Why Basic Visibility Trumps AI Strategy for Most Portfolio Companies, According to Lee McCabe

In a recent LinkedIn post, Lee McCabe discusses why many private equity portfolio companies are focusing on the wrong priorities when it comes to technology adoption, arguing that a foundational need for basic operational visibility often precedes any meaningful AI strategy.

McCabe highlights a common scenario faced by these companies: board members asking about AI strategies while the underlying business struggles with fundamental data challenges.

“Most portfolio companies do not need an AI strategy yet. They need basic visibility.”

The Gap Between Board Expectations and Operational Reality

McCabe points out the disconnect between the board’s forward-looking questions about Artificial Intelligence and the company’s current inability to answer basic performance queries. He illustrates this with the common struggle to ascertain weekly revenue figures, often requiring complex, multi-spreadsheet analyses and financial team interventions.

“Private equity is trying to have an AI conversation with businesses that still have an information hygiene problem,” McCabe writes, detailing a fractured data landscape where different departments (Ops, Finance, Sales) operate with conflicting or incomplete versions of the truth.

Focusing on Tools Over Fundamentals

According to McCabe, the excitement around AI often leads to an premature focus on advanced tools like automation, AI layers, and sophisticated dashboards. While these are important, he suggests they are ineffective without a solid data foundation.

“The excitement goes straight to tools. Automation. AI layers. Dashboards. Forecasting. Productivity gains. All the fashionable language. But underneath it, the business still does not have a reliable weekly grip on revenue, margin, pipeline, conversion, labour efficiency, or what is actually driving performance.”

He categorizes this misplaced focus not as an AI opportunity, but as an “operating problem.” McCabe emphasizes that AI’s utility is contingent upon a business having robust data, disciplined processes, and a consistent management rhythm.

The Importance of Data Hygiene and Management Rhythm

McCabe argues that the “boring truth” for many portfolio companies is the need to prioritize foundational elements before diving into AI. These include:

• Clean data
• Consistent reporting
• System adoption
• Establishing a single version of commercial truth

Without these essentials, any discussion of an “AI strategy” becomes a way to avoid addressing more fundamental operational deficiencies. As McCabe concludes:

“Until then, ‘AI strategy’ is mostly a modern way of avoiding the far less glamorous fact that the company still cannot explain last week properly. And if you cannot explain last week, you are not ready to automate next quarter.”

McCabe’s analysis suggests a call for a more grounded approach to technology implementation in PE-backed businesses, urging a focus on operational fundamentals to build a reliable base before layering advanced technologies like AI.