[ Blog ]
Paolo Timoni • February 9, 2026 • 8 min read
Every few decades, a technology comes along that changes how businesses operate. The personal computer did it. The internet did it. Cloud computing did it. Artificial intelligence is doing it now — and the pace is faster than anything we have seen before.
PE investors have noticed. According to McKinsey, 82% now view AI as a high priority, and 84% expect it to have a material impact on their business within five years. Global PE deal value in AI and machine learning companies jumped from $41.7 billion in 2023 to $140.5 billion in 2024 — a 3.4x increase in a single year.
But this article is not about investing in AI companies. It is about something closer to home: how we actually use AI inside the companies we own and run.
We are a small team — by design. We do not have floors of analysts or armies of associates. What we have is a way of using AI that lets three people do what used to take thirty. It shows up in every phase of our work.
Deal sourcing and screening. We use AI to identify and evaluate intermediaries, scan markets for acquisition targets that match our criteria, and monitor signals that indicate a company may be approaching a transition — whether through ownership succession, growth inflection, or operational strain. What used to take a team of analysts making hundreds of calls now runs on its own.
Due diligence and valuation. This is where the time savings are hardest to believe. AI-assisted analysis lets us process financial documents, legal agreements, customer contracts, and market data faster and more thoroughly than anyone could have two years ago. Legal diligence that once required 300 attorney hours can now be completed in 60. Industry and competitive analyses that took weeks can be drafted in days. We still make the calls. AI just compresses the time it takes to get smart enough to make them.
Portfolio monitoring. Dashboards pull financial and operational data from each portfolio company in real time. Anomaly detection catches things before they become problems. We spot trends early instead of reacting late.
The result is that we operate with the analytical depth of a much larger firm without losing the speed and personal attention of a small one. AI is what closes that gap.
Big companies have been spending on AI for years. They have data science teams, enterprise budgets, and existing infrastructure. For them, AI is an upgrade.
Lower middle market companies are in a different position entirely. A $30 million business typically runs on spreadsheets, manual processes, and institutional knowledge that lives in a few people’s heads. There is no data science team. There is often no CRM. Financial reporting is a monthly exercise, not a real-time capability.
This is exactly where AI hits hardest. They are not layering AI onto existing tech. They are jumping straight to capabilities that bigger competitors spent years and millions assembling piece by piece.
The data supports this. According to Everest Group, over 40% of mid-market enterprises are now bypassing traditional AI adoption stages entirely to close the gap faster. They move faster, carry less organizational baggage, and have fewer legacy systems in the way.
A mid-market company can go from no CRM to an AI-powered sales platform in months. A large enterprise trying to do the same thing fights through 18 months of procurement, integration, and change management. Right now, being smaller is actually an advantage — if you have a partner who knows how to put AI to work.
The AI projects that move the needle in portfolio companies are rarely glamorous. They are not about building proprietary large language models or launching AI-powered products. They automate the boring stuff, sharpen the analysis, and turn what lived in one person’s head into a system.
Here is what is actually working:
Sales and revenue growth. A B2B distribution company implemented AI-enabled lead scoring inside its CRM. The system ranks prospects by conversion likelihood using hundreds of behavioral and firmographic signals — more than any rep could track by hand. Result: 15% higher sales productivity and a noticeably shorter sales cycle. Across industries, 69% of sellers using AI say their cycles got shorter — by about a week per deal on average.
It changes outreach too. Generative AI drafts personalized emails using CRM data, improving response rates by an average of 28%. For a company where the founder was the sales team, that changes everything.
Operations and supply chain. Pattern recognition across big datasets is exactly the kind of analysis ops teams need but never have time for. One PE-backed food services company used AI-connected sensors to adjust product mix and demand forecasts — and cut costs 25%. Predictive maintenance catches equipment problems before they cause downtime. Route optimization cuts logistics costs.
For a manufacturing or distribution business doing $50 million in revenue, even a 3-5% efficiency gain drops straight to the bottom line. At a 6x EBITDA multiple, that margin improvement is worth real money.
Finance and reporting. Most lower middle market companies have a bookkeeper or controller managing the finances — someone skilled at keeping the books but not equipped to deliver real-time financial intelligence. AI fills that gap. Automated variance analysis, invoice matching, and compliance monitoring turn a reactive finance function into a forward-looking one.
None of this replaces a good CFO. It makes the CFO effective faster — clean data on day one, routine work already handled.
Customer service. Voice agents and chatbots now handle high-volume, routine calls at a level that matches — and sometimes beats — human agents. One deployment handled 156,000 calls a month on its own, resolving 94% on the first try. For a services business where customer experience is a differentiator, support that runs 24/7 without dipping in quality changes the economics.
INTERACTIVE: What an AI-powered portfolio company dashboard looks like
Sample data for a hypothetical $42M B2B distribution company. Click the tabs to explore Sales Intelligence, Operations, and Before/After AI views. View Full Screen →
We do not spray AI across portfolio companies and hope something sticks. We are disciplined about where and how we deploy it.
Our framework starts with three questions about every process in a portfolio company:
Where all three overlap, the opportunities are obvious. We build a prioritized backlog and execute in phases:
The real payoff is that this scales across the portfolio. We test something at one company, measure what happens, write the playbook, and run it again at the next one. What works once, works many times.
We have also learned what goes wrong. AI without clean data is a waste of money, so we fix the data first. Broad “digital transformation” programs that can’t point to EBITDA almost always fail. So we pick two or three things per company, not twenty. And every critical decision still has a person making the call.
For decades, PE value creation came down to two things: financial engineering and operational improvement. EY now calls AI the third pillar — a separate, repeatable source of returns that builds on top of the other two.
The math works. For every dollar invested in generative AI, the average return is $3.70. AI-assisted diligence cuts costs by up to 70%. Portfolio companies using AI in sales, ops, and finance report 26-31% cost reductions in those areas.
But the numbers are only part of it. A $40 million manufacturer can now have demand forecasting that rivals a Fortune 500 competitor. A regional services company can deliver 24/7 customer support without tripling headcount. A founder who spent 20 hours a week on manual reporting gets that time back.
At Augeo, AI is not a side project. It is built into how we run the firm and how we improve every company we own. It is how a small team delivers the analysis, speed, and hands-on support our companies need.
The firms that figure out how to use AI well will earn outsized returns. We plan to be one of them — and to bring every company we back along with us.
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