We were on the brink of collapse, a classic 'AI for everyone' startup with no focus. Then, one painful decision changed everything. Here's the real playbook for finding your niche in enterprise AI.
The Reality Nobody Talks About
A lot of the advice on enterprise AI rests on assumptions that made sense five years ago. The world has moved on.
The first thing to understand is that customer feedback is the only metric that matters. I've seen this play out across dozens of companies. The pattern is unmistakable.
Many teams spend months on the wrong path before realizing that the best solutions are often the simplest ones. Once they make the switch, everything changes.
The Framework That Actually Works
Here is a simple framework for evaluating enterprise AI. It's not complicated, but it requires discipline.
Step 1: customer feedback is the only metric that matters
This is where most people go wrong. They skip this step entirely and jump straight to execution. Don't do that.
Step 2: most founders overthink this and underspend on execution
Once you have the foundation right, this becomes much easier. I've watched founders struggle with this for months when the answer was staring them in the face.
Step 3: Iterate relentlessly
Nothing works perfectly the first time. The companies that get enterprise AI right treat it as an ongoing process, not a one-time project.
The AI Angle
Any discussion of enterprise AI in 2026 has to include AI. As someone who's invested in Anthropic, OpenAI, Scale AI, and Hugging Face, I have a front-row seat to how AI is transforming this space.
The short version: AI makes good practitioners better and bad practitioners worse. It's an amplifier, not a replacement.
Some teams use AI to get far more out of enterprise AI. Others spend heavily on AI that solves the wrong problem. The difference comes down to understanding what you're actually trying to achieve.
This connects to broader themes around AI strategy, AI implementation, business intelligence AI, enterprise AI, AI consulting that I've been thinking about a lot lately.
Final Thoughts
There are no shortcuts in enterprise AI, but there are smarter paths.
The strongest teams treat enterprise AI as a competitive advantage, not a checkbox. They invest in it early, measure it obsessively, and never stop improving.
If you're just getting started with enterprise AI, don't be intimidated. Everyone starts somewhere. The key is to start with the right mindset and the right framework, and then execute like your company depends on it. Because it probably does.
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: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.
Q:What's the most common pushback you get on this?
People often push back by citing exceptions or edge cases. And they're usually right that exceptions exist. But building a strategy around exceptions rather than patterns is a losing game for most founders.
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.