The AI hype has been loud for three years, but most leaders still tell me that the real work starts after the demo. That was the starting point for my conversation with the co-founder of AI Realized, a community built to help companies go from pilots to production with less noise and more results.
Christina has a calm, practical way of explaining why progress has accelerated from a tiny fraction of companies in production to roughly one in five this year, and why many of the remaining blockers have little to do with model choice and everything to do with people, policy, and permission to ship.
We’re talking about the messy middle between a proof of concept and a live service that customers can trust. According to Christina, the most complex problems are of an organizational nature. Teams need further training, guardrails and clear implementation guidelines to ensure effective execution. Legal and brand risks cause hesitation. The boards want more substantial evidence and better controls. That is where leadership is expressed in a very human way.
The skill she most often hears from successful program leaders is humility. No one knows everything here, and the leaders who admit that, invite challenge, and continue to learn are the ones who discover value without creating chaos. I loved her point that cross-organizational leadership is quickly becoming the hidden superpower as AI connects systems and workflows that were previously in separate silos.
We are also looking forward to the AI Realized Summit 2025, scheduled for November 5 in San Francisco. The number of attendees is intentionally limited to 500 to maintain high-quality conversations and true networking. Expect Fortune 2000 use cases across multiple industries, a healthy mix of predictive and generative work, and hands-on conversations about small language models, multi-model strategies, and running models within your security perimeter.
Eric Siegel will give a keynote on combining predictive analytics with generative techniques, and you’ll hear from executives at companies like Amazon, Audible, Red Hat, and Zscaler. Christina highlights an example from Fandom that combines predictive ad targeting with generative tools to increase brand safety and suitability, a trend I expect to repeat throughout the day.
If you run AI programs and need fewer slogans and more evidence, this episode will feel like a deep breath. We explore how to move faster while staying responsible, why smaller and multi-model setups are gaining traction, and how to build confidence with your board without overpromising.
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