For all the noise, the value from AI is remarkably concentrated. It shows up where the technology is aimed squarely at a core driver of the business, and where the surrounding workflow is redesigned to let it work. Everywhere else, it stalls in the pilot stage and quietly disappears.
The pattern separating value from theatre is consistent. Isolated experiments, however clever, rarely reach the P&L. Impact comes from picking the handful of use cases that genuinely move revenue, cost or risk, and committing to them properly, with the data, the integrations and the human oversight that real deployment demands.
It also means being honest about where AI won't create value, at least not yet. Not every process is worth automating, and forcing it produces fragile systems that cost more attention than they save. Knowing what to leave alone is as much a part of the work as knowing what to build.
Our bias is toward the few things that compound. We would rather ship one automation that reliably runs a revenue-critical workflow than a dozen demos that impress in a meeting and break in production.