Nobody yet knows how AI will change the way your firm operates, or the industry it competes in. Preparing for that change has to start now.
What agents can do changes every quarter, so nobody can draw in advance the operating model a firm needs to get the full value from them. A firm finds it by trying changes on its own work and keeping those whose value it can measure. Dromologue provides the mechanism for doing that: a method and software for experimenting with the operating model in every part of the business, from how teams are organised and funded, through how they build and use data, to how risk, security and ethics are assured. The leader of one team and the leader of a whole organisation use it the same way, to test what a change to the operating model would do before making it and to measure what it did afterwards.
Five problems leaders bring us
- “We don’t know which use cases to pick, or how to run them once they start.”
- “We can’t show the value of the use cases we already have.”
- “We don’t know how to shape the AI team so that governance, support and speed stop pulling against each other.”
- “We’re using agents, and nobody can tell us what that means for risk, compliance and security next year.”
- “AI is changing how we operate, and we have no safe way to learn and adjust as it does.”
Each of these sits inside a leader’s own authority. None of them needs a board decision to start.
A method and software for experimenting with the operating model
Each experiment is a release. Before it, the sponsor sets a target on four measures: value per cost, days from backlog to production, the share of obligations enforced by a check, and the share of the team’s practices that hold. The team makes the change inside its own build, and the record then shows whether the targets moved. A change too large or too risky to try for real runs on a copy of the record first, so a leader sees what it would cost and return before committing to it.
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The discipline skills
Nine disciplines written as Agent Skills, with 106 practices each checked against the team’s own records, running inside the AI tool the team already uses; they have run in Claude, Codex and Hermes. Agents take the steps that need judgement, under a program that limits what each may do.
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The digital twin
Every release updates a record of how the firm works: who owns what, who decided what, what the work cost and returned, and which practices hold. Each executive reads the part that is theirs, and none of it leaves the firm.
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The simulator
A proposed change runs through the twin before the firm makes it. Given a policy such as letting product owners decide releases under £50,000, it shows what the policy does to speed, cost and control.
- Organise
- Structure · Talent · Finance
- Build
- Architecture · Engineering · Data
- Assure
- Risk · Security · Ethics
Start with one team, or with the whole organisation
We don’t sell a transformation programme as the first step. The cost of experimenting has never been lower, which makes the cost of standing still higher than it has ever been. For the leader of a team, the first engagement is small, advisory and bounded: one question of yours, answered on your own work.
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One use case, run differently
We run a single use case in a new way and set its value beside the others you already have, so the comparison is made on your own numbers.
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One blocker cleared
We take the specific problem holding your AI work back, whether that is a risk sign-off, an integration or a funding question, and work it through to a decision.
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A programme others understand
We bring risk, finance and the teams that own the systems into the work early, so the next use case meets less resistance than the last.
Each ends with evidence you can take to your own leadership: what changed, what it cost, and what is still not known.
For the leader of a whole organisation, the work starts above the teams: the AI strategy, a review of the practices the firm will run, a diagnostic of where each already happens, and the selection of the first teams and use cases. The advice that comes first →
Why take the risk on us
We have spent thirty years watching new technology forced into old organisations, at HSBC, Scotiabank, Schroders and Chef among others. The technology usually works. What fails is the organisation around it: the handoffs, the approvals, the question of who owns the risk when the system acts. That is the part we work on.