One of the biggest misconceptions in enterprise AI today is that better models automatically lead to better outcomes. During my conversation with Ann Maya at Boomi World Tour London on The Ravit Show, we discussed why many organizations are still struggling to move AI initiatives from experimentation into production despite significant investments in technology.
What stood out to me was her perspective on AI readiness.
Most companies focus heavily on models and tools, but the real differentiators are governance, context, trust, and the ability to connect knowledge across the organization. As AI agents become more common in enterprise environments, these foundational capabilities become even more important.
We also explored:
• Why bigger models don't always produce better business outcomes
• The growing importance of agentic workflows
• How organizations should think about enterprise governance in the AI era
• Why context may become more valuable than the model itself
• The gap between AI ambition and AI execution
This was a thoughtful discussion on where enterprise AI is heading and what leaders should prioritize over the next few years.
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