Research, operating perspectives, and practical guidance on technology, AI, diligence, and value creation across private capital.
Showing 3 of 9 articles
A buy-side framework for translating cybersecurity diligence findings into valuation adjustments, contractual protections, closing requirements, insurance actions, and funded post-close remediation.
A decision model for determining which systems, controls, and data should become platform standards across add-on acquisitions and which capabilities should remain local.
A practical framework for turning selected private equity AI initiatives into production workflows through redesign, data and integration foundations, scale-or-stop governance, and portfolio replication.
A practical diligence framework for determining how AI could change a software company’s competitive position, retention, pricing power, margins, valuation, and ownership plan.
A buyer-oriented framework for deciding which technology risks to fix, prove, quantify, disclose, or defer before a private equity exit.
A practical framework for private equity firms and lower-middle-market companies to select, govern, and execute AI initiatives tied to measurable business value.
A practical framework for deciding whether an AI agent has the business case, controls, evidence, economics, and ownership required for production.
A practical framework for determining whether an acquisition target’s AI capabilities are real, defensible, scalable, compliant, and economically valuable.
A practical guide to the role of a technology operating partner across diligence, portfolio operations, AI, systems, and execution for private capital firms.