AI Do’s and Don’ts for Managers: Bridging Strategy and Technology
summary
SEB shares concrete practice: a separate SEBX lab, a pilot “use‑case competition” that floods with ideas, and an incident where an AI credit model produced gender bias when under‑tested. Implements passwordless login across the bank, an eight‑module cyber academy, insider‑threat detection and data‑centre cooling automation, and an agreement with Nvidia‑style compute partners to scale. Emphasizes classifying models under the AI Act, enforcing data lineage traceability, and four pillars: value alignment, governance and trust, org readiness, scalable execution.
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chapters (23)
- 0:00Introduction
- 1:49Leaders facing AI adoption
- 3:34Machine learning to black-box AI
- 5:48Avoiding AI use case trap
- 7:39AI risks in credit processes
- 9:10AI in repetitive tasks
- 10:13Regulatory frameworks and governance
- 13:29Business value first for AI
- 15:18Legal compliance processes
- 17:27Scaling AI solutions
- 19:58AI model security risks
- 22:30Audits and AI Act compliance
- 24:23Cybersecurity training
- 26:57AI future of banking
- 28:02Four pillars for AI value
- 30:32Zero trust readiness
- 31:59Scaling AI challenges
- 33:03Answering correctly not quickly in AI projects
- 34:49AI outcomes ownership
- 36:15AI for document QA
- 37:18AI security requirements
- 38:46Transparency with customers
- 39:29Closing leadership message
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