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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.

Author

Tymoteusz Olszewski

Tymoteusz Olszewski

Report cover: Your organisation has implemented AI. What’s next?

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.

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.

Tymoteusz OlszewskiHead of AI

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