Where to start with AI in your company
Most AI projects don't fail because of the technology. They fail because they start in the wrong place. Three questions to answer before writing a line of code.
The question we hear most often isn't which model should we use, but where should we start. That's the right question: the gap between a pilot that stays a demo and one that reaches production is rarely technical.
1. Which process actually hurts?
Before thinking about AI, look at the operation. The best first use cases share three traits: they happen many times a day, they consume time from skilled people, and they produce an outcome you can measure.
If a process runs three times a month, automating it is an interesting exercise, but it won't move the business.
2. Is the data available?
This is the layer that decides whether AI works in production or stays in the demo. You don't need a perfect data lake, but you do need to know where the information lives, in what format, and who maintains it.
An excellent use case with unreachable data delivers less than a modest use case with data at hand.
3. Who will use it every day?
Adoption isn't a training problem you solve afterwards: it's a design decision. If the solution forces people out of their usual workflow, usage drops within weeks.
That's why we build on top of existing workflows instead of creating parallel tools that compete with the systems the team already uses.
The starting point
A good first implementation isn't the most ambitious one — it's the one that leaves evidence: a number before and a number after. With that result in hand, the second conversation about AI inside the organization is a completely different one.