Build versus buy AI proposal software ✹ What the data says revenue leaders get wrong

The honest answer to "should we build or buy AI for proposals" isn't as easy of a question as it used to be. Across enterprise AI, internally built systems succeed roughly a third as often as bought or partnered ones, and only 5% of GenAI pilots ever reach measurable financial impact.

Yet for a revenue-critical workflow like proposals, the decision that matters is governed versus ungoverned.

That reframing comes out of a guide QorusDocs published this year, Beyond Build vs. Buy: How Leaders Should Navigate AI for Proposals, RFPs, and Value Selling.

Of course we came in a bit sceptical, because of course a proposal automation software company like QorusDocs wants you to buy their software, not have you build their own. But we were really impressed with this article. We read it as an independent analyst publication, set its frameworks against the available enterprise data, and the two line up uncomfortably well. This post is our read on what the evidence says, where the QorusDocs frameworks hold, and what a revenue leader should do when they start to ask themselves this very question.

Why "build vs buy" is the wrong question

The build-versus-buy framing assumes you need to either own or rent the software you're using.

AI has destroyed that symmetry.

A prompt now drafts an RFP answer in seconds. A custom GPT summarizes a requirements document over a coffee break. A junior analyst with no engineering background can stand up a chatbot against a folder of past proposals before lunch. Building has never looked cheaper or faster, which is exactly why the framing has become a trap.

The trap is that a working prototype and a governed system are different species, and we can't pretend like a vibecoded proposal tool lives up to its real competitors.

QorusDocs calls this the prototype trap, and the data backs the warning. MIT's NANDA initiative, in its 2025 study The GenAI Divide, tracked enterprise AI systems from evaluation to production. It found that 60% of firms evaluated them, 20% reached a pilot, and only 5% made it to production. The prototype is not the hard part. Everything after the prototype is the hard part.

What the data shows about building

The strongest evidence against reflexive building is a success rate. MIT found that AI tools bought from specialised vendors or built through partnerships succeed about 67% of the time, while internal builds succeed roughly a third as often. The same research found that 95% of GenAI pilots produced no measurable profit-and-loss impact at all, with only 5% delivering significant value.

Read those two findings together!

Building is not just slower or more expensive on average - and it is, but it also fails more often, in a field where most attempts already fail. The lead author put said that almost everywhere the researchers went, enterprises were trying to build their own tools, and almost everywhere, purchased solutions delivered more reliable results.

This matters most in exactly the place proposal teams operate. MIT noted the finding is especially relevant in highly regulated sectors, where firms are most tempted to build proprietary systems to keep sensitive data in-house. Proposals, with their pricing, client data, and compliance commitments, sit squarely in that zone.

The one question worth asking instead

QorusDocs wants you to ask yourself, "Is this capability core to how our business competes and wins?"

If the answer is genuinely yes, building deserves serious consideration, because owning a proprietary capability can create durable advantage. If the answer is no, the long-term costs of ownership rarely justify the build.

For a law firm, core business is client counsel and matter outcomes, not building pitch-management software. For an architecture or engineering practice, it is design and delivery, not building RFP automation. For an IT services firm, it is transformation and customer outcomes, not maintaining ROI calculators. In each case, proposal software supports the way the business competes; it is not the thing the business competes on.

A capability can be revenue-critical and still not be core, but proposals clearly influence revenue. The software that produces them is, for almost every firm outside the proposal-software industry itself, plumbing rather than differentiation. And yet, plumbing is something you buy from people who build it for a living.

The five costs that show up after the prototype

When building looks cheap, it is usually because a prototype is, well, cheap. QorusDocs names five costs that show up later:

1. An internal tool pulls time from engineering, product, IT, and enablement, plus someone who actually understands proposal workflows.

2. Technology: model access, integrations, monitoring, content connections, and the unglamorous work of wiring AI into CRM and Microsoft 365 with the right permissions.

3. Governance, which is what separates approved content from generated content and tracks every change, without which teams ship outdated claims. T

4. Adoption, because a tool nobody is trained to use fails regardless of how good it is.

5. Opportunity cost: every quarter spent building proposal software is a quarter not spent on the work that is actually core.

Opportunity cost is the one CFOs feel last and hardest. The question is not, "Can we build this?" It is, "What are we choosing not to do because we built this?"

The real divide: governed versus ungoverned

Here is where the data and the frameworks converge on a better question. The MIT research found that what separates the successful 5% is whether the deployment integrates deeply, adapts over time, and connects to real workflows. The failures share things in common, like brittle, generic, disconnected from how the work actually happens.

For proposals specifically, that integration is governance. A general-purpose LLM is excellent at the things QorusDocs lists as genuine strengths, like summarising an RFP, drafting a first pass, rewriting for an audience, brainstorming win themes, finding gaps in a draft. Where it falls short is everything that makes a proposal trustworthy at the point a buyer takes it to their CFO, like approved content libraries, compliance tracking, SME review routing, version control, auditability, and validated assumptions.

So the fault line is not build versus buy, like we might originally think it is. It is governed versus ungoverned AI.

An ungoverned build and an ungoverned subscription fail for the same reason. A governed system, whether bought or blended, is what reaches production. This is the case for purpose-built platforms, and it is why a vendor like QorusDocs frames its own category around uniting value management with proposal automation under governance rather than around raw generation speed.

What revenue leaders should actually do

The strongest AI strategy is the most focused one, and focus means matching the approach to the work rather than picking one answer for everything. The practical version looks like this:

Use general-purpose AI where speed and individual productivity matter and the stakes are low. Lean on enterprise AI, such as Copilot, where the work already lives inside your productivity environment. Build only where the capability is genuinely proprietary and central to how you compete, which for proposal software is rare. Buy or blend where you need governed, repeatable, revenue-critical workflows at scale, which describes most proposal operations.

Before turning any promising prototype into an owned internal tool, run the ownership test honestly. Can you govern it, support it, secure it, and improve it across its full lifecycle, including the day the person who built it leaves? If not, you do not have a tool. You have a liability that currently looks like a tool.

The goal was never to build more. It is to own the few things that make you win, and to buy the rest from people who own them for a living.

Independent analysis

This analysis was written by Christina Carter, founder of stargazy, an independent analyst publication covering the proposal and bid software market.

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Frequently asked questions

Should we build or buy AI proposal software?

For most organisations, buy or blend. MIT's 2025 research found vendor-purchased and partnership AI succeeds about 67% of the time versus roughly a third as often for internal builds. Building makes sense only when proposal capability is genuinely proprietary and core to how you compete, which is rare outside the proposal-software industry itself.

When does building your own RFP tool actually make sense?

When the capability is proprietary, strategically central, and creates competitive advantage you cannot buy. The QorusDocs litmus test is the question to ask: is this capability core to how our business competes and wins? If yes, and you can govern its full lifecycle, building is worth serious consideration.

Why do so many internal AI proposal tools fail?

Because a prototype is not a governed system. MIT found only 5% of enterprise AI systems reach production, with failures sharing brittle workflows and poor integration. Proposal tools also carry hidden costs in people, technology, governance, adoption, and opportunity that rarely appear in the initial business case.

What is the difference between using an LLM and a purpose-built proposal platform?

An LLM is excellent for individual productivity: summarising RFPs, drafting, brainstorming win themes. A purpose-built platform adds governance: approved content, compliance tracking, SME review, version control, auditability, and CRM integration. The risk is treating LLM productivity gains as if they were a governed proposal workflow.

Is build versus buy even the right question?

Often not. The more useful divide is governed versus ungoverned AI. An ungoverned build and an ungoverned subscription fail for the same reasons. What separates the successful 5% in MIT's data is deep integration and adaptation to real workflows, which for proposals means governance.

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Sources

QorusDocs, Beyond Build vs. Buy: How Leaders Should Navigate AI for Proposals, RFPs, and Value Selling, 2025. https://www.qorusdocs.com/resources/ebooks/beyond-build-vs-buy-make-smarter-ai-decisions-for-proposals-and-rfps

MIT NANDA initiative, The GenAI Divide: State of AI in Business 2025 (reported via Fortune, August 2025). https://fortune.com/2025/08/18/mit-report-95-percent-generative-ai-pilots-at-companies-failing-cfo/

MIT NANDA initiative, The GenAI Divide (production and adoption figures, via Virtualization Review, August 2025). https://virtualizationreview.com/articles/2025/08/19/mit-report-finds-most-ai-business-investments-fail-reveals-genai-divide.aspx