The State of RFP Software Today: Where AI for RFPs Excels and Where It Falls Short

If you're reading this, you probably know the pain: another RFP just landed in your inbox, 200+ questions, due in two weeks, and your team is already underwater with three other proposals. You're not alone—and the tools meant to help haven't always made things easier.

Here's the reality of where RFP software stands in 2025, what actually works, and what to watch out for when vendors promise the world.

The Manual RFP Era (And Why 35% of Teams Are Still There)

Let's start with where most teams began: fully manual RFPs. The process looked something like this:

You'd get an RFP. Someone (usually a sales engineer or pre-sales specialist) would open it in Word or Excel, then start the hunt. They'd dig through Sharepoint folders, Slack threads, old proposals, that one Google Doc someone made 18 months ago, and their own memory of "didn't we answer this before?"

Each question took 15-30 minutes to research and answer. A 100-question RFP meant 25-50 hours of work minimum when you factor in the reviews, edits, and the inevitable "actually, our product changed last quarter" moments.

The real pain wasn't just the time—it was the context switching. You'd be deep in writing about security protocols, then get pulled into a customer call, then come back and spend 20 minutes just figuring out where you were. Multiply that by every person on the team, and you see why burnout was rampant.

Today, 35% of companies still operate this way. Not because they want to, but because they haven't found a solution that actually works better than their current chaos. And honestly? Some legacy "solutions" aren't much better.

The Content Library Era: Better, But Still Broken

Around 2015-2018, RFP content libraries became the standard answer. Tools like Loopio, RFPIO, and others promised a simple solution: build a repository of pre-approved answers, then search and reuse them.

24% of companies today rely on content libraries as their primary RFP solution. And yes, it's better than fully manual. Instead of 30 minutes per question, you might spend 10-15 minutes searching your library, finding a close answer, and editing it to fit.

But here's what actually happened on most teams:

The content treadmill never stops. Your product launches a new feature. Your pricing changes. You acquire another company. A competitor makes a move that changes your positioning. That's 50+ answers in your library that need updating, and nobody has time to systematically review and update them. Within 6 months, 30% of your library is quietly outdated.

Then the real problems start. You pull an answer from the library for an RFP due in 24 hours. You paste it in. You submit. Three days later, you find out the pricing you quoted was from last year's model—completely wrong. Or worse, you described a feature that was deprecated six months ago, and now your prospect is asking detailed questions about something that doesn't exist anymore.

This is the moment of broken trust. You're an SE with an RFP due tomorrow, and you can't risk pulling outdated answers that make you look incompetent. So you bypass the content library entirely. You go find that Google Doc that Jenny from Product keeps updated. You Slack the engineering team directly: "Hey, I know this is last minute, but can you just write me a quick answer on our API rate limits?" You pull in your SME friends—the people who actually know what's current—and ask them to write the answer from scratch so you know it's right.

Because here's what nobody talks about: an outdated answer doesn't just waste time. It actively hurts your chances of winning. A prospect reads your answer about a feature that doesn't exist, or pricing that's wrong, and their confidence in your company drops a few percentage points. Multiply that across 20 outdated answers in a 200-question RFP, and you've materially damaged your win rate.

So you never update the content library (you don't have time), and within a year, your entire team has collectively decided to just write from scratch. The content library is still there, costing thousands per year, but nobody trusts it anymore.

The search problem: when you can't find what you know exists. These legacy tools are clunky in ways that compound every other problem. You need to find an answer about your GDPR compliance for a European prospect. You open the content library. There are 50 different tags: "Security," "Compliance," "Legal," "Privacy," "EU," "Data Protection," "GDPR," "Regulatory," and 42 others that someone created at some point for reasons you'll never understand.

You try three different tag combinations. Nothing. You try keyword search. Still nothing useful. You know someone answered this before—you saw it in last quarter's RFP—but the tagging system is such a mess that you can't find it. So you write it from scratch, again, because that's faster than fighting the tool.

The interface was designed by engineers who've never actually responded to an RFP under deadline pressure. Simple things—like searching for content the way you actually remember it—are inexplicably difficult. The tool becomes something you fight against rather than work with.

The collaboration blocker: when getting help means stopping work. Now let's say you finally draft an answer and you need Miguel from Engineering to review it because it's technical and you want to make sure you got it right. But Miguel doesn't have a license—the company only pays for 10 seats and they're all taken.

So you do what everyone does: you give Miguel your login credentials. Which immediately logs you out of the platform.

Now you can't work on the other 50 questions in your RFP. You can't access anything. You just sit there, blocked, waiting for Miguel to finish reviewing, log out, and hand your account back to you. Or maybe you Slack someone else on the team: "Hey Sarah, can you log out for 20 minutes so I can get back in?"

This is the license musical chairs routine that happens dozens of times per RFP. And you wonder why you're paying thousands of dollars for software that makes collaboration harder.

The content library becomes a bottleneck instead of a solution. It's not just that it doesn't help—it actively gets in the way.

The core issue: content libraries are fundamentally reactive. They store what you've already written, but they don't generate new answers. They don't adapt to the specific context of each RFP. They're a database, not a thinking tool.

AI for RFPs: Where We Are in 2025 (And the Vaporware Problem)

49% of teams now use AI in some capacity for RFPs. This is where things get interesting—and messy.

Here's the uncomfortable truth: there's more vaporware in the RFP software space today than ever before. Legacy companies that built their tools 8 years ago are slapping "AI-powered" onto their marketing pages and hoping you don't dig deeper.

We've seen teams buy "AI-powered" RFP tools, roll them out with excitement, and then... the AI generates answers that are clearly wrong, or vague, or require so much editing that it would've been faster to write from scratch. Six months later, nobody uses the AI feature. They're back to the content library, except now they're paying 2x the price for AI they don't use.

The only metric that actually matters: acceptance rate. What percentage of AI-generated answers does your team accept as-is, without editing or rewriting? Because if the answer is low, the AI isn't saving you time—it's creating more work.

At Arphie, our average acceptance rate across our customer base is 84%. That means 84% of the time, the AI generates an answer that the SE or pre-sales engineer looks at and says "yes, this is correct and complete, shipping it." The other 16%? Those get edited or rewritten, which is fine—no AI is perfect, and we'll talk about when AI should and shouldn't be used.

But 84% is the benchmark. If a vendor can't tell you their acceptance rate, or if it's below 50%, the AI probably isn't ready for production use.

What Makes AI Actually Useful (And What to Look For)

If you're evaluating RFP AI tools, here's what separates real solutions from vaporware:

1. Confidence Levels: The AI Needs to Know What It Doesn't Know

Not all AI answers are created equal. Sometimes your knowledge base has a crystal-clear, recently-updated answer to a question. Other times, the AI has to infer based on related information, or there's no information at all.

The best AI systems tell you the difference. For example, for one of our leading B2B SaaS customer, they see average confidence levels as follows: 

• High confidence: The AI found a direct match in your knowledge base. You spend 10-15 seconds scanning to verify it's correct, then move on. This is where you save massive time.
• Medium confidence: The AI pieced together an answer from multiple sources or had to make reasonable inferences. You spend 2-3 minutes reviewing more carefully, maybe tweaking language. It is common to see medium confidence answers require minor edits, which happens for 7% of answers on Arphie’s platform.
• Low confidence: The AI is inferring based on the information in your knowledge base to make its “best guess”. It is common to see low confidence answers require major edits, which happens for 4% of answers on Arphie’s platform.
  Unanswered: 2% of questions are unanswered on Arphie’s platform, which requires manually written answers. However, we still speed up the process by allowing your team to search across the knowledge base through our search functionality, and we surface all sources that might help you answer the question. More often than not, this is your signal to pull in a subject matter expert.

Without confidence levels, you waste time meticulously reviewing every single answer, even the slam dunks. With confidence levels, you can triage effectively. And the reality is sometimes you need to pull in 10+ different SMEs to properly answer an RFP. The VP of Engineering for the security architecture question. The Head of Customer Success for the implementation timeline question. The Compliance lead for the SOC 2 question. 

At Arphie, we deeply understand SE workflows. We know that SMEs are pulled in once a month to answer questions not yet stored in the knowledge base. That’s why we price per project instead of per user. There’s no need for licensed musical chairs. If you need to bring in 10 SMEs for one project, you just… do it. 95% of our customers use auto provisioning to help SMEs auto create an account via SSO. Everyone gets access, contributes their expertise, and moves on.

2. Source Citations: Trust, But Verify

When the AI generates an answer, where did it come from? Which document? Which section?

Without citations, you're flying blind. You don't know if the answer came from your current security whitepaper or the one from 2022 that's now outdated. You don't know if it's piecing together information from three different docs that might contradict each other.

With citations, you can:

• Quickly verify the information is current
• Jump to the source document to see additional context
• Identify when the AI is pulling from the wrong place (and fix it)

At Arphie, every AI-generated answer includes the specific sources used. You can review them in-platform, or click through to the original document if you need more context. And if you remember an answer differently, "wait, I think we covered this in that customer case study", you can search for it using keywords, phrases, or even just the document title as you remember it.

This isn't just about trust. It's about enabling your team to work faster because they can quickly verify instead of slowly re-researching.

3. Understanding When AI Works and When It Doesn't

Here's where a lot of vendors overpromise: they imply AI can handle everything. It can't.

AI is terrible at company tribal knowledge. Questions like "who will be responsible for this customer's success, and what does that org structure look like?" or "what's our internal escalation process if something goes wrong?" These answers aren't written down in polished documents. They live in people's heads, in Slack conversations, in how your team actually operates day-to-day. AI can't generate this—you need a human who knows.

AI struggles with ambiguous context. Let's say you have two products: an expense management tool and a corporate spending platform. Both have onboarding processes, but they're different. Someone asks "what does onboarding look like?" Without additional context about which product, the AI might pull from both, or guess wrong, or generate a franken-answer that's confusing.

The solution? Tagging and context management. At Arphie, you keep tagging simple—usually no more than 5 tags that actually matter. Tag certain documents as "expense" and others as "reporting." That's it. When someone asks "what does onboarding look like?" and you've tagged the RFP as "expense," the AI only looks through expense documents. It's giving the AI context the same way you'd give context to a junior analyst on your team.

Think of AI as a brilliant junior analyst who just joined your team. Give it the proper context, and watch as it gives you leverage to spend your time on more important matters. The best AI tools make it easy to provide that context.

Comparison Table: RFP Response Approaches

How to Actually Evaluate RFP Software (Without Getting Burned)

You're going to see a lot of demos. A lot of promises. A lot of "powered by AI" claims that may or may not mean anything.

Here's how to cut through the noise:

Ask for the acceptance rate. What percentage of AI answers are used as-is by customers? If they can't or won't tell you, that's a red flag. If it's below 60%, the AI probably isn't saving time yet.

Request a real trial with your actual data. Not a demo with the vendor's pristine, perfectly-organized demo knowledge base. A real trial where you upload your messy Sharepoint folders, your outdated Google Docs, your random Confluence pages, and see what happens. How long does setup take? How good are the answers?

Talk to multiple customers at reference calls. Ask them specifically: "How often do you actually use the AI versus falling back to manual?" and "What percentage of your team actively uses the platform?" This tells you if the tool actually stuck or if it's shelfware.

Compare total cost of ownership, not just license fees. If a tool charges per user, calculate what you'll pay when you inevitably need to add 10 more SMEs. If it requires 200 hours of setup and training, factor that in. If the AI acceptance rate is low and people spend twice as long editing bad answers, that's a cost.

Look for proof, not promises. "We help companies respond 42% faster" means nothing without context. "Our customers accept 84% of AI answers as-is" is measurable and verifiable. Choose vendors who talk in specifics.

The RFP software market is full of vaporware right now because AI is new and everyone wants to claim they have it. But actual, production-ready AI that saves your team time? That's rare. It requires deep expertise in both AI/ML and the specific problem domain of RFPs and knowledge management.

Take the time to properly evaluate. Your team's sanity, and your company's win rate, depends on it.

The Bottom Line: Where RFP Software Should Be in 2025

After a decade of content libraries and two years of AI experimentation, here's where RFP software should actually be:

AI that generates genuinely good answers most of the time. Not 40% of the time. Not "sometimes." Most of the time—north of 80%—the AI should produce answers your team can accept with minimal editing.

Confidence levels that help your team triage. Spend seconds on high-confidence answers, minutes on medium-confidence ones, and pull in experts for low-confidence questions. This is how you actually save time.

Source citations for trust and verification. Your team shouldn't have to wonder where an answer came from or whether it's current.

Context awareness through tagging and filtering. The AI should understand which product, which customer segment, which use case you're dealing with, and only surface relevant information.

Honest limitations. Good vendors tell you what their AI can't do. It can't handle tribal knowledge. It needs help with ambiguous context. It works best when your knowledge base is reasonably organized. That honesty is a sign they know their product and respect your time.

If a vendor can't speak clearly about these dimensions—or worse, makes vague "AI-powered" claims without specifics—keep looking. The technology exists today to genuinely transform how your team handles RFPs. But only if you choose a tool that's actually built for 2025, not 2018 with a new label.

Your team deserves better than vaporware. They deserve tools that actually work.