Slow AI Adoption (And Why You Should Fight for It)

Slow AI Adoption (And Why You Should Fight for It)

Everyone wants to move fast. Move fast, ship fast, iterate fast. It's the default language. But when it comes to AI, moving fast might be the worst thing you can do.

Here's the data: Companies prioritizing speed have a 70% failure rate within 18 months. The slow companies—the ones that took 3–6 months to plan, then 3 months to pilot—have a 73% success rate.

You're watching people run toward a cliff. Don't follow.

Why Slow Actually Works

Speed assumes you know what you're doing. Most companies don't. They haven't asked what problems AI actually solves. They haven't asked what their data situation looks like. They haven't asked what could go wrong. Speed skips the questions and jumps to the answers. That's how you fail.

Slow companies ask the hard questions first:

• What specific problem are we actually trying to solve? (Not "should we use AI," but "what exact output do we need and why?")
• What's our data quality? Where are the gaps?
• What could go wrong if this doesn't work as expected?
• How do we know if this actually helped?
• What organizational change do we need to make this work?

That takes time. It's uncomfortable. But it prevents expensive failures.

The Three-Month Principle

A month is too short to understand anything. Three months is the minimum viable timeline for real learning:

Month 1 (Planning): What are we trying to do? Why? What could go wrong? What do we need to learn?

Month 2 (Pilot): We do a real pilot on a real problem with real data. Not a demo. A pilot.

Month 3 (Assessment): Did it work? What broke? What do we need to change? Should we scale this, pivot, or stop?

Then you iterate. But you iterate from a foundation of actual learning, not speed.

Governance and Monitoring

Slow companies also build governance and monitoring. They ask: Who decides what gets built? How do we catch when things go wrong? How do we handle hallucinations or biased outputs?

Fast companies don't. Then they deploy an AI system that gives bad recommendations or biased outputs and they get sued.

How to Push Back

Your job is to slow things down responsibly. When your company wants to speed up:

• Ask questions. "Do we actually know if this will solve the problem we're trying to solve?" Force the thinking.
• Demand pilots before scale. Don't let them launch company-wide without testing at scale first.
• Push for monitoring. "How will we know if this is working? How will we catch if it breaks?"
• Insist on data work. "Do we have the data quality required for this to work?"

These aren't slow. They're smart. And your company will thank you when you avoid the failures that plague the speed-obsessed.

Speed is for execution, not for strategy. Get the strategy right, then move as fast as you can.