"We want to accelerate AI." You hear that in almost every boardroom today. What you hear far less often is the question that should come before it: is our organization actually ready for the way of working AI requires?
Cloud was the first test. Many organizations treated cloud mainly as an infrastructure migration — move the VMs, deal with the rest later. The operating model, decision-making, ownership questions: those stayed largely untouched. On-premise governance in someone else's datacenter.
AI now sits on top of that. Not as a new, isolated challenge, but as an accelerant for that exact unfinished transition. Buying an AI assistant is no more "AI adoption" than moving a workload to the cloud was "cloud adoption." The question that actually matters is:
Can an organization still struggling with its operating model already adopt AI responsibly, at scale?
Our answer: yes, for limited and controlled applications. Not automatically for AI at scale — and certainly not for agentic AI that executes decisions on its own. In between sits a concrete construct: the AI Target Operating Model (AI TOM). Here's how we build it, step by step, following our methodology.
Shatter — putting the assumptions on the table
We don't start with a technology choice. We start by asking which organizational friction AI would amplify today. Where do decisions get stuck? What knowledge is trapped in people's heads and inboxes instead of in reusable sources? Where are people already using uncontrolled AI tools, and why is the official alternative slower or less usable?
That last one is often the sharpest signal. Shadow AI is rarely a discipline problem. It's the predictable result of a gap between what employees need and what the organization safely offers. Making that gap visible — without immediately closing it off with more committees — is the first move.
Rewire — building the foundations of the AI TOM
An AI TOM isn't an entirely new model sitting next to your existing organization. It's an extension and a heavier version of a modern cloud foundation: identity and access control, secure platforms, automated guardrails, shared ownership. AI adds specific, heavier questions on top of that:
- Who may use which models, data, and agents — not just who may provision resources.
- Who owns an AI decision or agent action — not just who is the workload owner.
- How do we weigh model cost, quality, and human review against each other — not just how we control cloud spend.
The governance shift that comes with this might be the most important one: from asking for approval to offering safe capacity. A central team that has to assess every use case inevitably becomes a bottleneck. Full freedom without shared guardrails creates just as much risk. The mature form sits in between — a small team builds reusable platform capabilities and guardrails; teams use them independently and carry responsibility for their own application.
Activate — from diagnosis to movement
This is where it gets stuck most often in practice. Teams recognize the symptoms — slow delivery, fragmented tools, unclear costs — without anything changing, because the existing organization still feels safer in the short term than the transformation does. Activating means: not tackling everything at once, but addressing exactly the neurons that need intervention, and leaving the rest untouched.
In practice, that's why we never start with an advisory document. We start with a diagnosis an organization can go through and recognize itself in.
The AI Signal Scan: the first concrete instrument
That's exactly what the AI Signal Scan is: not a separate tool detached from this methodology, but its first practical application. Ten questions across five domains — direction & decision-making, knowledge & data, process & platform, assurance & accountability, behavior & adoption — that together reveal where AI would accelerate value today, and where it would mostly amplify existing noise.
The scan doesn't produce an artificially precise success percentage. It produces a friction map: which domains are strong, which are under pressure, and which combinations of weak domains reinforce each other. And for every signal it detects, a concrete first move, following the same rhythm: Shatter, Rewire, Activate.
In short, it isn't a marketing tool. It's the smallest, most concrete way to show how we work, before a single advisory conversation has even taken place.
Want to see where AI would accelerate value in your organization today — or where it would amplify the noise that's already there?
Take the AI Signal Scan →10 questions · 2 minutes · Free · Or book a call