The PE-exit diligence framework.

Tech diligence isn't just about architecture anymore. Here's what buyers actually spend their time on.

In my last post, I shared the 7-area AI due diligence checklist. But AI readiness is just one piece. Here's the full framework.

During our exit, I spent the most time preparing the architecture. I thought that's where buyers would go deepest.

I was wrong.

They spent the most time on Pillar 6 (data strategy) and Pillar 3 (talent risk). The architecture questions were almost procedural. The data questions felt like an interrogation.

The 6 Pillars:

Pillar 1

Product Discipline

Scalable commercial engine vs. a tool. Roadmap governance. Multi-tenant codebase or drowning in client forks? Integration readiness.

Pillar 2

Can your foundation support 5 years of growth without re-platforming? Stack tiering. EOL plans. Bottleneck roadmap.

Pillar 3

Human Capital&Talent Risk

Is the business dependent on 2-3 key people? Buyers dug hard here. Key contributor risk. Skill mapping for Cloud, AI, DevOps. Performance culture.

Pillar 4

Operational Excellence

R&D allocation across maintenance, innovation, tech debt. Unit economics. Documentation over tribal knowledge.

Pillar 5

Cyber, Compliance&AI Governance

Vulnerability hygiene. OSS policy with automated SBOM. GenAI governance for code risk and customer data.

Pillar 6

Data Strategy&Monetization

With AI uncertainty, valuations increasingly depend on narrative, not just numbers. Data is the foundation for AI, a defensible moat in a world where AI is commoditizing everything else. A strong data story matters more than ever.

These aren't six separate boxes to check. They're interconnected. Architecture enables data. Data powers AI. AI improves product. Product attracts talent. Talent builds better architecture. It's a flywheel, and buyers are looking at how fast yours spins.