24 June 2026
AI Regulation Will Hit Mid-Market SaaS First
AI regulation is not going to land on the frontier labs first and work its way down. It’s going to land on mid-market SaaS companies faster, because they’re easier targets and harder to defend.
Key takeaways
- Regulatory attention on frontier labs distracts from where enforcement actually starts
- Mid-market SaaS companies have less legal capacity to absorb sudden compliance demands
- Clients and investors will start asking for AI governance documentation before regulators do
- Building compliance documentation now is far cheaper than building it under pressure
- This is a finance and operations issue as much as a legal one
I’ve built and exited companies through Obby and Baluu, and I now run FirstMotion, an AI Search and GEO agency working inside B2B SaaS every day. Through VAQA’s advisory work on AI Implementation and board level governance, I’ve watched the gap between well prepared companies and exposed ones widen fast over the last year.
This piece explains why I think mid-market SaaS is more exposed than the headlines suggest, and what founders should do about it before anyone forces the issue.
Why the headlines are pointing at the wrong companies
Most of the AI regulation coverage in the press focuses on frontier labs: model safety commitments, disclosure requirements, and high profile hearings involving companies like OpenAI and Anthropic. That coverage is legitimate, those companies genuinely sit at the centre of the biggest policy questions.
But it creates a false sense of security for everyone else. Founders read those stories and reasonably conclude that regulation is a frontier lab problem, something happening to companies with billions in funding and dedicated policy teams. That conclusion is wrong, and it’s wrong in a specific way worth understanding.
Why mid-market SaaS is actually more exposed
Regulators, and just as importantly, enterprise procurement teams and investors, tend to focus scrutiny where enforcement is easiest and most visible. Frontier labs have enormous legal teams, existing compliance infrastructure and the resources to negotiate directly with regulators. Mid-market SaaS companies generally have none of that.
Consider what actually happens in practice. A large enterprise customer’s procurement team adopts a new AI vendor questionnaire, because their own legal department has started demanding it. That questionnaire gets sent to every AI enabled vendor they work with, regardless of size.
A mid-market SaaS company with an AI feature and no governance documentation suddenly has a matter of days to answer questions it’s never had to think through before.
That pressure arrives from clients and investors well before it arrives from a regulator, and it arrives with a deal or a funding round attached, which makes it far more urgent in practice than a distant policy timeline.
What “governance documentation” actually means
This phrase sounds abstract, so let me make it concrete. In my experience, the baseline package a mid-market SaaS company should have ready looks roughly like this.
| Document | What it covers | Who asks for it |
|---|---|---|
| AI usage policy | What models are used, for what, and where data flows | Enterprise procurement, investors |
| Data handling statement | How customer data is used in training or inference | Enterprise legal, regulators |
| Model risk register | Known limitations, failure modes and mitigations | Board, enterprise security teams |
| Human oversight record | Where humans review AI output before it reaches customers | Auditors, enterprise clients |
None of this requires a legal team the size of a frontier lab’s. It requires someone in the business sitting down and writing it honestly, then keeping it updated as the product changes.
Why waiting is the expensive option
I understand the instinct to wait. Building documentation for a regulatory framework that hasn’t fully solidified feels like effort spent on a moving target, and founders are busy building product, not writing policy.
But the cost asymmetry here is stark. Building this documentation proactively, on your own timeline, with your own team, costs a few weeks of focused work. Building it reactively, because an enterprise deal is stuck in procurement or an investor’s diligence checklist has flagged a gap, costs weeks you don’t have and often costs the deal itself while you scramble.
I’ve seen this play out directly with clients: a promising enterprise contract stalled for over a month because the vendor questionnaire asked questions nobody at the company had ever written down an answer to. The work still had to happen. It just happened under far worse conditions.
Where this intersects with fundraising
This isn’t purely an operational concern. Investors doing diligence on SaaS companies with AI features are increasingly asking governance questions as part of standard due diligence, not as an exotic add on.
A founder who can hand over a clear governance package signals operational maturity well beyond what the document itself covers. A founder who can’t answer basic questions about how their AI features handle customer data raises a flag that slows the round down, even if the underlying product is strong.
Getting ahead of a problem that hasn’t fully arrived yet
The pattern with every regulatory shift I’ve watched play out is the same: the companies that treated early preparation as a formality did far better than the ones that waited for certainty. Certainty never really arrives before enforcement does.
Mid-market SaaS companies don’t need to solve this at frontier lab scale. They need a clear, honest, reasonably current governance package that answers the questions procurement teams, investors and eventually regulators are already starting to ask.
This is core to the AI Implementation advisory work I do at VAQA, alongside the finance and fundraising implications it carries. If your AI features don’t have governance documentation behind them yet, get in touch and I’ll help you build a package that actually holds up under scrutiny.
Frequently Asked Questions
What size of company should start on AI governance documentation?
Any SaaS company with an AI feature that touches customer data should start now, regardless of headcount. I’ve seen companies well under 50 employees get asked for this by a single large enterprise client, so size doesn’t buy you time here.
Isn’t this a legal team’s job, not a founder’s?
It starts with the founder or a senior operator because they understand the product and data flows best. A lawyer can help formalise the language, but someone inside the business needs to own the underlying content, and in a mid-market company that’s often the founder.
How is this different from a standard data privacy policy?
A data privacy policy usually covers general data handling under frameworks like GDPR. AI governance documentation is more specific: it covers how models are used, what decisions they influence, and what human oversight exists, which most existing privacy policies don’t touch at all.
Does FirstMotion help with AI governance documentation?
No, FirstMotion’s focus is AI Search and GEO strategy, helping B2B SaaS companies get visibility and citations in AI systems. Governance and compliance documentation sits under VAQA’s AI Implementation advisory work instead.
How long does it take to build a basic governance package?
For most mid-market SaaS companies, a focused two to three week effort is enough to produce a defensible baseline package, assuming someone senior can dedicate real time to it. That’s a fraction of the time it takes when you’re building it under pressure from a stalled deal.
Tom Batting is a Forbes 30 Under 30 entrepreneur, founder of Obby and Baluu, and founder of FirstMotion. He advises founders and leadership teams through VAQA on board advisory, growth, go-to-market, AI implementation, operational efficiency, and finance and fundraising.