Inside the Agentic Enterprise

For a few pounds per million tokens you have the same frontier models as your fiercest competitor, improving at the same pace, on the same day they ship.

Which means the differentiator has moved from the model doing the automating to what it automates: your organisation. Every factory could buy electricity, and the winners were the ones that rewired around it.

Elsewhen blogWhy the model is not your moatModels converge and keep getting cheaper. The moat is the harness around them and the context it holds.Read the piece →

The hard part is everything around the model: the business context the AI works in, the workflows it runs, the coordination of agents at scale, how your people teach it and the interfaces through which the work gets done. That layer, the one that joins a general capability to a particular business, is the only part nobody can sell you off the shelf.

Deployment is a settled question. Gaining from it is not. In McKinsey’s latest State of AI survey, nearly 90% of organisations use AI regularly, 37% can attribute any profit impact to it and 6% are earning material value. That 6% run the same models as everyone else. What sets them apart is that they run AI as one programme for the whole business, held to the P&L from the start. They decide where it will pay before they build, rebuild those workflows end to end along with the wiring that carries work between their systems and put as much into governance and their people as into the technology.

So this report is about the wiring. How your operating model becomes software you own, and what has to be built before agents can run anything end to end.

The dominant way most organisations consume AI has a ceiling, and it is lower than it looks.

Chat is often a poor interface for real work: it puts the entire burden of phrasing, context and follow-through on the person, one conversation at a time. And while putting a chatbot on every desk may feel like progress, the gain that matters arrives when the workflows themselves are redesigned around what agents can run.

Meanwhile tokenmaxxing, pouring tokens across the organisation and treating consumption as a proxy for value, is proving to be a measure of activity and little else.

Elsewhen blogThe end of tokenmaxxingWhat happens when boards start asking what all those tokens bought.Read the piece →

The market has started to price this in. Gartner counts roughly 130 genuine agentic AI vendors among the thousands claiming the label, and has a name for the rest: agent washing, the rebranding of assistants, RPA and chatbots as agents without the underlying capability. Buying activity and buying capability look identical on a slide. They diverge in production.

Done right, the prize is the cost base itself, put to work. When agents run the workflows, operating expenditure stops being a fixed cost and becomes elastic capacity: the same spend produces more output each quarter as the system learns, and your people move up the value chain.

If the gains come from redesigning the work, it pays to be precise about what the work is. All work is a series of steps to reach a goal, and every one of those series can now be codified.

Onboarding a patient, approving an invoice, closing the books, migrating a portfolio. Each is a workflow: a trigger, a set of steps, a point where someone decides, an outcome written back. An organisation is just a collection of them, thousands, layered and interlocking. That collection is your operating model.

For decades it lived in people's heads, in policy PDFs and in systems of record, because writing software to run it was slow and expensive. That constraint has gone. Coding agents now generate, test and ship software at a pace that was impossible 18 months ago, so any workflow can be codified and the operating model itself becomes something you build and own.

Elsewhen blogFind your first agentic opportunityThe workshop we run to pick which of your workflows to codify first, and the five tests we put each one through.Read the piece →

Codifying a workflow means choosing who runs each step. The human in the loop belongs wherever a mistake is costly, regulated or hard to undo, and everywhere else the agent can act alone.

Whatever the use case, the same system sits underneath: the surfaces your people use, the agents that do the work, the coordination that keeps it honest, the context it reads, the systems it writes back to.

A single agent on a laptop is a demo. An enterprise running thousands of agent executions a day across finance, compliance and customer service is an operation. The same shift happened with cloud computing: running one server is trivial, running a fleet is a discipline of its own.

We call the reusable core under all of this the Productivity Platform. It is built once, owned by you and reused across every use case, so each new agent ships faster and cheaper than the last. It is model-agnostic, so as new models arrive they plug straight in and you are never tied to one vendor. Because you own the core, its value compounds with every use case.

Custom software is now cheap to build and yours to keep. Work nobody would have automated two years ago suddenly pays for itself.

The jobs coming to us are the ones where the money already sits: what the operation costs to run, what the licences cost to keep and the revenue these models make buildable for the first time.