Business Transformation World Summit 2026: AI That Actually Works

Last week, our team headed to Miami for OPEX Business Transformation World Summit 2026. The energy was different this year...AI isn’t new anymore. Everyone wants to know: "how do we actually make this work?"

We came ready to answer that question.

Booth 21: Real Conversations, Zero Fluff 

We set up our booth with one goal: show how Fortune 500 ops teams actually deploy AI agents in production.  

The questions we heard most: 

• "How do we move beyond pilots?"
• "Why do our AI projects keep stalling?"
• "What does it actually take to scale this?"

The answers kept coming back to the same thing: you can't automate what you don't understand. Capture how work really happens, including context switching, manual fixes, workarounds. Context ensures your AI agents can learn from the best humans, not the documentation department. 

The Session: Building the AI-Ready Enterprise 

Our VP of Agentic AI Solutions, Dan Dantus, took the main stage alongside Peter Emmett, Director of Enterprise Data Operations at Cardinal Health, to talk about something most AI vendors avoid: why context comes first. 

The core message? AI agents are only as good as what they learn from. Train them on documentation, and they'll break in production. Train them on real human work patterns, and they'll actually deliver. 

Peter shared Cardinal Health's results: leveraging process intelligence from Skan AI, they reduced write-offs from $20M to $35K. Not a typo. That's what happens when AI learns from operational reality instead of outdated SOPs. 

Watch the full session here →  

The Panel: Experts Share Their Experience

“Dan Dantus, our VP of Agentic AI Solutions, joined Ashutosh Chaudhari from Cisive and Puneet Arora from Google to explore what separates enterprises that merely automate from those that truly learn and adapt. The panel cut through the automation hype to reveal the real architecture of self-optimizing operations. 

Key takeaways from the panel: 

• The three-agent framework: Scout, Guardrail, and Sentinel agents create self-healing systems—reinforcement learning at enterprise scale.

• Process intelligence eliminates guesswork: Capture real work telemetry and detect drift automatically, no manual workflow mapping required.

• Quality and speed aren't enemies: Crawl-walk-run frameworks with confidence thresholds let you move fast without breaking things.

• Your processes are competitive advantage: Operational signals become defensible IP—sameness doesn't win markets. 

The message was clear: self-optimization isn't about eliminating humans. It's about elevating them from transaction processors to workflow architects. Start small, prove value, then scale systematically with the same guardrails Tesla used before going autonomous. 

Watch the full panel discussion here → 

The Dinner: Talking About Real Strategies  

Beyond the booth and sessions, we hosted a dinner with transformation leaders navigating the same challenges: stuck pilots, uncertain ROI, and the pressure to show AI results now.

The conversations went deep. How do you identify which processes are ready for AI? How do you measure what matters before and after deployment? How do you build agents that work alongside people instead of replacing them?

These aren't theoretical questions anymore. AI is here now. But AI without operational context is just expensive guesswork.

What's Next  

Thank you to everyone who joined us—at the booth, in the session, and around the dinner table. The conversations about how AI learns from real human work made this event especially meaningful. 

If you missed us in Miami, both sessions will be available on-demand soon. In the meantime, we're back in San Jose turning conference energy into real transformation. 

Because the enterprise of the future isn't built on AI demos. It's built on AI that actually understands how work gets done. 

Want to see how agentic ideas become operational reality? Request a demo to explore how AI agents trained on real work patterns deliver in production.