AI strategy is a leadership problem before it is an engineering one. The teams that get returns from agents do not start with a model — they start with a decision about which work matters, what to build versus buy, and how to move from a contained pilot to production without betting the company on it.
These posts are written for the people who own that decision. They cover how to identify the few workflows where agents create real leverage, how to weigh in-house development against vendor platforms, and how to sequence investment so each phase pays for the next.
Topics include build-versus-buy frameworks, pilot-to-production sequencing, tying AI spend to measurable business outcomes, and the organizational questions — ownership, risk, and capability — that determine whether an agent program scales or stalls. The throughline is honest: where agents earn their keep, and where they do not.