AI executives from Archer, Demandbase and Highspot and AWS reveal how they're tackling AI's biggest challenges—from securing data, managing regulatory changes and keeping humans in the loop.
Topics Include:
- Three AI leaders introduce their companies: Archer, Demandbase and Highspot's approaches to enterprise AI
- Demandbase's data strategy: Customer data stays isolated, shared data requires consent, public sources fuel training
- Geographic complexity: AI compliance varies dramatically between Germany, US, Canada, and California regulations
- HighSpot tackles sales bias: Granular questions replace generic assessments for more accurate rep evaluations
- SBI framework applied to AI: Specific behavioral observations create better, more actionable sales coaching
- AI transparency through citations: Timestamped evidence lets managers verify AI feedback and catch hallucinations
- Archer handles 20-30K monthly regulations: AI helps enterprises manage overwhelming compliance requirements at scale
- Two compliance types explained: Operational (common across companies) versus business-specific regulatory requirements
- EU AI Act adoption: US companies embracing European framework for responsible AI governance
- Human oversight becomes mandatory: Expert-in-the-loop reviews ensure AI decisions remain correctable and auditable
- The bigger AI risk: Companies face greater danger from AI inaction than AI adoption
- Agentic AI security challenges: Data layers must enforce permissions before AI access, not after
- AI agents need identity management: Same access controls apply whether human clicks or AI acts
- Human oversight in high stakes: Chief compliance officers demand transparency and correction capabilities
- Future challenge identified: 80% of enterprise data behind firewalls remains invisible to AI models
Participants:
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