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.
Dromologue provides a mechanism, a set of practices and software, that enables leaders of teams or whole organisations to test the changes to their operating model that AI and other forces require. It collects data from teams as they build, gives guidance on the effective practice the organisation requires, and measures the impact of those practices on team operations and overall value delivery.
There are many ways to start: defining your strategy and how AI reshapes it, tentatively describing what you think “good” may look like in an evolved operating model, or just selecting use cases that enable you to test your approach to using AI.
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 platform for experimenting with the operating model
Each release of software or product should meet two criteria. Can we show it added value? What did it tell us about how our way of operating needs to change? We provide content to help you refine your operating model, and a software platform that guides your team, monitors their progress and lets you test changes to how you work at team scale before you take them wider.
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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 workshops →
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.