Every month, another mid-market company announces an AI initiative. Six months later, the project is quietly shelved. The technology wasn't ready, they say. The vendor overpromised.
That's rarely the real story.
The foundation problem
The problem isn't the model. It's what's underneath it. AI systems need clean, consistent, accessible data to function. They need clear processes they can augment or automate. They need organizational alignment on what "success" actually looks like.
Most companies don't have any of that when they start an AI project. They have years of data scattered across five systems that don't talk to each other. They have processes that live in people's heads. They have leadership teams with competing visions of what AI should do for the business.
Deploying a model on top of that chaos doesn't make the chaos go away — it amplifies it.
What the mountain taught us
There's a reason you don't start a 14er ascent at 3pm in July. The summit might be technically reachable — but the conditions aren't right, and the risk isn't worth it. You come back when you've done the preparation.
The same logic applies to AI. The technology is ready. The question is whether your business is.
What readiness actually looks like
Before we take a client into any AI implementation work, we ask three questions:
Can you describe where your data lives and who owns it? Not a vague answer — a specific one. If the answer is "it's complicated," that's the first thing we fix.
Do you have documented processes for the workflows AI would touch? AI can automate a process or augment a decision. It cannot define either from scratch.
Is there leadership alignment on the outcome? Not just enthusiasm — alignment. Someone has to own this. Someone has to be accountable when the first version doesn't work perfectly.
If the answer to any of these is no, we do that work first. It's less exciting than deploying a model. It's also the reason our projects actually deliver.
The path that works
The companies we've seen get real value from AI consistently do the same things: they start smaller than they think they should, they fix their data before they touch models, and they pick one workflow to transform rather than trying to change everything at once.
It's not glamorous. But 23 summits in, the approach that works is the approach that's prepared — not the one that moves fastest.