What You Defer in the First 100 Days You Don’t Recover

Here is a pattern we’ve seen enough times that it bears calling out. A deal closes after a competitive, exhausting process. Thorough diligence has framed the IT plan by ranking technology risks, identifying integration priorities, and flagging leadership gaps. But when the ink dries, the deal team shifts to the next transaction while the portfolio company is passed off to an operating team who wasn’t in the room during diligence.  

The business is running and revenue is coming in.  Nobody owns the IT plan, though, and there’s no forcing function to act on it. Five weeks pass before someone asks about it, and by then there are already new priorities competing for attention and still no named owner to drive the original work forward. 

This is a sequencing and ownership failure, and it's deceptively expensive. Because nothing is obviously “broken” in business operations, organizations can easily make the mistake of forgetting about executing that IT plan until the hold period is half over and the value creation timeline is no longer what the model projected. 

In the examples that best illustrate what getting the data foundation right makes possible, the value was already there but the infrastructure to see it wasn't.

A highly acquisitive commercial and residential HVAC company growing through a deliberate roll-up thesis ran into a version of the problem that most acquisitive mid-market businesses encounter. Every acquired company ran on different systems, tracked different metrics, and managed its own financials independently.

The firm knew it needed consolidated reporting. But until it built a shared data platform and began normalizing metrics across acquired companies, it didn’t know how much insight was locked inside the comparison. The company could benchmark acquired businesses against each other and bring best practices from the top performers to the rest. It also discovered that some units had been misclassifying project revenue as recurring revenue. Correcting that reclassification contributed to more than $100 million in incremental enterprise value. The data was always there. Before implementing a shared platform, there was no way to compare the businesses against each other, and the misclassification compounded unnoticed in the financials.

This pattern repeats across industries and deal types. A distribution business measures on-time delivery against internal shipment records rather than customer delivery windows. At the executive level, performance looks fine. Meanwhile, the business is expediting orders to cover delivery failures the internal metric was never designed to catch, and the margin erosion from those expediting costs doesn't show up anywhere until the data foundation exists to connect the two.

Organizations that have learned to operate without that foundation stop asking for what they're missing. The value creation plan keeps working around the gap rather than closing it, and the hold period stretches to accommodate a plan that was never as executable as it looked.

Most PE-backed companies have more AI enthusiasm than AI infrastructure, and the distance between the two is where adoption stalls.

The parallel to the business intelligence boom of the early 2000s is instructive. Organizations that deployed analytics tools on poor data infrastructure spent years recovering credibility before those tools could be used as intended. The dynamic with AI is the same, compressed into a shorter window because the tools are more visible and the expectations are higher.

Where AI is generating real, measurable value in the hold period is narrower than the conversation usually suggests. The most effective early applications are connective, bringing together data from systems that previously required manual reconciliation, accelerating management reporting, improving the speed and completeness of diligence analysis. That compression of time between data collection and insight compounds across the hold period.

A portfolio company management team with faster reporting cycles makes better decisions. A diligence team that can synthesize findings faster produces better recommendations. Neither outcome is possible on fragmented infrastructure, which is why the foundation work and the AI agenda aren't sequential. They're the same investment.

The firms that get to value faster in the hold period aren't doing something exotic. They've made one decision that others defer: technology and data come first, not after the business has stabilized. Every specific choice that follows, which KPIs to build, who owns the plan, how to govern the data, flows from that posture. And that decision gets made, or not made, in the first 100 days.

That window matters more than most firms treat it. The first 100 days are when change is most expected, most accepted, and most executable. Leadership is paying attention. The organization hasn't yet settled into the patterns it will hold for the next four or five years. The decisions made in this window compound across everything that follows. Get them right and the hold period accelerates. Defer them and the plan works around the gap, month by month, until the value that should have been realized isn't.

McKinsey's 2026 Global Private Markets Report notes that the firms pulling ahead have more than doubled their operating group size since 2021, with 60 percent now using operating group members to identify and quantify bankable performance improvements during diligence itself, before close, not after. The engagement is moving earlier because the firms that wait are learning, hold period by hold period, that the cost of waiting is not recoverable.

The first 100 days don't guarantee the outcome. But they determine the speed at which the outcome becomes possible.