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Host Zoe Cunningham speaks with Macs Dickinson, Director of Engineering at LHV Bank on the challenge many technology leaders are facing as AI tools become embedded in everyday work. While generative AI can accelerate delivery, it can also create a hidden risk: knowledge debt. The loss of deep system understanding when teams rely too heavily on generated outputs.
Drawing on his experience in highly regulated industries including banking and gambling, Macs explains why “fail fast” does not translate to environments where reliability and accountability are critical. He shares how his teams are adopting machine learning and AI agents safely, starting with narrow internal use cases, building strong guardrails, and ensuring engineers retain ownership of what they ship.
Discover:
- Why AI can speed up delivery while increasing long term risk
- Why regulated industries require a different mindset for AI adoption
- How machine learning models can be governed through monitoring and human review
- Why generative AI is harder to test than traditional software
- How starting small reduces risk and builds organisational learning
- Why ownership and understanding still matter in AI assisted engineering
