{"id":13950,"date":"2026-06-22T19:41:01","date_gmt":"2026-06-22T19:41:01","guid":{"rendered":"https:\/\/cheesecakelabs.com\/blog\/"},"modified":"2026-06-22T19:41:04","modified_gmt":"2026-06-22T19:41:04","slug":"ai-readiness-for-private-equity","status":"publish","type":"post","link":"https:\/\/cheesecakelabs.com\/blog\/ai-readiness-for-private-equity\/","title":{"rendered":"Private Equity is Committed to AI: Here\u2019s Why Most Portfolio Companies Aren\u2019t Ready to Collect"},"content":{"rendered":"\n

Somewhere in a boardroom right now, a private equity Operating Partner is presenting a slide deck with the words \u201cAI-Driven Value Creation\u201d on the cover. The financial model <\/strong>shows 200 to 400 basis points of EBITDA expansion. The hold period is 36 months. The investment committee is nodding.<\/p>\n\n\n\n

Forty-eight hours later, that same Operating Partner walks into a portfolio company and sits down with the VP of Technology<\/strong>. The conversation that follows is the moment everything either starts moving or quietly stalls. <\/p>\n\n\n\n

What the Operating Partner<\/strong> finds, more often than not, is a company running on a 12-year-old ERP system, customer data split across three platforms that don\u2019t talk to each other, and an engineering team that has never built an AI application in production<\/strong>. The financial model looked clean. The infrastructure underneath it does not.<\/p>\n\n\n\n

This is the gap that is defining private equity\u2019s AI moment in 2026 is the implementation<\/strong> gap.<\/p>\n\n\n\n

What the data says about AI readiness in PE-Backed companies<\/h2>\n\n\n\n

The PE industry\u2019s commitment to AI is not hype. It is board-level doctrine backed by real financial data:<\/p>\n\n\n\n

95%<\/strong><\/td>of PE firms have begun<\/a> or plan to implement agentic AI in their operations in 2026.<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n
200\u2013400bps<\/strong><\/td>EBITDA expansion achieved by PE<\/a> funds that deploy AI correctly across portfolio companies within 12 months.<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n
58%<\/strong><\/td>of PE-backed companies<\/a> have no formal AI strategy at the point of acquisition.<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n
72%<\/strong><\/td>of PE Operating Partners now consider IT modernization a top-three lever for value creation \u2014 up from 49%<\/a> three years ago.<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

Read those numbers together and a picture emerges. The ambition is nearly universal. The mandate is backed by financial evidence. And the majority of portfolio companies<\/a> arrive at the starting line without a strategy, without modern infrastructure, and without the engineering capability to build what the operating model now demands.<\/p>\n\n\n\n

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\u201cMost funds get AI wrong because they treat it as 25 separate technology projects instead of one operating model.\u201d<\/p>\n<\/blockquote>\n\n\n\n

The failure mode is consistent. A PE firm acquires a company, installs a value creation plan, and tasks the portfolio company CTO with \u201cimplementing AI.\u201d What follows is a series of disconnected pilots: a chatbot for customer service, an automation tool for accounts payable, a dashboard built on top of a data warehouse that was never properly designed.<\/p>\n\n\n\n

Eighteen months later, the board asks why EBITDA hasn\u2019t moved. The answer is always the same: the initiatives never added up to an operating model.<\/p>\n\n\n\n

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Read more: <\/strong>What We Learned Exploring AI Prototyping Tools<\/a><\/p>\n<\/blockquote>\n\n\n\n

Why standard AI tools fail mid-market portfolio companies<\/h2>\n\n\n\n

This is the year that every major technology company launched an AI product for business<\/strong>. Anthropic launched Claude for Small Business with integrations for QuickBooks, HubSpot, and DocuSign. Microsoft deepened Copilot across the Office suite. Salesforce shipped Einstein across its CRM platform. Bain partnered with OpenAI to build an enterprise deployment practice targeting Fortune 500 companies.<\/p>\n\n\n\n

All of this is real and all of it matters. But none of it solves the problem facing a $150M specialty chemical distributor <\/strong>running on a 2008 ERP system with customer data in three separate spreadsheets and a field service team logging work orders in a system that hasn\u2019t been updated since 2015.<\/p>\n\n\n\n

A DocuSign integration does not fix fragmented data architecture. A CRM AI layer<\/strong> does not replace a warehouse management system that was never built for predictive inventory. A QuickBooks connection does not modernize a 50-location distribution network\u2019s supply chain intelligence.<\/p>\n\n\n\n

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The companies that cannot solve their data fragmentation, compliance requirements, legacy infrastructure, and workflow complexity with a toggle are precisely the mid-market businesses PE firms are acquiring and trying to modernize. That gap is where the real implementation work happens.<\/em><\/p>\n<\/blockquote>\n\n\n\n

There are three categories of solution in the AI market right now:<\/strong><\/p>\n\n\n\n