Summary
Panelists explained how they are adopting agentic AI internally before selling it: tie use cases to financial value, define operating models, and centralize safety, privacy, and guardrails. They stressed matching models to workflow risk and ROI—using frontier models only for high-intelligence security tasks while cheaper deterministic or specialized systems handle simpler work.
Key Topics
5 key topics from Steve Lucas, Arun Chandra, Nidhi Aggarwal and Tomas Statius at HumanX Amsterdam 2026. Thicker branches were mentioned more often in the talk.
An Agentic Backbone
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Key Takeaways
- Define financial outcomes before deploying agents across functions.
- Centralize safeguards, privacy, and governance while enabling edge teams.
- Match model cost and accuracy to each workflow step.
- Replace human-in-loop approvals with human-over-loop safeguards for machine-speed attacks.
- Test agent safeguards internally before selling customer solutions.
“it's not just can you do it with AI, it's should you do it with AI?”
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