AI adoption curve and getting started
Most companies stall in the same place. A few people on the team are using ChatGPT for meeting notes or email drafts — individual experiments that nobody coordinates. The jump to organizational adoption requires something different: shared data foundations, governed deployment, and a specific operational problem to solve. The companies that move fastest don't start with an AI strategy. They start with a frustration — a report that takes too long, a process that breaks every month, a question nobody can answer without three spreadsheets — and apply AI to that one thing. Prove value there, then expand.
Go deeper
Your HVAC company has three people using ChatGPT to draft customer emails, one dispatcher who uses it to optimize routes, and a controller who is terrified of it. You are in the experimentation phase — individual adoption without organizational strategy. The risk is not that people are using AI. The risk is that they are each using it differently, with no shared data, no security review, no quality checks, and no way to scale what is working to the rest of the organization.
The trap most companies fall into is jumping from experimentation to buying an enterprise AI platform. They skip the foundation: What data do we have? Is it clean? Who should have access? What decisions would change if we had better data? The organizations that succeed at AI integration are the ones that got their data house in order first — even if that meant a boring six months of data cleanup before anything flashy happened. The AI is only as good as the data it can access.
Questions to ask
- How many people in our organization are currently using AI tools, and do we have visibility into what data they are putting into those tools?
- What is the single operational question that, if we could answer it in real time, would most change how we run the business?
- Before we buy any AI tool, can we describe our data — where it lives, how clean it is, and who owns it?