Engineering Alpha in Private Equity

Measuring the ROI of Tech Transformations

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How do you actually prove that a massive technology transformation is working? In Episode 4, we flip the script and dive into Paul Karner’s world of data science and causal inference. Using a real-world example of a private equity portfolio company that implemented machine learning to automate manual document review, Paul explains the critical process of tracking tech ROI. Dave and Paul break down why a tech transformation shouldn't just be a cost-saving measure, the nightmare (and necessity) of tying operational tech data directly to your ERP, and how to start with your financial end-goals and work backward.

Key Takeaways:

  • The Cost-Savings Trap: Why undertaking a tech transformation where the only goal is cost savings usually leaves money on the table—or fails entirely.
  • Connecting the Silos: Why you must marry up operational data with your timekeeping and ERP systems to prove that a new tool is actually saving labor hours.
  • True EBITDA Leverage: How to avoid the "tokens versus humans" trap. Instead of just replacing headcount, learn how redeploying saved labor hours to scale output sends every additional margin dollar straight to the bottom line.
  • The 30-Day Playbook: Paul’s actionable advice for leaders currently in a transformation: start with a clear vision of how the project rolls up into the P&L, crack into the financial data, and work backward to find the gaps.
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    Engineering Alpha in Private EquityBy Paul Karner and Dave Mangot