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Build, scale and manage your organisation’s learning

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Featured Content

Featured Content

Implementing AI in your Organisation

In this video, Elizabeth explains how organisations can successfully adopt AI and data science by fostering a data-driven culture and strategically implementing AI projects.

Blockchain and Smart Contracts

In the first video of this video series, James explains the concept of blockchain along with its benefits.

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10 Takeaways From the EU AI Act

10 Takeaways From the EU AI Act

Pete Hannam

Data and AI Systems Architect

You've made it to the end - here are 10 key things to remember.

You've made it to the end - here are 10 key things to remember.

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10 Takeaways From the EU AI Act

1 min 47 secs

Key learning objectives:

  • Understand the 10 key takeaways from the EU AI Act

Overview:

To prepare for the EU AI Act, treat compliance as a cross-functional leadership priority rather than a technical task. Identify your role as a provider or deployer, classify systems based on specific use cases, and integrate requirements into initial designs to avoid costly retrofitting. Maintain rigorous documentation, leverage existing GDPR frameworks, and inventory systems immediately to ensure all "trustworthy AI" practices align with legal and ethical standards.

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Summary
What do I need to remember about the EU AI Act?
  1. If your AI might touch EU citizens, plan for compliance
  2. Appoint clear internal accountability
  3. Classification is determined by how you use the AI
  4. Integrate requirements into the initial architecture
  5. Identify if you are a provider or deployer for each system
  6. Maintain a paper trail of why you classified a system a certain way
  7. Integrate AI compliance into existing frameworks
  8. Use ‘trustworthy AI’ as a brand differentiator
  9. Inventory all data-driven systems and screen them against prohibited AI practices
  10. Train everyone to ensure requirements are understood

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Pete Hannam

Pete Hannam

Pete Hannam has over twenty years’ experience building large-scale data platforms, including those that now underpin modern AI systems. His work has ranged from national security infrastructure processing hundreds of billions of records to global enterprise platforms supporting tens of thousands of users. He has architected AI-ready systems for government, consultancies, and enterprises, with a strong focus on governance, security, and regulatory compliance. His work emphasises that successful AI deployment depends not only on technology, but on understanding the organisational, technical, and regulatory context that enables AI initiatives to deliver real value.

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