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PRACTICE · 02

AI and Transformation

We turn AI into a measurable corporate system with governance and adoption built in — not a demo that stops at the pilot.

AI and TransformationPRACTICE
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The real problem organisations have with AI is not model selection. It is this: which business decision are we trying to improve, who will use it, where does the data come from, what happens when it is wrong, and how will we measure success? Every AI initiative built without answering those five questions dies at the pilot stage.

The unit of work in this practice is the decision, not the model. Any initiative that starts without asking where AI will sit, who owns the decision and who carries the cost of an error stays stuck at the pilot stage, however well it is built technically. So the first deliverable is not a model but a decision inventory: which decisions repeat, which are data-driven, and which must keep a human in the loop.

We do not treat governance and adoption as separate from technical setup. Usage policy, risk classification and human oversight rules are written alongside the pilot, and role-based training is planned on the same calendar. What stops scaling is usually not technology but the absence of those two layers.

Key takeaways

  • An AI project starts with choosing a decision, not a model.
  • Governance is not a brake; it is what makes scaling possible.
  • Without role-based training, adoption typically stays below 20 percent.
  • One measurable win in the first 90 days is the strongest argument for the next budget.
02SERVICES

What sits under
this practice?

03SCOPE

Working topics
in this field.

Use case prioritisationData readiness assessmentArchitecture and toolingPilot designScaling planUsage policyRisk classificationHuman oversight rulesData privacy and GDPR/KVKKAudit and loggingMaturity assessmentUse case portfolioValue–effort matrixBudget and resourcingQuarterly milestonesExecutive awareness programmeTeam application workshopsPrompt and workflow trainingSafe usage trainingAssessment and certificationProcess mappingAutomation opportunity analysisWorkflow designIntegrationPerformance monitoring
05RELATED INSIGHTS

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07FAQ

Frequently asked questions

Where should we start with AI transformation?
Not with tool selection but with a decision inventory. List the decisions in your organisation that repeat often, take time and carry a high cost of error. Choose your first pilot from the ones with accessible data and measurable impact.
Our data is not ready — can we still start?
Yes. Waiting for data readiness means never starting in most organisations. The right approach is to prepare the minimum data needed for one chosen use case and expand during scaling.
Is AI governance necessary for smaller companies?
Necessary, but it does not have to be heavy. A 3–5 page usage policy, a risk classification and a human oversight rule is a sufficient start for most mid-sized organisations.
How do you handle data privacy?
In use case design we evaluate data minimisation, anonymisation and local processing from the outset. We work with your legal team and put the decision and its rationale in writing.
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