With the rise of AI, it became clear we're not just seeing a new wave of technology – we're seeing a fundamental shift in how products are built, sold, and adopted.

Over the past decade, I've partnered with early-stage Enterprise-AI founders – always from day zero, and always deep in the build.

That shift made me take a step back and ask: what have we really learned from the field – and what's actually working for startups?

The same questions kept coming up – from both founders and investors:

• How do you embed AI where it actually delivers value? • What drives enterprise user adoption beyond the hype? • What kind of data strategy creates real, lasting defensibility?

So I started writing – aiming to distill what we've learned from partnering closely with dozens of Enterprise-AI companies, working with hundreds of enterprise customers, and learning from thousands of users – into something useful for the teams building right now.

Today we're launching The Enterprise-AI Startup Playbook – the outcome of that learning journey. It's a collection of patterns, insights, and lessons drawn from partnering with founders building and selling Enterprise-AI products into real workflows.

A few core takeaways from the playbook – drawn from patterns we've seen across the strongest Enterprise-AI teams:

• AI is the enabler – not the product. • Seamless integration into existing workflows beats technical perfection or GenAI hype. • User adoption is driven by trust and usability. • Proprietary data often creates a stronger long-term moat than the AI model itself. • The best products create a data flywheel – usage sharpens the system.

We don't claim to have all the answers. The field is evolving – and so are we. But these patterns, drawn from real trenches, have already helped us become better partners to early-stage teams.