When AI Actually Makes Sense for Your Business
Most AI initiatives fail before a model is ever trained — because nobody priced the decision the model was supposed to improve.
Start with the decision, not the data
Every successful AI deployment we've shipped began the same way: someone named a specific decision the business makes thousands of times, badly or expensively. Which truck takes this load. Whether this referral is urgent. When this turbine needs a technician. If you can't name the decision, you don't have an AI opportunity — you have an AI aspiration.
The inverse is also true. Companies sitting on modest data but a clearly priced decision routinely outperform companies with petabytes and no target. Data volume is the most overrated input in the entire conversation.
The arithmetic nobody runs
Take the decision's frequency, multiply by the cost of getting it wrong, and multiply by how often humans currently get it wrong. That number is your ceiling. If it's smaller than the cost of building and operating the system, stop. We end roughly a third of our AI assessments with a recommendation not to build — and those clients come back, because the next opportunity we bring them clears the bar.
If you can't name the decision, you don't have an AI opportunity — you have an AI aspiration.
Keep judgment human
The deployments that survive contact with reality almost never remove people from the loop. They remove clerical work from around the judgment. A dispatcher who approves AI-proposed assignments in one click is faster and happier; a dispatcher replaced by an opaque allocator is a resignation letter and a rollback. Design for the approval, not the automation.
If this sounds like a conversation your team is having, we should have it together.