The slide deck looked confident. Twelve pages. A clear value creation thesis. AI-driven operational improvements projected to expand EBITDA by three percentage points within the first 18 months. The investment committee approved it. The deal closed.<\/p>\n\n\n\n
Three weeks later, the Operating Partner sat across from the portfolio company\u2019s VP of Technology and asked a simple question: \u201cWhere does your customer data live?\u201d<\/em><\/strong><\/p>\n\n\n\n The answer took eleven minutes.<\/strong> There was a CRM. There was also a separate order management system. There was a data warehouse that had been partially built two years ago and then deprioritized when the lead engineer left. There were spreadsheets \u2014 more than anyone had counted. The ERP was from 2011. The API layer that was supposed to connect everything had never been finished.<\/p>\n\n\n\n The EBITDA model had assumed that data was accessible, clean, and queryable. It was not. And until it was, nothing in the AI strategy could move.<\/p>\n\n\n\n This is not an unusual story. It is, in fact, the most common technology story in mid-market private equity<\/a>. And it is exactly why the AI strategy<\/a> conversation is the wrong conversation to have first.<\/p>\n\n\n\n Private equity firms are extraordinarily disciplined about financial due diligence. Legal, accounting, market analysis, customer concentration, supplier risk \u2014 all of it is stress-tested before the deal closes. The financial model is built on verified inputs.<\/p>\n\n\n\nStrategy without diagnosis is guesswork<\/strong><\/h2>\n\n\n\n