Veriet advises executive teams and boards on AI strategy, then stays to build the operating model and the fluency that turn it into growth.
Book a conversation →AI strategy, Roadmap, Governance and policy, Agents, Operating model, Use-case discovery, Process reinvention, Automations, Fluency, Enterprise configuration
Strategy sets the direction. Capability grows the business. We work on both, from the decisions the board owns to the tools on every desk.
For executive teams and boards deciding where AI takes the business. We develop the strategy, design the operating model around it, and stay on as the interim owner of AI until the organisation names its own.
For the organisation that has to comply and the board that answers for it. AI policy, guardrails, and a reporting rhythm that lets directors govern AI rather than watch it.
Book a conversationFor organisations ready to put the strategy in people's hands. Each track sets the tools up around the team's actual work, trains people on their own tasks, and ends when the team ships use cases without us. The recommendation follows your strategy rather than a vendor.
We believe Claude is best in class for most organisations and business users today, and it is where most of our engagements run.
For Microsoft estates where the agreement and the residency rules have already settled the question. We make the seats you already pay for earn their keep.
For teams that grew up on ChatGPT, and most did, since it came first.
We generally work with organisations of one hundred to a thousand people where the CEO is in the room, a board is in place, and AI has no internal owner yet.
Situation
Capable teams and no shared account of what the tools could carry, so adoption ran ahead in some functions and stalled in others.
What we did
We ran education across chat, agentic coding, and the connected workspace, set the tooling up properly for the people who would use it, then pointed everything at a single hackathon day.
What changed
Teams left with use cases running against their own systems, and the executive group with a position on where AI belongs in the operating model.
Situation
The business wanted a path to an agentic organisation rather than another round of pilots.
What we did
We built the capability uplift strategy and the roadmap behind it, defined the customer segments and had them built into the unified data layer, then connected business users to that layer so they could develop use cases against real data on their own customers. Education and workshops ran with the executive team alongside the build.
What changed
The business moved from asking what AI could do to running a sequenced capability build, and a competitor analysis became a standing capability for agentic signal detection, with the people, process, and technology to watch the tripwires and a pre-agreed response when a critical event fires.
Situation
The firm wanted AI in the hands of the whole team rather than a contained pilot.
What we did
We set up and connected the enterprise deployment, worked through the firm's own workflows to define the use cases worth building first, then took the Australian team through fluency uplift: training on the tools, then a hackathon where those use cases got built.
What changed
Configured tooling, a defined use-case set, and a team that can build against it without us.
Situation
The company was relaunching for its twentieth year and wanted its operating model rebuilt for an AI world, not decorated with it.
What we did
We ran interviews across the business, then synthesised what we heard into the highest-value opportunities and a strategy sequenced over the following eight months.
What changed
The leadership team had a position to relaunch on and a defensible order for what gets rebuilt first.
Situation
The group acquires software companies and holds them, so capability compounds across the portfolio rather than resetting at exit. The constraint was fluency, not platform.
What we did
We ran the education programme, developed use cases with the teams who would own them, and closed with a hackathon where those use cases got built.
What changed
Teams across the group work with the tools directly, and the use cases they built are theirs to extend.
Alex spent a decade building AI and data products before moving to advisory, and has spent the last few years with executive teams working on exactly this: business strategy, AI strategy, governance, and the capability uplift that follows.
The product background shows in the work, which favours shipped outcomes over slideware. More at alextran.co (opens in a new tab).
In revenue first. Engineering ships more in a quarter, marketing runs campaigns it could not staff before, and the customer data sitting in your systems gets used. Costs fall too, because faster teams cost less to run. But cutting has a limit and getting better does not, so that is the side we work on.
Execution is, but 5 decisions sit with the leadership team: which tools are approved for which work, what company and customer data may leave the business, how agents are supervised and who can stop one, who owns redesigning how the work gets done, and how you will know it is working.
If your people use AI at work, you already have one; it's just unwritten and ungoverned. The governance engagement replaces it with one the board has actually approved.
Yes, as advisers first. We work out where agents and automation belong in the operating model and how the board keeps them supervised, and we can stay involved when a build follows.
Licences arrived through the Microsoft agreement before anyone designed the ways of working around them. The Copilot track fixes the second half.
It starts with a conversation, moves to a readiness assessment across your executives, and lands a plan the board can fund. Delivery runs through Advisory, Governance, and Fluency. You can start at training alone, though most clients find it surfaces strategy questions, which is why the engagement widens.