Access Holdings didn't wait for their portfolio companies to work it out. Other firms should take note.
We recently hosted Nik Kapauan and Matt Treuth from Access Holdings for a Mastering AI webinar. Access are a lower middle market buy-and-build investor based in Baltimore, and they're doing something most PE firms only talk about: actively driving AI and data adoption across their entire portfolio.
They liked the idea enough to fly us over from London to train their whole team. Which tells you something about how seriously they take this.
At a recent industry event, only 27% of PE professionals said their firm's data foundations were strong enough to support AI at scale. That's bad. But the number isn't really the problem. The problem is that too many firms are waiting for their portfolio companies to sort this out on their own. Access aren't waiting. And the results speak for themselves.
Access invest in founder-run businesses in the lower middle market. A lot of these companies are running on QuickBooks and not much else when Access arrive. Nobody at those businesses is going to spontaneously build a data warehouse or figure out an AI strategy. So Access do it for them.
From the moment they close on a new platform, there's a playbook. A standard tech stack goes in - NetSuite for finance, Salesforce or HubSpot for CRM, UKG for HR. Portfolio company data gets pulled into a centralised Snowflake data warehouse through Fivetran. The whole thing can be up and running in days, not months, because the model is pre-built and repeatable.
This isn't about being controlling. As Nik put it, these operators are actively looking for guidance. They want to know what good looks like. And by being opinionated early, Access create the conditions for everything that comes after.
This is something we'd love to see more PE firms do - not just for data, but for AI too. The firms we work with that get the most out of AI training are the ones that actively push it into their portfolio companies, rather than leaving each portco to work it out alone.
The tech is important. But what really impressed us about Access was how much of their approach comes down to people.
They run something called the Access Creating Executives (ACE) programme. These are sharp, early-to-mid career hires - typically two to five years out of business school, often from firms like McKinsey or BCG - who learn Access's playbook and then embed in portfolio companies. There are ACEs for strategy, data, M&A, and technology.
They sit with the central team for three to six months, learn the systems and the thinking, and then transfer into the business. It gives portfolio companies immediate, skilled people to drive change. And it builds capability that stays in the business long after Access exits.
Nik said it's by far their most popular programme with portfolio company executives. Once they see what an ACE can do, they keep asking for more.
We see the same thing in our AI training work. You can give people the best tools in the world, but without the right people to champion them and bring others along, nothing changes. Access have understood that from the start.
Access have built something impressive on the data side. They pull in transaction-level data from across the portfolio - not monthly summaries, individual transactions - and layer on external data too. For their car wash platform (250 locations), they overlay weather data so they can isolate variables that management can actually control. Performance conversations end up being about decisions, not excuses.
They've also built competitive pricing tools, overtime monitoring agents, and predictive models for churn and demand forecasting. Proper stuff.
But none of it needed perfect data on day one. Access started with a clear architecture and a repeatable process, and built on it over about six months. Not a three-year project.
And while all of that was happening, they were also investing in AI skills for their people. They didn't treat "get the data right" and "start using AI" as one-thing-then-the-other. They ran them side by side. Because AI is useful right now, even before your data is perfectly organised. And once the data does come together, having a team that already knows how to work with AI means you move much, much faster.
Access call their model "build, operate, transfer." The central team builds a capability, runs it to create value straight away, and then transfers ownership into the portfolio company over 18-24 months.
The whole architecture is designed so any portfolio company can be cleanly separated with its own standalone setup at exit. It's not about creating dependency on Access. It's about giving businesses a head start they'd never get on their own, and then making sure they can stand on their own feet.
With data and skills foundations in place, Access are pushing into more ambitious territory. Nik talked about building AI agents for investment committee support - tools trained on historical LOIs, term sheets, and IC decks that can help inform decisions on whether to advance on a deal and what terms to offer.
Matt's focused on using AI to find the signal in the noise. When you're running 250 locations and tracking 20 variables per store, you need something that can say "these five things need your attention today." That's where AI stops being a nice-to-have and starts being essential.
We asked each of them for one thing they'd tell a PE firm earlier in this journey.
Matt's was practical: get your data into one central place. The tools are cheaper and faster than they were three years ago. Even without a full AI roadmap, centralised data is the foundation everything else gets built on.
Nik's was broader: be curious. The landscape shifts constantly. Read, listen, experiment. You don't need a perfect strategy. You just need to be the one driving things forward, not waiting for your portfolio companies to do it for you.
That last point matters. The PE firms getting the most out of AI aren't the ones with the fanciest technology. They're the proactive ones. The ones pushing capabilities into their portfolio and investing in the people and skills to make it all work.
Access are a good example of what that looks like in practice. We were glad to be a small part of it.
We work with PE firms to train investment teams, portfolio ops, IR, legal, compliance, and finance on real workflows. If you want to see how that looks in practice, browse our private equity training page or go straight to our private equity results.
Where to next
If this topic is relevant to your team, these pages are the best next stop.
See how we train deal teams, portfolio ops, IR, legal, and finance on live investment workflows.
See the case study with measured ROI, time savings, and adoption outcomes.
See how bespoke sessions are designed, delivered, and measured.
Most organisations have no AI governance programme. EU law already requires AI literacy. Here's why HR leaders must treat this as a people challenge, not a tech one.
Shadow AI is everywhere. The EU AI Act forces it into the light. Here's why businesses that lead on AI transparency will win customer trust – and market share.
AI isn’t coming – it’s here. The real question is how we use it responsibly. From bias to transparency, we explore what ethical adoption actually looks like in practice – and how teams can embrace AI’s potential without compromising on their values.