Insights
Views, not trends, on AI value creation, due diligence and Agentic Engineering.
White papers and templates
- The Technical Due Diligence Template
- The AI Due Diligence Template
- The Portfolio AI Scorecard
- Agentic Engineering in an Existing Team: The 90-Day Rollout
- AI Levers in E-Commerce, from Catalog to Customer Service
- 100 days show whether the AI bet pays off
One lever running in production after 100 days is worth more than three sitting on a roadmap.
- The toolchain is the easy part of this shift
The toolchain is the easy part. Quality assurance and measurement decide whether agentic development actually makes you faster and better.
- The operating model decides, not the tool
Introduce AI agents without resetting roles, approval limits and audit trails, and you get demos, not results.
- In-house AI agents beat SaaS past this point
Buy commodity use cases. Build what's woven into your data and your process logic.
- The data foundation AI agents actually need
An agent is only as good as the access you give it to the truth inside your company, not as good as the volume of data you own.
- The EU AI Act applies to you too, without high-risk AI
The EU AI Act also applies to companies without high-risk AI. The duty to ensure AI literacy and transparency already applies to everyone.
- Customer service agents need knowledge, not scripts
A customer service agent is only as good as the access you give it to real data, not just as good as the language model behind it.
- The best AI roadmap for owners is the smallest one
One AI initiative that's live is worth more than ten sitting on a roadmap.
- Three levers actually move EBITDA through AI
Three levers actually move EBITDA through AI: process cost, margin per transaction, and capital tied up. The rest is preparation or theater.
- Tech DD today must also check AI risk
A technical due diligence that ignores AI today misses both the biggest opportunities and the biggest risks in the stack alike.