Your organisation has implemented AI. What’s next?

9 AI governance pillars you cannot overlook

What organisations must do after deploying an AI system, so that the technology becomes reliable, safe & compliant. This guide was created to help companies move from post-deployment chaos to full control and optimisation, using a model of 9 key AI governance pillars.

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  • Tymoteusz Olszewski Head of AI

    We created this report to cut through speculation and provide clarity. For EU banks, governing AI is a six-figure annual OPEX per high-risk model – the price of trust, safety, and regulatory permission to scale. What I would suggest is to treat governance as core infrastructure for AI at scale, not an afterthought.

What’s inside

    • The new reality after implementing AI

      Why the average ROI from AI initiatives is only 5.9%, and how weak governance drives hidden costs – like an additional $670,000 per security breach.

    • 9 key pillars of mature AI governance

      A complete model across three stages:

      • STAGE I: building foundations and taking control,
      • STAGE II: implementing strategy and managing risk,
      • STAGE III: optimising and verifying.

    • Actionable steps for each governance pillar

      From organisational accountability frameworks, data governance, ethical culture, MLOps and monitoring, to ROI tracking and automation.

    • The risks of neglecting governance

      Unapproved AI tools, loss of model accuracy, flawed decision-making, compliance penalties of up to 7% of global turnover.

    • Expert advice

      Practical insights on governance as code, data quality safeguards, automation opportunities, and how to accelerate innovation safely.

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