Build vs Buy in the AI Era
The build-versus-buy question in enterprise AI is rarely a clean binary. It is an architectural decision shaped by data gravity, organizational capability, risk tolerance, and the speed at which the underlying technology is shifting. This piece examines the structural factors that should drive that decision—and the ones that quietly derail it.
The Real Cost of Enterprise AI
The economics of enterprise AI extend far beyond model development. Infrastructure scale, data pipelines, operational discipline, and governance all shape the long-term cost profile of intelligent systems once they enter production environments.
Running AI Reliably in Production
Enterprise AI initiatives rarely fail because of models. Reliability challenges emerge once intelligent systems interact with live data, infrastructure, and operational workflows. Sustainable AI adoption depends on observability, disciplined data pipelines, controlled change management, and clear operational ownership.
Control Before AI Autonomy
AI autonomy shifts enterprise systems from analysis to action. Sustainable scale depends on identity discipline, enforceable policy boundaries, and real-time visibility—foundations that must be designed before intelligent systems are allowed to operate independently.