After three years and dozens of clients, my AI consulting firm has a treasure trove of data on what works and what doesn't in business intelligence. I'm sharing our most surprising findings.
The Counterintuitive Truth
In enterprise AI projects, the best practitioners often do less, not more.
The lesson: simplicity beats complexity every time. It sounds simple. It's incredibly hard to execute.
What I've Learned From 56 Companies
A few patterns in enterprise AI projects come up again and again.
The founders who handle enterprise AI projects well understand that the data tells a different story than your gut.
Real Talk: What Actually Matters
Here is what actually matters in enterprise AI projects.
First, execution speed beats perfection. Every time. Companies rarely fail because they moved too fast on enterprise AI projects. Many fail because they moved too slowly.
Second, measure everything. If you can't measure it, you can't improve it. Set up tracking from day one, even if it's basic.
Third, talk to your users. It sounds obvious, but many founders make decisions about enterprise AI projects in a vacuum. Get out of the building. Talk to real people.
This connects to broader themes around AI implementation, AI consulting, AI strategy, corporate AI adoption, enterprise AI that I've been thinking about a lot lately.
Wrapping Up
I've shared a lot here, and I know it can feel overwhelming. But you don't need to get enterprise AI projects right on day one. You just need to get started and keep improving.
Founders who do well at enterprise AI projects tend to share one trait: they're relentlessly practical. They don't chase perfection. They chase progress.
That's the mindset I'd encourage you to adopt. Start where you are. Use what you have. Do what you can. And keep pushing forward.
As always, I'm rooting for you.
Frequently Asked Questions
Q:How has this view evolved over time?
My thinking on most topics has changed significantly over the years. Early in my career, I held many conventional views that experience proved wrong. I try to update my beliefs when the evidence changes.
Q:How can I apply this thinking to my own situation?
Start by identifying the core principle behind the opinion, not the specific example. Then ask yourself: does this principle apply to my context? If yes, test it in a small, low-risk way before going all in.
Q:What experience informs this perspective?
This perspective comes from over a decade of building companies in Silicon Valley, two successful exits (RemoteTeam to Gusto, MovieLaLa to Gfycat), and investing in 234 startups including Anthropic, OpenAI, and Scale AI. I write about what I've lived.
Q:Do all experts agree with this view?
No, and that's fine. The best ideas in business are often contrarian. I share my perspective based on my experience and data, but I encourage you to seek out opposing viewpoints and form your own conclusions.