Executives from ServiceNow, TwelveLabs, and AWS share unfiltered lessons on organizational change, data strategy, and best practices for scaling AI products worldwide.
Topics Include:
- Three panelists explore scaling and monetizing agentic AI products.
- ServiceNow built entirely new prompt engineering roles from scratch.
- Evaluations-first culture replaced traditional QA — a major mindset shift.
- TwelveLabs' "Tokens Never Sleep" initiative broke down team AI resistance.
- Unlimited token usage revealed how underused AI actually is.
- Rich: the competitive window for acting on AI is closing.
- ServiceNow's DART program governs customer data for evaluation only.
- Enterprise data governance is fundamentally different from consumer companies.
- Disorganized internal data breaks agentic systems — structure comes first.
- Your data, not your model, is your competitive advantage.
- Krish's surprise: enterprise governance made model training nearly impossible.
- Jae: powerful AI tools erode human opinions faster than expected.
- Rich: boardroom-to-shop-floor AI adoption is unlike any previous wave.
- Customers began favoring velocity after watching competitors win with it.
- ServiceNow shipped agents at 20% resolution — then iterated upward.
- Focus early agents on reversible, low-risk, two-way-door actions.
- AWS tracks internal AI adoption in a structured weekly mechanism.
- TwelveLabs' two models power sophisticated video RAG workflows at scale.
- A Hollywood studio cut full episodes to four minutes using them.
- Voice agent worked perfectly in demos — broke immediately in production.
- Customers now measure how fast a product is getting smarter.
- Future-proofing infrastructure is every product leader's top anxiety today.
- MCP and A2A enable message-passing — but deeper problems remain.
- Semantic mismatch, agent identity, and trace governance remain unsolved.
- Tiered autonomy, trust, and data foundations define who ultimately wins.
Participants:
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