Current-source control: Source conflicts, freshness, ownership, and approvals remain governed while repetitive library administration is reduced.

• Cross-functional participation: Unlimited-user positioning makes it easier to involve occasional legal, security, product, compliance, and executive reviewers.

• Complex submission support: Teams can work with difficult spreadsheets, formatted Word documents, and buyer portals without rebuilding the final submission manually.

Where AutoRFP.ai Falls Short

• Long-form persuasive writing: Insurance teams whose primary requirement is highly narrative, creative proposal writing may prefer a specialist such as AutogenAI.

• Standalone Trust Center workflows: Conveyor has a stronger specialist focus when document sharing and customer self-service security reviews are the main requirement.

• Buyer-side procurement: AutoRFP.ai is designed for companies responding to RFPs, not procurement departments issuing and scoring supplier RFPs.

Customer Review

Sara H said, “Our RFP process was highly manual, with no response library and team members having to painstakingly answer questions for each new RFP, a tedious and time-consuming task. AutoRFP.ai completely transformed this workflow by automatically filling out about 90% of the first draft using responses from previous RFPs, allowing us to jump much further into the process.”

Kristen C., Marketing Manager, said, “We are new to AutoRFP but so far we are really enjoying it and can see the value it will bring our company. Setting up new projects and working through them is so much easier and controlled than working from spreadsheet versions prior.”

Raphael Schmideg, Chief Operating Officer at IMTC, said, “Reaching the RFP stage with clients is now a smooth process. With a 90% automation rate, we can quickly produce a first draft based upon previous responses, making the RFP process efficient and stress-free.”

Mihai Popa, Bid Manager at FintechOS, said, “AutoRFP.ai is saving more than 60% of the time allocated before using the tool. The stakeholders involved can allocate this time to more strategic tasks.”

Who AutoRFP.ai Is Best For

• Large insurance carriers: Organizations coordinating formal responses across multiple departments and review layers.

• Enterprise-selling insurers: Teams regularly answering complex customer RFPs alongside security and governance questionnaires.

• Regulated response teams: Organizations where claims may later need to be explained to auditors, regulators, security teams, or internal compliance leaders.

• Cross-functional proposal teams: Groups involving bid managers, legal, risk, product, security, and subject matter experts.

• Teams replacing fragmented workflows: Insurers currently managing responses across spreadsheets, shared documents, legacy libraries, and isolated AI tools.

Video transcript

Did you know the average RFP can take thirty-two hours of manual grueling work? Now, in this video, in under ten minutes, I'm gonna show you how you can use AI RFP automation to drastically reduce the amount of time it takes to get to a first draft for your RFP, DDQ or security questionnaire using AutoRFP.ai. Sick. Let's jump into it. So at AutoRFP, we're an AI RFP software automation platform, across the globe with hundreds of customers using our software every day, battle-tested AI to help you automate RFPs. First, what's the problem? So an RFP or request for proposal or due diligence questionnaire or security questionnaire is a pain felt across all industries, whether it's construction, software, technology, finance, healthcare, anyone selling

to government or private businesses. And these glorified question and answers take hours and hours for people to complete, for them to win new business. It's a crucial part for your business to win enterprise and government contracts, which really help you grow sustainably and quickly. But when you go to bid on one, you are met with the RFP. Average response times are thirty to forty hours, usually involve five to eight people, seventy percent of content is reused but hard to find, and the average win rate across all industries is only forty-three percent. So you're spending hours with uncertainty that you may win, which is where efficiency and writing better responses, leveraging AI helps you win more faster. Now, looking into RFP automation, you have a number of options.

You can pick up a legacy RFP software. They've been around since the late nineties. They brought software to the RFP problem. Effectively, a glorified question and answer bank, like a database. You upload Q&A pairs, and then they try to use keyword matching to find the most relevant to then help you answer questions that you get in your new RFPs and tenders. You can also do AI builds yourself. So you might use ChatGPT or Claude, and you can see our other videos about how you can potentially use them. But effectively, you hit a ceiling where it's hallucinating, it's taking more time now to fix things than it should, and just doesn't have enough context to find the right answer most of the time. Or you can choose an actual AI native leader like AutoRFP.ai. Built from AI from the get go and have built the engine around zero

hallucination, multimodal architecture to leverage the latest models across all your major providers, library-less approach, so it doesn't take a lot of time to maintain the system, really high automation rates and enterprise security built in from day dot. So why do teams choose AutoRFP.ai? The reason is your knowledge is always current. We integrate with over twenty different systems, and we pull in from all your various file management and different software to make sure that your data is always up to date, and you don't have to maintain it across multiple different places. We're most accurate in the category because we leverage the different models, including specialized re-ranker models, embedding models, search models, and your large language models where they're best. And our team of over fifteen software engineers and AI engineers make sure that this is battle-tested, evals are correct, and it produces the correct answer based off your source context. And it's one platform for every stage of the RFP journey, from intake to new RFP

to AI-powered go/no-go to drafting to reviewing and using agents to review and update your RFP response, translation to collaboration across SMEs and different team members, ensuring that they can easily collaborate in an easy-to-use platform, and then exporting as well. Let's start with the AI native auto library. This is the core of the platform where your different content sources and past projects and up- and web scraping all live in the one place and- Any question that comes up, whether it's in an RFP or a team member asking the question, can be automatically answered with trust-based scores, specific semantic search, and ensures that the correct answer is found and used to then answer and generate an appropriate response. Then effectively, you upload a blank RFP.

The AI-powered response engine then automatically generates responses, translates it and everything to have the correct answers. Then your team can very easily edit, review, integrates with Slack and Teams, and everyone's notified on project deadlines. Now, I've spoken enough. Let's jump into the actual product, and you can see AI RFP automation from the start. We start by creating a project, which is a new RFP. We've got our portal agent that can scrape your requirements from web portals like SAP, Ariba and others, automatically ingesting those answers into your AutoRFP.ai instance, and then automatically drafting responses for you to easily enter back into the portal. Or you can upload a zip that contains a PDF, Excel, Word doc of your RFP and import that into the platform.

First we have our AI go, no-go. This ask different questions of your RFP based on your company context to ensure that should we actually bid on this RFP before we start it. It'll automatically grab out key details and link it to our CRM via our integration with Salesforce and so on. And here it's answered each question, and you can see it has confidence levels, it has trust built in, and you can then look back and see where the original source and what, for instance, table or other information. Our AI importer automatically selects every requirement, child requirements, dropdown pick lists response cells, everything else that's required for that RFP. We can manually change it if needed, but it automatically pulls that in. Then we can choose what content from our library or just choose every content, and our intelligent tagging and hierarchy system will make sure that the most relevant content is used for that response. And then I can create my project.

Next, the AI response engine then automatically starts sourcing the correct content from your auto library, re-ranking and finding the most relevant information, using that to then draft, redraft, and edit responses vi- with AI, and then provide those responses back to you in matter of seconds. And you can see here my thirty or so requirements automatically being filled out across the entire project. It's chosen the relevant pick lists, and each one of these have trust scores that I can understand further where this came from. It also has our AI-powered feedback score, and this is where AutoRFP is different to other systems. We don't wanna just help you source the correct answers. We wanna help you write better responses. And this goes into our feedback loop, where as you use the platform and write better responses, the AutoRFP system learns from those responses, and continually, your responses get better to help you win more faster.

Here we can do inline comments, so I can notify my team and so they can jump in, get notifications. I can submit, approve, add attachments, and everything else I can do in this platform. Finally, we have our project overview, which is our project management HQ for this particular RFP, making sure everyone understands deadlines. You can send reminders out to team members and just know when something needs to be completed by and when completed by and who is completing it, making sure that your RFP response is submitted on time and you're not faced with five PM Friday deadlines, calling someone to make sure you can get the correct answer to the correct question that's our short introduction to AutoRFP.ai. There's a lot more in RFP automation and AI RFP software that you can learn but feel free to reach out to our team. We'd love to provide a detailed demonstration to you so you can understand if this is a good fit for your business Already, AutoRFP.ai is in forty-four-plus countries across the globe with hundreds

of customers across different industries like technology, finance, and healthcare, and our customers are winning more faster. One of our customers like Shana Sweeney from SugarCRM won fifteen of their top twenty-five enterprise customers using AutoRFP.ai. They're using AI RFP automation to win more today, and it's a competitive advantage for their businesses. Our pricing is incredibly transparent. You can find more information on our website, to get in touch with our team, head over to our website, AutoRFP.ai. Book in a demo today and learn more and see if we can help you win more faster.

2. Loopio: Best for Mature Insurance Content-Library Workflows

Loopio is a strong option for insurance companies that already operate a structured proposal function with dedicated content owners.

Its mature content-library model gives teams a central place to store approved responses, establish review cycles, collaborate with subject matter experts, and reuse information across RFPs, DDQs, and security questionnaires. This is a genuine strength for insurers that already have people and processes in place to maintain that library.

The tradeoff is that the library still needs to be managed. Automated review reminders make maintenance more structured, but they do not remove the content-management responsibility.

Key Features

• Centralized response library: Stores approved answers and supporting content for reuse across projects.

• AI-assisted drafting: Uses library material and connected sources to recommend or prepare responses.

• Content ownership: Assigns owners and review schedules to frequently reused information.

• Collaboration workflows: Coordinates proposal contributors and subject matter experts during active responses.

• Confidence and source indicators: Helps reviewers determine which suggested answers deserve additional review.

Pricing

Where Loopio Shines

• Established operating model: Well suited to insurers with mature bid and proposal processes.

• Content organization: Provides a structured way to manage large volumes of reusable approved information.

• Product maturity: Offers an established platform, polished workflow, and large existing customer base.

• Review discipline: Makes recurring content reviews and ownership easier to formalize.

Where Loopio Falls Short

• Ongoing library upkeep: Content still needs to be organized, tagged, reviewed, and refreshed.

• Dedicated ownership: Large insurance libraries may require a content manager or proposal-operations team.

• Content drift: Changes to products, controls, policies, or disclosures require teams to keep reusable answers synchronized.

• Large reviewer groups: Insurers with many occasional contributors should evaluate how licensing affects wider participation.

• Library-maintenance burden: Teams looking to reduce traditional content-management work may prefer a self-updating knowledge model.

Customer Review

A Finance Associate said, “I have been using Loopio for ~3 years and it has been a great experience of using this product. As my team is mostly involved in DDQs / RFPs, Loopio has been fully integrated in response populating process and overall enhance team productivity and accuracy.”

A Senior Federal Sales Proposal Administrator in the insurance industry said, “I dislike having to reformat things in my questionnaire after I export, I dislike that I cannot always fix mapping that I do manually and that I can never fix automapping.”

Who Loopio Is Best For

• Established insurance proposal departments: Teams with repeatable RFP processes and substantial reusable content.

• Dedicated content managers: Organizations that can actively maintain response libraries.

• Library-first teams: Insurers that want curated approved Q&A content to remain the center of their response process.

• Mature governance functions: Companies with defined content ownership, review schedules, and proposal controls.

3. Responsive: Best for Complex Insurance Proposal Operations

Responsive is best suited to larger insurance companies that need extensive project management, enterprise reporting, content governance, and professional-services support.

Its platform goes beyond response generation into project coordination, analytics, Trust Center capabilities, and adjacent procurement workflows. That breadth can be valuable for insurers operating across multiple business units, regions, product lines, and response teams.

The same breadth can make the platform heavier than necessary for insurers whose primary goal is accurate RFP and questionnaire automation.

Key Features

• Enterprise project management: Coordinates owners, deadlines, sections, reviews, and approvals across concurrent responses.

• Content management: Stores verified proposal and questionnaire information for reuse.

• AI-assisted responses: Generates drafts from existing content and connected sources.

• Reporting and analytics: Tracks project activity, workload, and proposal performance.

• Trust Center: Supports controlled sharing of security and compliance documentation.

• Professional services: Provides implementation and operational support for complex enterprise deployments.

Pricing

Where Responsive Shines

• Large-scale proposal operations: Strong fit for insurers running many complex responses simultaneously.

• Project-management depth: Provides detailed coordination and visibility across distributed teams.

• Reporting: Gives proposal leaders and executives more operational insight into workload and performance.

• Professional-services support: Useful for insurers that need a structured enterprise implementation.

• Broader workflow coverage: Supports adjacent assessment and procurement use cases beyond standard proposal responses.

Where Responsive Falls Short

• Implementation weight: Large deployments can require configuration, process design, training, and dedicated ownership.

• Feature complexity: Smaller insurance response teams may not need the entire platform stack.

• Content maintenance: Verified library material still requires ongoing ownership and review.

• Lean-team fit: A focused proposal team may find the platform heavier than a purpose-built response workflow.

• Source verification: Teams prioritizing answer-level evidence should test how source visibility and unsupported-answer handling perform on their most sensitive questions.

Customer Review

A Sales Enablement Operations Manager in the insurance industry said, “Responsive has allowed us to decrease time spent on RFPs by 49%, while allowing us to increase our invite to finalist rate by 25%. This tool alone has saved the company countless dollars and has played an active role in helping us close business.”

A Sales and Marketing Manager in the insurance industry said, “Wish there was one more level for sorting responses between collections and tags. When copying/pasting responses directly into a Word document, the search term has the odd ‘bubble’ spaces around the word you used to search, which formats differently than a regular space in Word and has to be manually corrected. It was quickly corrected, but the search bar was briefly moved and made much smaller with an update. Please don’t do that again :)”

Who Responsive Is Best For

• Large insurance enterprises: Organizations with several business units, regions, or proposal functions.

• Mature bid operations: Teams that need advanced project management and reporting.

• Complex governance environments: Insurers with layered review structures and formal operating processes.

• Broad response organizations: Companies whose requirements extend beyond standard RFP and questionnaire completion.

4. AutogenAI: Best for Narrative-Heavy Insurance Proposals

AutogenAI is a strong option for insurance teams whose competitive responses depend heavily on persuasive long-form writing rather than structured questionnaire automation.

The platform supports bid qualification, AI-assisted writing, research, review, and proposal development. AutogenAI is particularly strong at developing persuasive long-form narrative bids and tenders, making it useful when an insurer needs to build a compelling case rather than answer hundreds of structured requirements.

Key Features

• Bid qualification: Helps teams evaluate opportunities before committing significant writing resources.

• AI proposal writing: Supports creation and improvement of narrative bid and tender responses.

• Proposal outlining: Helps teams structure compliant response documents around buyer requirements.

• Research support: Assists writers in gathering context for proposal development.

• Review workflows: Supports validation and refinement before submission.

Pricing

• AutogenAI does not publicly disclose a fixed starting price.

Where AutogenAI Shines

• Long-form writing: Particularly strong when proposal success depends on persuasive, differentiated narrative.

• Complex tender responses: A good fit for teams producing substantial bid documents rather than primarily short-form questionnaires.

• Writing-team support: Helps proposal professionals develop structure, messaging, and content rather than simply retrieving reusable answers.

• Qualification support: Adds value earlier in the opportunity process before drafting begins.

Where AutogenAI Falls Short

• Structured questionnaire focus: Insurance companies dominated by security questionnaires, DDQs, or tightly controlled Q&A should test whether its workflow fits that workload.

• Answer-level defensibility: Teams handling regulated responses should examine source traceability and what happens when evidence is insufficient.

• Security questionnaire consolidation: Its primary strength is proposal writing rather than specialist InfoSec questionnaire automation.

• Library-maintenance objective: Teams specifically seeking a governed, self-updating response knowledge system should compare the underlying content workflow carefully.

• High-stakes Q&A: Insurers should test source verification using sensitive compliance, security, and operational-control questions rather than judging the platform only on narrative quality.

Customer Review

AutogenAI has limited independent customer feedback available across major third-party review platforms. Broader online feedback generally highlights faster proposal drafting and useful AI-assisted bid workflows, while some users note a learning curve, occasional usability friction, and the need to review AI-generated content carefully.

Who AutogenAI Is Best For

• Narrative-heavy insurance bid teams: Proposal writers producing substantial persuasive submissions.

• Tender-focused insurers: Teams competing through formal written proposals where prose quality matters heavily.

• Dedicated proposal writers: Organizations with specialist writers responsible for developing customized bid narratives.

• Teams prioritizing proposal storytelling: Insurers whose workload is less dominated by structured questionnaires and repetitive Q&A.

5. Conveyor: Best for Insurance Security Reviews and Trust Center Workflows

Conveyor is a specialist security-review platform built around questionnaire automation, customer Trust Centers, document sharing, and portal-based security workflows.

That focus can be highly useful for insurers whose biggest response bottleneck sits with security or GRC rather than the proposal team. Customers can access approved security information through the Trust Center, while security teams can automate recurring questionnaires and manage document requests more systematically.

Its specialization is also the main tradeoff. Conveyor is not designed to replace a broad RFP and DDQ response platform when commercial, product, implementation, and narrative sections matter equally. For that wider shortlist, see how teams evaluate options beyond Conveyor’s security-review focus.

Key Features

• Security questionnaire automation: Generates responses for recurring customer security assessments.

• Agentic Trust Center: Gives customers controlled access to approved security information and documentation.

• Portal completion: Supports questionnaire work inside browser-based customer portals.

• Document sharing: Manages controlled distribution of security and compliance evidence.

• Customer self-service: Reduces repetitive requests for commonly requested security material.

• Salesforce workflows: Connects customer-trust activity with commercial processes.

Pricing

Where Conveyor Shines

• Security specialization: Purpose-built around the workflows of InfoSec, GRC, and customer-trust teams.

• Trust Center experience: Strong choice when self-service security documentation is part of the customer journey.

• Document sharing: Useful for controlled access to security and compliance evidence.

• Portal-heavy reviews: Designed for teams frequently completing external security assessments.

• Focused implementation: A narrower specialist can be attractive when customer security reviews are the single major bottleneck.

Where Conveyor Falls Short

• Broader RFP management: Commercial and narrative proposal sections are outside its core specialization.

• DDQ coverage: Insurance and financial-services DDQs are not a central product focus.

• Proposal strategy: Qualification, win themes, and full proposal management are secondary use cases.

• Cross-functional consolidation: Insurers may still need another platform for broader RFP response work.

• Original-format workflows: Teams handling complex Word and Excel submissions should test full import and export fidelity.

Customer Review

Reviewers often highlight Conveyor’s time savings, ease of use, and helpful support, while some report occasional AI inaccuracies, slower response times, and workflow or usability limitations.

Who Conveyor Is Best For

• Insurance security teams: Organizations dealing with frequent customer security questionnaires.

• GRC functions: Teams responsible for security evidence, customer trust, and recurring assessments.

• Trust Center users: Insurers that want customers to self-serve approved security information.

• Specialist buyers: Organizations comfortable keeping security-review automation separate from broader RFP management.

How to Choose the Right RFP Software for Insurance Companies

Insurance companies should evaluate RFP software based on how safely information moves from internal knowledge to an approved customer response. Drafting capability matters, but so do evidence, ownership, review controls, document fidelity, and visibility into whether automation is actually reducing specialist work.

1. Test How the Platform Handles High-Stakes AI Answers

Insurance responses involving security, privacy, operations, and compliance need to be verifiable, not just convincing. Responsive and APMP’s 2026 State of Strategic Response Management Report highlights this risk, with Vodafone Senior Bid Manager Ken Lebek cautioning that AI is “still hallucinating a lot” and stressing the importance of the knowledge foundation behind the LLM.

Test platforms using sensitive questions where approved information may be incomplete, conflicting, or unavailable. Reviewers should be able to verify the evidence behind an answer and clearly see when human input is required.

AutoRFP.ai approaches this through source citations, Trust Scores, content-age signals, and human routing when approved evidence cannot support the answer. That makes the evaluation about verifiability rather than whether the AI can produce plausible prose.

2. Match the Platform to Your Actual Response Mix

A company answering mostly security questionnaires has a different software requirement from one producing complex multi-section proposals.

Conveyor may be the cleaner choice when Trust Center and customer-security reviews dominate. AutogenAI is stronger when persuasive long-form proposal writing is central. Loopio works well when a dedicated content team already manages a mature response library, while Responsive fits larger operations that need extensive coordination and reporting.

Do not pay for breadth your team will not use, but avoid choosing a specialist if it forces you to maintain a second system for half of your response workload.

3. Test Ownership Across Business Units and Review Functions

Insurance companies may have different contributors responsible for product, security, legal, risk, operational, and commercial information. The platform should make those boundaries visible.

Check whether you can assign content owners, control permissions, route responses through sequential reviews, preserve version history, and produce an audit trail showing how the final answer was approved.

Responsive offers deep enterprise workflow capabilities, while Loopio fits teams that already use defined library ownership and recurring content review. The deciding factor is whether the software reflects your real internal governance model.

4. Measure What the Automation Actually Removes

A tool can generate a lot of text while still leaving reviewers with almost the same workload. Insurance teams should therefore measure how generated responses are actually used after they reach a human.

Look at how many answers are accepted with minimal change, require substantial rewriting, or are completed manually. Also examine where reviewer effort remains concentrated.

AutoRFP.ai’s Automation Reporting breaks response work down by automation level and can report results by project and time period. Its broader reporting also tracks workload and capacity, giving response leaders a more concrete way to determine whether AI is creating usable capacity rather than simply increasing draft volume.

5. Test the Full Submission Workflow, Not Just the Editor

Use a real insurance RFP or questionnaire during the proof of concept. Include the messy parts: multi-tab Excel files, dropdowns, formatted Word documents, several reviewers, missing evidence, and an external portal if your customers use one.

Then evaluate the complete journey from intake to final submission. Check question extraction, source verification, assignments, approvals, formatting, portal completion, and whether the finished response returns in the format the buyer expects.

A strong drafting experience is useful. A strong end-to-end response workflow is what prevents the proposal team from rebuilding half the submission manually after the AI work is finished.

Future of RFP Automation for Insurance Companies

The future of insurance RFP automation will be judged less by how much text AI can generate and more by how well the response operation controls risk, preserves specialist capacity, and turns bid activity into useful business information.

AutoRFP.ai’s 2026 Proposal Win Rate Report found that AI adoption alone does not predict stronger proposal performance.

Higher-performing teams combine automation with mature structure, governance, content operations, and customer insight.

Content libraries by themselves also showed only a weak relationship with better outcomes, reinforcing that technology works best when it improves the wider response operating model.

• AI will become a verification layer: Generating a response will become the easy part. Insurance teams will increasingly expect the system to show supporting evidence, identify weak sources, detect conflicts, and escalate uncertainty before approval.

• Automation will be measured by reviewer effort: Leaders will move beyond counting drafted questions and look at edit levels, specialist capacity, manual work, and where human intervention remains necessary.

• Bid data will feed product and risk decisions: Repeated non-compliance or missing requirements across RFPs can reveal patterns that deserve attention beyond the proposal team. Gap Analysis can turn recurring requirements into structured information for security, product, compliance, and leadership discussions.

• Knowledge governance will become more dynamic: Static repositories will increasingly give way to connected systems that identify stale or conflicting information and learn from newly approved responses without removing ownership and review.

• Specialists will validate more and draft less: The Proposal Win Rate Report recommends moving subject matter experts toward validation rather than primary drafting. For insurers, that means legal, security, risk, and product specialists can spend more time confirming high-risk answers instead of repeatedly reconstructing standard responses.

• Tool consolidation will become a strategic choice: Some insurers will continue using specialist products for Trust Centers or long-form proposal writing. Others will favor platforms that consolidate RFPs, security questionnaires, DDQs, collaboration, governance, and reporting into one response operation.

For insurance companies, the end state is not fully autonomous proposal writing. It is a response system where automation handles repeatable work, specialists retain control over high-stakes information, and leadership can see whether the process is improving quality, capacity, and commercial execution.

Video transcript

You've just received that monster RFP. It's a lot of work, and you're excited to dive in and potentially win this massive contract. You've started using AI, but how do I actually win? What is the RFP response that I need to write to win this deal? That's what takes from basic level of proposal writing to what wins. I'm Rob from AutoRFP.ai. We're an AI RFP software. I personally complete and win RFPs on the daily, and I'm keen to dive in today about using AI for an RFP response that actually helps you win RFPs. I'm gonna be covering win themes. I'm gonna be covering leveraging customer insights to write strategic narrative that helps you actually win RFPs. Yes, we're gonna be talking about AI automation and saving time,

but it's not just about that. It's not about doing an RFP as fast as possible with as little effort as possible and just putting out slop into the world. It's about writing and winning RFPs. But first, as I did say, it's about RFP automation with AI for our RFP response process. It's about automating the mundane. Before you dive into how you can use AI to help you win RFPs for RFP response, take a step back and think about, what are the activities I do related to RFPs that don't actively help me win RFPs? So automate the mundane. AI's real job in an RFP, isn't to do what you do well and what humans do well, and that is writing strategic narrative. It is to do what it does well, and that is hunting and pecking throughout

your past responses, automating kind of the basic responses and really making sure that those are compliant as Jasper Cooper, our CEO and co-founder at AutoRFP.ai, put in our proposal win rate report for 2026, the real advantage isn't automating content, it's what teams do with the time they get back. So automate as much as you can on the mundane So firstly, these are some clear things you can hand to AI from the start. The clear yes or no. Does your product or service do this? And it has a yes box or a tick box or a radio button or a drop-down selection. Yes, . AI should be completing those 99% of the times. Of course, having a human to review if appropriate, but that is where AI is good, the black and white. Company information all the content forms you get about company legal name, company entity, where the office is based, and so on.

If there isn't a place to kinda put your flair there, of course, just basic information, AI is good for that. Boilerplate. Then you've got boilerplate and your security and compliance questions. Across our customer base, 63% of every AI-generated answer is approved with zero to one-word changes. That is 63% of AI-generated answers are perfect. A human still reviews them, but it doesn't require any manual editing. That's freeing up enormous time for teams using our software and the other software out in the market to then, take that time back to do what wins. So what are those activities you can do to help win? First Automate the answer everyone gives and write the answer only you can give. But first, let's cover off what a great answer looks like, and I'll give you both a good and a bad example. So what does a great answer look like?

And this is subjective, of course, to your industry, to your country your buyer. It's all dependent on so many factors. My background is in technology RFPs. That's where I've spent ten years working and selling and writing RFP responses across local government, national government, state government, as well as private enterprise RFPs in Australia, in the UK, in Europe and it is very subjective what a great answer looks like. But I'm gonna take on a couple of core principles that are gonna help you think about what a great answer looks like for your use case. So leads with the verdict. I'm a big believer in front-running the value of the response in the first sentence or two. What that means is effectively, if we're thinking about how humans read, and especially if your job is to read a handful of RFP responses, it's pretty

hard work to continually stay focused and read an entire response and remember everything that you read in there. You wanna make sure that it's easy for the reader to understand the value in your response, and tick off and give you the points that you need in that evaluation criteria. Second, mirror the buyer's words. This is where your understanding of that industry, of that country, of that buyer goes into how you talk about the response. Three, specific enough, no competitor could paste it. Again, imagine you have an evaluation criteria and you are marking this RFP, and you have two responses that look exactly the same. How are you gonna differentiate? That's where being specific enough no competitor could could paste it is so important to make your response stand out, short and scannable. Now, short is dependent on the type of RFP or RFI that it may be and what they're expecting for responses. Make it scannable. Make it a pleasure to read. Don't make it giant block paragraphs that are incredibly hard, again,

for that evaluator to give you the marks for that response. Make it have bullet points. Make it have flowing paragraphs. Make it have a summary or conclusion at the end if reasonable. Make it short and concise and scannable this is probably the biggest sin I see in executive summaries. Someone writes an executive summary, maybe it's the CEO has the standard template one that they use, and it's all about them. It's all about your company, it's all about your experience, and it's boring to read. Make it about the buyer. Easy way to do this is scan the left margin. How often do the sentences start with we, our, your company's name, and so on? How often is this talking about you? Leverage your customer insights and incorporate it into your win themes to make it about them. Tie your solution into the pain and the problems that they are living, and write about them, not about you. But you're tying everything back into their world because it's just more contextual, and it's easier for them to map your response to the evaluation

criteria and how it meets their stated objectives and goals for the RFP. So this is what a great answer can look like. This answer was actually is using social proof. It's one of our answers that we would write, and effectively it's talking about a migration. So SugarCRM migrated from Qvidian to AutoRFP.ai in 2024 and deployed in two weeks. The first sentence has the value. Social proof time. Because we're thinking about migrations. The buyer might be thinking about how long does that take? What's the risk here? How long does it take is answered in the first sentence, and social proof helps alleviate the risk. Then the next three dot points, again, incredibly skimable and readable and has numbers to draw attention. So this requirement was regarding do you have any customers who have migrated? A forgettable answer. AutoRFP.ai.ai, so leads with me, leads with us. Maintains version control automatically through the History tab. First of all, a lot of flowing commas, a pretty long sentence. We've kinda cut off the response, but it keeps going.

No hook, no numbers no so what for the buyer. And effectively it's correct, and for a functional question in a response, it could be a great response if it was looking for a black-and-white response. So it's a forgettable response. So how would we improve this response? We might say Audibility and traceability is core to the platform. This extends to the history tab in which… and then you might use then dot points to list out all the relevant comma points there. So I'm gonna give you some concrete examples of how you can use AI to incorporate win themes and customer insights to help you write winning RFP responses. First of all, the data. We did a survey of over a hundred winning bid teams and asked them what do they do to win. These are teams that win more than 50% of the RFPs they bid on. 71% of high win teams use win themes, 42% of low win teams use win themes.

So a clear distinction, the difference there. This is a strategic part of your RFP response use your intuition and your knowledge of the buyer, your knowledge of your company and your products and services to really fine-tune it. But it can definitely be helpful in thinking of ideas and going back and forth and helping you once you've generated those win themes, actually deploying the win theme across an RFP response. So the RFP, what you receive from the buyer, often will have a bunch of context about their current situation That is gold to help you understand exactly what to incorporate. Once you have the win themes, then you go into applying win themes everywhere. Try to win th-thread these win themes consistently throughout every section. Flag answers that drift, especially on the answers where it matters. I'm using my project agent here so it's talking about differentiation, and then it has access to the web, it has access to my content library, it has access to my CRM, and it's gone

through and looked at all that in different information and found a bunch of different information relevant to this RFP that I'm currently working on and helped create some win themes. So win themes are, it's a scalable platform. Win theme number two, reduces security and compliance risk. It's easy to use and it's integration flexibility integrates with their entire tech stack. Maybe that is also a point of competitive differentiation. If I understand the market and the competitors really well, potentially my solution might be the only one that has a particular integration with a particular system in the buyer. So I wanna highlight that fact consistently that we have the experience of integrating their entire technology stack to our solution, and that is important because of X, Y, Z, because of what's stated in the RFP, because the buyer has told us or we've spoken to the buyer about it. Okay, so we've got all these different win themes that was created via AI, and now I'm gonna ask it, can you now incorporate these win themes across functions?

The AI is now going to start incorporating the win themes by editing these responses for me, by searching my past content, and effectively giving me a stronger narrative of why this buyer should choose our solution Now, it's generating those responses. I can go through, I can see the changes it made, and I can accept this or not. Okay, cool. That looks good. And then I can go through, and I can look at these responses and accept and change them as well, and make sure it incorporates what I want in the responses as well. Let's try and find one here You can see it keeps using the word configurable. So it's reasserting though that vocabulary that ties to integration strengths of our platform, But that's just an example of how we, how I used AI and the AutoRFP.ai project agent to generate win themes based off the RFP project, based off my knowledge of the buyer. And then from that we work to incorporate four win themes, and

then I've used AI to help apply that. I would then go through and edit and make changes here if necessary. And then we've got a strategic narrative throughout that section on the functional requirements. So what are customer insights? It's not just that we know who the buyer is and we've spoken to them a couple of times, but it's actually understanding their current state. It's about understanding their pain, their problems, why they're looking to go out to market, what has been their history of solutions, and everything we understand about that customer, about industry, about the geography and other relevant customers in the space to, understand their world and help pitch a solution that would generally provide value to them. So where can customer insights live? First of all, in your CRM. There's a goldmine of information in your CRM Then you've got your recorded discovery calls. This could be from systems like Gong or Clari and effectively any calls or demonstrations or workshops that you've had with the prospect before the RFP

strategy and workshop sessions. This is really important, in the world of capture, is helping shape that RFP in a subtle way. And a big way is strategy and workshop sessions or giving updates on the state of the market and other information that helps you position the buyer to understand the world and the category that they're looking to procure their products or services in. Team interviews. So again, you might have a sales team, pre-sales team or legal compliance, they all know incredibly well what the buyer is looking for, especially if they've spoken to buyer and, or they understand the industry well. And speak to them, talk to your team, bring out internal meetings that add value and help you understand the customer and provide their knowledge into things like the strategic narrative, like the win theme for that RFP. So content is what you say and your past content, but insight is why it matters.

You can say a bunch of stuff in an RFP, and it can come out looking like gobbledygook and be of no value to the buyer, and you can get a really low mark and not tick off any evaluation criteria or compliance matrices, and you're gonna lose. Anyone can generate an RFP with AI, but insight is why it matters. And why does that matter? Again, tying back to the proposal win rate report where we interviewed and asked winning bid teams what do they rate most highly as to why they win, customer insights was the number one reason, and 88% of high win teams were doing customer insights, whereas only 67% of low win teams had a defined customer insights process. But again, of all the reasons why they win, the number one reason for high win teams was customer insights. So all that information we just spoke about, they're leveraging

that to win competitive RFPs in my same response, we're gonna jump back into our section, and we're going to ask my agent, my project agent, to look at my CRM notes, I have a couple of call transcripts in there that have been made up, and help them edit these responses based off the knowledge of that CRM. So there's my prompt. It's gonna look inside HubSpot. This is the made-up company, and it's going to go through and look at these notes without me having to effectively point it to it. It's gonna hunt and peck. And it's all fake, and it's going to use that to help respond to my fake RFP. So here you can see it's used a bunch of tool calls via the MCP. So effectively, my AI in AutoRFP.ai is speaking to HubSpot's server and grabbing all that information and then parsing that and contextualizing that for the RFP because my AI understands the RFP because it's right there in front of it. And it's going through, and it can look at all the information, and then

it's pulled out a stakeholder map. All the stakeholders and role in the decision, what they care about, and where it sourced that information. So that's really important, especially with thinking about it's actually a person behind the marking criteria. Could be procurement, could be a decision-maker and then it's talking about the actual drivers and pain points. And you can see here it's actually pulled out a lot of different information from those calls about what's important for this RFP. And it's then going to effectively take all that information, take my library content, so it's still sourced in reality of what my product and company can actually achieve, and it's going to take those win themes and must-win sections, and it's going to effectively craft that into a response. And now I'll ask it, "Cool. Can you now update section B?" Based off the context information there Again, here's two responses where it's worked in that context to that response. And you can see that I can accept that, easily make those

changes, and pretty happy with it. Those responses. That's how it would incorporate the customer insights and so on into it. I And one big thing I wanna call out is SME-led drafting, so the subject matter expert writing the response from a blank page, is a low-win habit. Ninety-four percent of high-win teams from our survey and our interviews, the proposal team writes, the SMEs review. So SMEs write for precision, whereas proposal teams write for persuasion. And we're talking back through the entire thing about customer insights, about win themes, about what makes a great response. We're talking about persuasive narrative and writing, and SMEs will write for the technical correct answer, which can be a good answer, but proposal teams write for a great answer that will actually win you that RFP. So don't fall into that trap. Make sure that when you have SMEs, they're just reviewing and approving information.

Or even better, you're sourcing that from an approved library of content that SMEs already approved, so they don't even need to approve it, but they're just reading over and approving things. But someone else is actually incorporating everything we've spoken about today into that response, and they're just approving the technicalities. One model actually that can really help speed up SME time and reduce the time as well is that AI can draft the repeatable responses, again, sourcing from approved content and then sourcing from the context of your company and the SMEs just going in and validating low-confidence bespoke responses So that's how you can use AI to help raise the floor and incorporate insight, themes, and narrative into your RFP response and really use AI to automate the mundane. Now, if you wanna get a hold of the 2026 Proposal Win Rate report that I covered throughout some of the great stats throughout today's video you can see the link in the description below. All right, thanks. I'm Rob from AutoRFP.ai.ai. Cheers.

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