Sahin Boydas
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How Bad AI Strategy Nearly Sinks Companies

I confess: my initial approach to AI was a disaster. I'm sharing the painful story of my biggest leadership failure and the redemption that followed, hoping you can avoid my near-fatal mistakes.

2024-10-148 min read537 words

I confess: my initial approach to AI was a disaster. I'm sharing the painful story of my biggest leadership failure and the redemption that followed, hoping you can avoid my near-fatal mistakes.

The Counterintuitive Truth

In AI strategy, the best practitioners often do less, not more.

The lesson: most founders overthink this and underspend on execution. It sounds simple. It's incredibly hard to execute.

What I've Learned From 47 Companies

A few patterns in AI strategy come up again and again.

The founders who handle AI strategy well understand that the data tells a different story than your gut.

Why Most Approaches Fail

Many common approaches to AI strategy are flawed at the root.

The root cause is usually one of three things:

Copying what big companies do without understanding why they do it. What works for Google doesn't work for a 10-person startup.

Over-engineering the solution when a simple approach would work better. I've seen teams spend six months building something that could have been done in two weeks.

Ignoring the human element. Technology is the easy part. Getting people to actually use it is where the real challenge lives.

The AI Angle

Any discussion of AI strategy 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 AI strategy. 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 leading through uncertainty, AI change management, managing AI teams 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 AI strategy right on day one. You just need to get started and keep improving.

Founders who do well at AI strategy 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.

FAQ

Frequently Asked Questions

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.

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.

Written by

Sahin Boydas

Serial entrepreneur, #1 angel investor, author of Becoming Top 1%

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