Data, analytics and agentic transformation

The situation

An agency whose reporting describes what already happened, delivered after the moment to act on it has passed. Routine operational work absorbing senior time that should be going to client thinking. And a genuine appetite for AI arriving before the foundation underneath it was ready to carry any.

What I am doing

• Leading the whole chain rather than a layer of it, because the value that reaches the end is the product of every link and not the best one.
• The data foundation first. Ownership, lineage and a governed layer the analytics can actually stand on. This is the unglamorous part and skipping it is why the interesting part fails later.
• Then the analytics layer. Governed metrics and self service, so answering a client question is a query rather than a rebuild.
• Then agentic capability, sequenced onto something solid. Routine operational work moved to agents with authority boundaries and rollback defined before anything runs unsupervised.
• Capturing the baseline before each phase goes live, which is the only point at which it can be captured at all.

Why healthcare makes it harder

Regulated content raises the bar on every automated step. It is not enough for an agent to be right. The reasoning has to be explainable to a client, to a brand team, and eventually to a regulator, and it has to be reconstructable months later. That constraint shapes the architecture from the first decision rather than being bolted on at review.

It is also why the governance work runs alongside the build rather than after it. Nothing here goes live and gets governed afterwards.