A structured, board-ready assessment that maps how your processes actually work versus how they are documented, surfaces the invisible human judgement keeping operations running, and produces a prioritised roadmap for where AI should - and should not - operate.
Most AI readiness work is a capability inventory. This is not. The Diagnostic is an organisational reality check, built on thirty years of operating inside regulated financial services and public-sector infrastructure. Typical findings include:
The output is not a slide deck. It is evidence the organisation can act on, and that an AI-sceptical board member can read without needing to be sold to.
The Diagnostic operates across two structural layers of AI readiness. The first runs before deployment, mapping operational reality and producing a prioritised roadmap for where AI should and should not be introduced. The second runs at the moment of execution, providing the evidentiary record of what was authorised and what actually committed. Both are required. Neither answers the other's question.
Before deployment.
The AI Readiness Diagnostic maps how processes actually run, surfaces invisible human judgement, identifies where AI creates value versus where AI introduces risk, and produces a prioritised roadmap that leadership can act on.
At the moment of execution.
ARBITR provides deterministic, evidentiary proof that what was authorised is what actually executed, at the moment it executed. The output is a portable, audit-suitable evidence bundle.
Senior decision-makers weighing AI deployment in environments where "it will probably work" is not a sufficient answer. CTOs, CROs, CDOs, heads of compliance, heads of fraud, and heads of technology inside banks, insurers, payment institutions, and financial infrastructure providers. Board members and NEDs responsible for technology risk and governance. Enterprise technology leaders in high-trust environments beyond financial services - government, healthcare, critical infrastructure. And serious operators in non-regulated sectors who recognise that the questions are the same even when the regulator is not watching.
KPMG surveyed fifty Swiss financial institutions in January 2026. The findings read less like an industry report and more like a diagnostic case file: 84% recognise AI as a strategic priority, only 8% have a defined strategy. 84% identify fraud and transaction monitoring as the highest AI potential, only 38% are developing solutions - a 46-point gap between ambition and action. 55% cite cultural resistance as the biggest barrier. 53% lack clear use cases. Only 18% have a dedicated AI risk management framework. 48% report joint or no accountability for AI risk. 46% offer no AI training at all. 51% rely on vendors for AI capability.
McKinsey tested 25 organisational factors against AI success. Only one predicted measurable returns: workflow redesign before deployment. 80% of organisations that saw measurable returns had redesigned their workflows first. 21% of all organisations actually did this. Roughly four out of five are layering AI onto processes they have never properly examined.
The Diagnostic is built around that finding.
Engagement length is typically four to eight weeks for the diagnostic phase, extending to twelve weeks or more for governance design and implementation support. Delivery is remote with on-site sessions as required. The output is board-ready: a findings document, a prioritised roadmap, and an accountability mapping that identifies where decision authority currently sits and where it needs to move. The Diagnostic is vendor-neutral - independent of any AI platform, cloud provider, or tooling choice.
Thirty minutes. No pitch. No obligation. An honest assessment of where you stand.
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