Why fast-growing startups are losing enterprise deals at the last hurdle
The went well; the pricing conversation was constructive; the decision-maker was enthusiastic; then procurement got involved, and everything went quiet.
I have seen this pattern more times than I can count, and the frustrating thing is that it is almost always avoidable. Startups winning on product, losing on process. The specific process that is catching them out is vendor risk assessment (which is a series of articles on their own); and, increasingly, the questions enterprise procurement teams are now asking about AI.
This is no longer a niche concern. IBM’s 2026 Cost of a Data Breach Report found that shadow AI incidents doubled year-on-year and featured in 43% of all security incidents. Enterprise security and procurement teams have read that data. They are asking harder questions about the AI tools in their suppliers’ stacks, and most startups are not prepared to answer them.
Which raises the question: why not?
What enterprise procurement teams are actually asking
The questions vary by sector, but the core set is consistently focused on AI governance and security, and most founders I speak to have not sat down and worked through their answers.
What AI tools does your team use, and who approved them?
Most startups cannot answer this cleanly. Tools get adopted informally, often without IT sign-off or documentation. When procurement asks for a list of AI systems in use, the honest answer is usually “we’re not entirely sure.” That is not the answer enterprise clients want.
What data does your AI process, and where does it go?
If your tools are cloud-hosted, if prompts are being sent to third-party models, if outputs are stored externally, you have a data processing question you need to be able to answer. Enterprise clients in regulated industries will want to know whether their data, even indirectly, comes into contact with your AI systems. If you cannot give a clean answer, the risk assessment fails.
What happens when your AI gets it wrong?
This is the accountability question, and it is the one I find founders most often have not thought through. Who is responsible when an AI output causes a problem? What is the remediation process? Who gets notified? For enterprise clients in financial services, healthcare, or legal, these are not theoretical questions. They expect a considered answer.
How do you comply with the EU AI Act?
As of 2 August 2026, the EU AI Act’s binding obligations are live. That includes mandatory AI literacy requirements, audit logging for AI systems used in certain contexts, and documented accountability structures. Enterprise procurement teams in regulated industries are beginning to add AI Act compliance to their vendor questionnaires. If you are selling into Europe, or into European subsidiaries of global businesses, this is not a future concern. It is a current one.
The governance question is really a leadership question
AI governance feels like a compliance problem; something you hand to a technical team or deal with once you are big enough. But the questions above are not technical questions. They are leadership questions. Who decided what tools your team can use? Who owns the risk if something goes wrong? What is your organisation’s position on AI accountability?
If you do not have clear answers, the issue is not your security posture. It is that nobody at the top of your organisation has sat down and worked through the questions.
The good news is that getting ready does not require a complete overhaul of your tech stack. But it does require a few things to happen at the leadership level, not the IT level.
Know what you have. Start with an AI inventory: every tool your team uses, including the non-approved ones, who uses it, what data it touches, and where outputs go. Founders are often surprised by what they find.
Own the governance question and actually write a short AI policy. Not for compliance theatre, but so that you can answer, clearly and without hesitation, who is accountable for AI in your organisation and what happens if something goes wrong.
Know your data story: Be clear on whether any customer or client data touches your AI systems, directly or indirectly. If it does, know which systems, under what terms, and what your data processing agreements cover.
Get ahead of the EU AI Act: Even if you are not yet subject to full requirements, being able to demonstrate awareness and a compliance roadmap is a genuine differentiator in a procurement process. It signals maturity. Enterprise buyers notice.
The startups that win enterprise deals
Enterprise procurement is not going to get easier on this. The IBM data shows that the problem is growing. The regulatory environment is tightening. And enterprise security teams are becoming more sophisticated in their requests.
The startups that will win enterprise deals are not necessarily the ones with the most advanced AI. They are the ones whose leadership teams can clearly and confidently answer what their AI does, who is responsible for it, and what they have done to manage the risk.
That conversation starts at the top, before procurement gets involved.
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