Perspectives on AI, data strategy, and the future of enterprise intelligence.
Five infrastructure controls that make agentic AI safe to deploy in production. Most teams already use them for humans; the gap is applying them consistently to non-human actors.
A startup lost its entire database and every backup in nine seconds. The AI agent was the proximate cause. The architecture was the root cause. What needs to be true before you deploy.
Compliance is a legal status. Privacy is a property of the system. The two are not the same, and the distance between them is where the next decade of incidents will live.
We train neural networks as frequentists but we want them to behave as Bayesians. Almost every interesting failure mode of modern AI lives in that gap.
Writing code used to be the bottleneck and the bill. With AI-assisted development, the marginal cost has collapsed. Most enterprise contracts haven’t caught up.
In banking, healthcare, telecom, and the public sector, AI migrations fail in a recognisable way. The legal constraint is not a final review, it is a design input.
87% of AI projects never make it to production. The bottleneck is almost never the algorithm, it's the data. Here's what separates success from stall.
