A Better Exit: How Private Equity Can Utilize AI to Sell Portcos More Effectively

Private equity firms are sophisticated buyers, constantly searching for prospective acquisitions that will give them plenty of value creation upside, and AI continues to evolve as a must-have tool to enhance purchase decisions. However, downstream in the value creation journey, AI is also taking root as a key component for private equity firms to prepare portcos for sale while maximizing actual returns on their investments.

When it comes to preparing an exit, the same skills that make for a great buyer—identifying prospects, cleaning up problems, and demonstrating upside—can help firms and portfolio companies get a much better price. And in the same way that AI can rapidly speed up due diligence, help generate cash fast in the first days of ownership, and improve post merger integration, it can also make preparation for sale faster and more comprehensive. AI can be helpful in exit preparation, particularly when the deal market is soft and exits are hard to come by—and especially when AI tools are used by experts with deep knowledge of the dynamics of the market and the industries involved. This approach can lead to a big improvement in the price, and even be the difference between selling and having to hold longer. 

Sponsors and portco executives have many ways of using AI to make their companies more attractive to buyers. This article describes three insightful approaches that, in our experience, can quickly demonstrate tangible value and make the approaches visible to prospective buyers:

1. Improving commercial operations
2. Cleaning up legacy technology
3. Identifying opportunities in existing vendor agreements and contracts

All three are important, and all of them can fix problems in anticipation of due diligence and spotlight opportunities for the new owner. With existing, proven AI and machine learning (ML) tools, these tasks can be completed quickly and efficiently.

Identifying strengths, weaknesses, and opportunities in commercial operations

Buyers seek upside by moving ahead on a clear path to generate quick returns on their new investments while also laying the groundwork for longer-term gains. Demonstrating opportunities for revenue growth is a great way to reveal that upside and, paradoxically, so is identifying problems when the problems are accompanied by a plan to resolve them. 

Advanced technologies such as AI/ML can be deployed to identify multiple ways to identify—and quantify—opportunities in salesforce effectiveness, customer churn, pricing, customer service, and other commercial activities. For example, analyzing customer acquisition costs, retention rates, and customer lifetime value can uncover ways to generate gains in customer loyalty while also revealing underexploited opportunities. We partnered with a  $5-billion company in the retail industry to train ML models that analyzed sales, marketing, and customer data at a granular level. This approach provided deep insights into the effectiveness of the retailer’s promotional campaigns, and identified untapped potential to improve revenue generated by future initiatives by 30%. Without ML, the effort would have taken weeks of examination. With another client, a furniture retailer, we used ML models to plot out what-if revenue and margin scenarios based on different combinations of initiatives, and as a result, the seller became able to show potential buyers several possible paths to rapid commercial success. 

Smart sellers can also use AI to conduct rapid analyses of potential buyers that identify potential synergies with the offerings of the acquiring company and which levers should be pulled to make it happen. That’s especially persuasive when the seller can point to opportunities it hasn’t itself been able to seize because it lacks capital or other resources the buyer might be able to provide.

Cleaning up legacy technology

One of the most valuable applications of AI is its ability to address “technology debt’.”   Technology debt is what companies incur when they fail to keep legacy systems up-to-date or they resort to quick fixes that don’t fix the underlying issues. CISQ, the Consortium for IT Software Quality, estimates that by next year, nearly 40% of IT budgets will be spent on tech debt—and buyers know to look for it during due diligence. Buyers also know that the post-merger integration of tech stacks is nearly always a source of frustration due to the complexity and diversity of software; as we documented in our 2024 Digital Disruption Survey, both business and technology executives agree that post-merger integration is the technology management challenge they most often struggle to address effectively. Sellers can bolster their case by demonstrating that they have tech debt under control—or at least that they can show where it is and what it would take to resolve it. 

This is difficult, detailed, technical work, and AI can be of enormous help. AI can be trained to flag outdated code, conduct security assessments, and assist in the tedious but important process of code refactoring: cleaning up code that has become messy or outdated through the years. Our experience shows that AI can reduce refactoring time by as much as two-thirds and cut labor costs by 15 to 20%. Some of today’s AI models are even writing entire applications autonomously. As those models improve, companies will realize material benefits to the ways they develop and work with software. 

Vendor and Contract Management

A seller should think like the investors it hopes to attract. That means the seller should ask itself what a buyer wants to find—and doesn’t want to find—when considering a potential acquisition. A significant opportunity lies in understanding who your vendors are, the terms of your contracts with them, and the associated spending. Sellers can now apply AI to their existing vendor agreements to uncover critical details related to specific terms, change-of-control clauses, termination penalties, guarantees, and more. That applies to agreements of all types, such as facilities, leases, and software. With AI, you can extract specific data points, clauses, or total value across the entire vendor base or portfolio in hours instead of weeks. 

This approach has the added benefit of being able to capture details from often-ignored areas—like tail spend, which historically has been ignored due to the manual effort involved in reviewing hundreds or thousands of obscure PDF files. Working with one discount retail chain, we applied generative AI in a novel way to process more than 12,000 contracts in less than an hour to examine the client’s landlord and geographic footprint. Smart buyers are using AI for vendor management in post merger integration; smart sellers can get there first by applying technology to do the work up front. 

In short by being deliberate and strategic in one’s deployment of AI and ML technologies and by knowing the limitations and constraints of real-world scenarios, executives can realize the benefits of data they already have. Generating those insights has three benefits:

First, the insights make you more attractive to a buyer by helping the buyer build a compelling deal thesis with both revenue and cost projections supported by real, data-driven insights. Second, it's good business for you anyway: even if no deal happens – or until it does – you'll have more insights into ways of improving the business. Third, you will have demonstrated something highly attractive – and rare – to a potential buyer: the ability to make effective, value-creating use of AI devoid of the hype. Given a choice between two otherwise similar target companies, a buyer will prefer, and probably even pay a premium for, the one that has proven its ability to leverage AI for real business impact.  

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