A few weeks ago, I wrote about whether AI is lowering MSP valuations, and my answer was no (at least not yet). But there is an aspect of the M&A process that AI is having a huge impact on: its speed.
From the buy-side, it has drastically cut the time it takes to work through a data room. From the sell-side, it’s optimized the process of preparing to go to market.
Those who know me know my mantra is “Time kills all deals,” and that has never been more true.
AI is changing the pace of the whole process, and those who aren’t keeping up risk being left behind.
On the Buy Side: Data and Diligence Moves Faster
For years, a lower middle market technology deal followed a fairly predictable rhythm. Seller materials went out, buyers spent a few weeks reviewing it, first round indications of interest (IOIs) or letters of intent (LOIs) came in, a handful of buyers were invited to continue the conversation, an LOI was signed, and a standard 90-day diligence period began. That rhythm still exists, but the timeline inside it has compressed.
Buyers are using AI earlier in their search process, not just during diligence. A group that used to rely on an outreach list or a manually screening industry databases can now scan far more companies, far faster, and flag the ones that fit their thesis. That is part of why I am seeing more owners receive unsolicited interest well before they ever planned to go to market. Not every unsolicited offer is a good one, but they are happening more frequently.
Buy-side AI capabilities have also shifted the norms for go-to-market materials. Previously, in addition to a preliminary data room and a blind profile, we would spend weeks crafting a highly detailed 30- to 40-page CIM. This was the primary way a seller’s story (and key numbers) reached a buyer. Now, after the blind profile, we’re finding that many buyers prefer to simply download the data room and run their own analyses on it using their AI tools – no CIM required. At the end of the day, the numbers tell the real story that will make or break a deal. This gets everyone to the heart of it sooner.
The same speed is showing up in due diligence after the LOI as well. Data that used to take a buyer’s deal team weeks to work through can be processed in a fraction of the time using AI. More and more, we’re seeing the standard 90-day due diligence period shift to 60 days. But even if due diligence timelines are compressing, the rigor behind them is not. Buyers still ask the same hard questions. They simply get to them faster, which can leave a seller less time to catch and fix a problem before it becomes a buyer’s problem to find.
AI is also becoming part of how some sellers evaluate buyers. A few clients this year have told me that a buyer’s AI strategy (whether it be used to expand service offerings, improve margins, or otherwise stay competitive), was a meaningful part of their decision between offers. That is a newer element in negotiations that simply did not exist a few years ago.
None of this is about whether your business itself uses AI. It is about how fast the market around your sale is moving on the buy side, and whether your side of the table can keep up.
On the Sell Side: How We Use AI to Prepare You Faster
On our side of the table, the shift looks different, but it is just as real. Before AI, turning messy, disparate data into clean, organized schedules was slow, manual work. Katie and I would spend weeks reconciling spreadsheets by hand before we ever got to the interesting part: figuring out what the numbers actually meant for a seller’s story and their multiple.
Today, we use AI-powered analysis to do the tedious, time-draining work so we can spend more time on things that really matter: understanding the ins and outs of your business, getting ahead of items that a buyer might flag, and setting you up to get every dollar possible for your business.
That doesn’t mean the analysis changed. It means I get to it faster.
We’ve built AI skills that will take a handful of basic reports out of Quickbooks or other accounting software and quickly create in-depth customer churn and concentration reports, analyze revenue by industry or line of business, break down customer mix, create detailed P&L summaries over multiple years, and more.
One of the most tedious tasks has historically been aliasing customer and employee names. It’s imperative to protect this sensitive information when courting buyers, and we often don’t recommend sharing names until well into the due diligence process, after an LOI. What previously took hours across multiple files can now be done with a few clicks. In fact, we’ve trained our tools to catch any unaliased names we may have missed in our go-to-market materials.
AI is also allowing us to more effectively respond to specific buyer requests. Early in the process, it’s not uncommon for a buyer to ask for reports that aren’t in the data room. Previously, we might not have had the time or bandwidth to accommodate, especially those that would require parsing through large amounts of data. Now, it’s much easier to deliver these requests in a timely manner, giving buyers what they need to make their best offer and keeping the process moving forward.
AI-powered analysis has also helped us compile more effective support for EBITDA normalizations. Our tools let us match potential addbacks to source detail far faster than a manual review ever allowed, which means the normalization schedule we build is more complete and more defensible, not just faster to produce.
For example, we had a client that ran personal expenses through the company credit card, totaling a meaningful amount that could be added back. They identified these charges via their credit card statement, though, and we needed to match each one to a GL entry to understand which expense accounts they hit on the P&L. As a manual task, this would have taken hours, if not days. The sell-side QofE firm they hired said they would have to charge extra if they were to tackle it. Instead, we used AI to run the process. After setting up the skill and reviewing the results, we ended up with all of the data we needed, saved our clients money, and were able to confidently support the addbacks when it came time for the buy-side QofE.
The value of using AI is not the speed for its own sake, but what the speed buys us: more time to look at what the numbers actually mean and more opportunity to tell the right story.
The Analysis Still Gets a Second Set of Eyes
None of this replaces judgment, and I want to be direct about that, because I know some owners hear “AI” and assume something important got automated away. It didn’t. What changed is how quickly a first pass gets built, not who decides what belongs in front of a buyer.
AI is very good at pattern recognition across large data sets. It is not good at knowing that a one-time bonus paid to a departing employee two years ago should not be treated the same way as a bonus paid every December; or that a customer who “churned” was not actually lost, they were acquired by another one of your customers and the revenue never left. Those are judgment calls, and they still get made by an experienced advisor: informed by the analysis, not replaced by it.
I think of it the way I think about a Quality of Earnings report before it goes in front of a buyer. The report itself does not close a gap in trust. What closes that gap is knowing someone with real expertise and transaction experience stood behind every number in it.
Why This Protects a Seller’s Valuation
Buyers do not expect every number to be ideal. They expect every number to be explainable. A buyer who asks a hard question and gets a clean, immediate answer moves forward with confidence. A buyer who has to wait a week, or gets an answer that shifts from the one before it, starts wondering what else in the data room might not hold up.
That hesitation is where deals lose value. Retrades usually happen in the space between a signed LOI and a closing call because something surfaced in diligence that should have been caught and explained months earlier. The faster and more consistently we can put organized, accurate numbers in front of a buyer, the smaller that window becomes.
It’s also a matter of maintaining leverage as a seller. A seller whose numbers do not hold up under a buyer’s first round of questions rarely gets a clean second chance. The buyer usually does not walk away outright, but the tone of the process changes. Requests get more granular, terms get more conservative, and the multiple that looked achievable in the first conversation quietly becomes harder to hold onto by the time a purchase agreement is drafted.
How to Stay Ahead of This as a Seller
If speed is now built into the process on both sides of the table, the sellers who benefit most are the ones who show up ready, not the ones who start organizing their records after a buyer has already asked the first hard question. Two things make the biggest difference here, and neither one requires you to use AI yourself.
Keep your financials clean and current, not just the year before you plan to sell. When your books are already organized, we can move through the kind of analysis described above in days rather than weeks, which matters more now that buyer requests can move just as quickly. A buyer who is used to fast, AI-assisted answers from their own team will notice and lose confidence if your side of the table is still working off messy, non-GAAP books. And because more sellers are being approached before they ever planned to go to market, the runway to get your financial house in order before a buyer shows up interested is shorter than it used to be.
Work with an advisor who already has an AI-powered process built and ready. Not every advisor has done this work. Some are still preparing sellers the way it was done ten years ago, one spreadsheet at a time. That is not necessarily a bad advisor, but it does mean a slower start, more manual back and forth, and less time spent on the parts of the process that actually require judgment, not just organization. It is worth asking a prospective advisor directly how they use AI in their own process. Not because they need to write code or build software, but because the answer tells you how quickly, and how well, they can get your numbers in front of a buyer.
I have said for a long time that time kills all deals. Every week that a buyer spends waiting for quality sell-side data is a week where their enthusiasm can cool, where a competing opportunity can pull their attention elsewhere, or where a straightforward process turns into a drawn-out one. Being ready, and working with someone who can move quickly once you are, is the best protection against that.
Common Questions Sellers Ask About AI and the Sale Process
Do I need to use AI in my own business before I sell?
No. What matters is that your advisor uses it well on your behalf, not that you have adopted it yourself. As of right now, this has no bearing on how a buyer will value your fundamentals.
Can I just have AI review my financials without anyone checking the output?
We don’t recommend it, as AI can make mistakes. Every schedule and adjustment we build still gets reviewed by an experienced advisor before it ever reaches a buyer.
Does better preparation mean diligence will be faster for me too?
Often, yes. The better organized your data is going in, the less back and forth a buyer typically needs once questions start. Still, the overall length of diligence depends on the complexity of your business and the buyer’s own process.
How long does it typically take to get my financials diligence ready?
It depends on the complexity of your data, but what used to take several weeks of manual cleanup can often be done in days now. “Fast” only matters if it is also accurate, which is why every output still goes through a full review before a buyer sees it.
Will using AI to prepare data for a transaction change my valuation?
No, not directly. This is about how quickly and accurately we can present your numbers, not what a buyer ultimately decides they are worth. The fundamentals – recurring revenue, margins, customer concentration, and growth – still drive that conversation. AI can give us more opportunity to identify and get ahead of issues before they become major problems, though, which helps us protect your valuation.
What This Means If You’re Planning to Sell
You do not need to become fluent in AI tools yourself, and you do not need to figure this out alone. What you do need is financials that are organized enough for us to hit the ground running, and an advisor whose own process can keep up with the depth and pace of buyers today.
What matters is this: by the time a buyer sits down with your numbers, they should not be the first version anyone has stress-tested. They should be the version that has already survived a magnifying glass similar to the one the buyer is about to hold up to them.
If you’re planning to sell in the next one to three years, let’s talk about where your numbers stand today, and how ready they are for a transaction.
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