Most small and mid-size energy companies know AI matters. What they do not have is a clear, safe way to start. The instinct is to look for a tool and buy it. That is backwards. A good first AI project does not start with a tool. It starts with a number you want to move, and it stays small on purpose.
Start with a number, not a tool
Before anyone talks about models or platforms, answer one question: what would you actually change if this worked? Hours saved per week. Faster response times. Fewer errors. A cost you want to cut. If you cannot name the number, it is not a project yet, it is a science experiment. The number is what keeps the work honest and tells you, at the end, whether it was worth it.
Pick one painful, repetitive process
The best first project is boring on purpose. Look for work that is high-volume, rule-heavy, and done the same way every time. That is where AI is strong and where a mistake is easy to spot and fix. Avoid, for now, anything that is rare, high-stakes, or requires deep human judgment.
A quick test: if you can explain the task to a new hire in a few minutes and it happens dozens of times a week, it is probably a good candidate.
De-risk it with a pilot
You do not commit to a year-long rollout to find out if something works. You run a short pilot. A sensible shape:
- Scope it to one process and one team.
- Build a working version in weeks, not months.
- Run it alongside the current way of doing things, so nothing breaks.
- Compare the results against the number you picked at the start.
A pilot turns a big, scary "are we doing AI" decision into a small, cheap "did this one thing work" decision. If it did, you expand. If it did not, you learned something for a fraction of the cost.
What to measure
Keep it tied to the business, not the technology. Nobody cares how clever the model is if the number did not move. Track the before-and-after on the metric you chose, plus a couple of practical ones: how much manual time disappeared, and how often a person still had to step in. Those tell you whether it is really working and where it needs another pass.
Common mistakes to avoid
- Boiling the ocean. Trying to transform everything at once instead of proving one thing first.
- Buying before scoping. Locking into a platform before you know the problem.
- No owner. A pilot with no one responsible for the number quietly dies.
- Waiting for perfect data. You clean data as part of the project, not before it.
Done this way, a first AI project is low-risk and fast. You pick one number, prove you can move it, and earn the confidence to do the next one. That is how a small energy company adopts AI without betting the business on it.