AI You Can Prove: What Lenders, Boards and Buyers Look For

Sooner or later, someone outside the business is going to look hard at your operations. A lender underwriting a new facility. A board asking where the margin went. A partner doing diligence. A successor deciding whether to take the keys. Sometimes a buyer opening a data room.

They all ask the same question: is the performance real, and will it hold up without heroics? They're looking for risk. They're looking for upside. And increasingly, they're looking at whether your operations are modern enough to keep performing without expensive fixes.

AI-enabled operations have become part of that conversation. Not because anyone cares about AI for its own sake — they don't. They care because a company that has automated its repetitive workflows, documented the efficiency gains, and built systems that don't depend on any single person's tribal knowledge is a fundamentally less risky business.

The Outside View

Investors are already measuring AI's effect on the companies they own. According to Pictet's 2025 survey, more than 60% of PE respondents attributed revenue improvements at portfolio companies directly to AI — primarily through headcount reduction and productivity gains.¹ BDO's 2025 PE survey found that 84% of fund managers report longer holding periods for portfolio companies, making operational efficiency during the hold period more critical than ever.²

What does that mean for you?

It means anyone evaluating your business is already thinking about AI. They're asking whether your operations could be improved — and if the answer is "yes, dramatically," that's not a compliment. A lender reads it as risk. A board reads it as missed margin. A buyer prices in the cost of doing the work themselves, and you absorb the discount.

The alternative: do the work now, and document it.

Same EBITDA, Different Story

Every serious review ends in a document that tells the story of how your operations work — a credit memo, a board deck, an investment memo. The narrative it tells matters as much as the numbers.

Two companies with identical EBITDA can tell very different stories.

Company A describes manual processes, key-person dependencies, and operational improvements that "could be achieved with the right investment." The reader hears: execution risk, hidden costs, and a plan someone else will have to carry out.

Company B describes three automated workflows, documented efficiency gains over 14 months, monthly EBITDA impact reporting, and a management team that can articulate exactly how the systems work. The reader hears: modern operations, lower risk, and proof that the gains are sustainable.

Same EBITDA. Different story. Different terms — on a credit line, in a board meeting or at the negotiating table.

The Bar Is Rising

Outsiders know where AI value sits. BCG's 2025 research found that 70% of the potential value from AI is concentrated in core business functions like sales, manufacturing, and supply chain — exactly the areas any diligence team looks at hardest.³ And adoption is now the norm: McKinsey's 2025 State of AI report showed 88% of organizations now use AI in at least one business function, up from 78% a year earlier.⁴

The bar is rising fast.

A reviewer who looks at your operations and finds obvious automation opportunities that you haven't pursued isn't going to be impressed by your growth story. They're going to wonder what else you've left on the table.

The 18-Month Playbook

Whatever the occasion — a refinancing, a recapitalization, a leadership transition or a sale — the timeline works the same way:

Why 18 Months, Not 6

Outside reviewers are skeptical of recent changes. They should be. A company that implemented AI three months before a review looks like it's dressing up for the dance. A company with 14 months of documented, consistent EBITDA improvement looks like it's genuinely well-run.

The documentation trail matters. Monthly reports showing a clear trajectory of gains — even modest ones — tell a much more compelling story than a single impressive quarter. Consistency signals sustainability. And sustainability is what lenders, boards and buyers all pay for.

There's also a practical reason: it takes time to get good at this. The first 90 days of any AI implementation involve iteration, adjustment, and learning. The real compounding starts after the system has been tuned to your specific business for 6–12 months. That's when the gains accelerate and the documentation becomes genuinely impressive.

The Earlier You Start, the Bigger the Payoff

The best outcomes come from companies that start 18–24 months before the moment that matters. That's enough time to deploy, refine, and build the documented track record that outsiders actually value.

But even a company with a closer deadline can benefit. The key is having someone focused on implementation and results while leadership focuses on the conversation itself.

Whatever stage you're at, the window for creating measurable value is shorter than people think. The right time to start is now.