What happens when your AI ambitions collide with the reality of your infrastructure?

Across boardrooms everywhere, agentic AI has quickly moved from experimental projects to strategic priority. The excitement is easy to understand. Business leaders see opportunities to automate workflows, improve decision-making, and increase productivity. Yet behind the headlines and product announcements sits a less visible challenge that many organizations are only beginning to understand.

In this episode of Tech Talks Daily, I speak with Abby Strong, Chief Market Officer and Chief Customer Officer at Cribl, about the growing gap between AI ambition and operational readiness. Drawing on new research conducted with Harvard Business Review Analytic Services, Abby shares why so many organizations are struggling to move AI initiatives from pilot projects into production environments.

The findings paint a fascinating picture. While almost every business leader surveyed views agentic AI as strategically important, only a small percentage believe they currently have both the strategy and infrastructure required to support it. At the heart of the challenge is data. As AI agents interact with systems, applications, and services, telemetry volumes are increasing at rates that many organizations never anticipated. In some cases, data volumes have doubled or tripled, creating unexpected infrastructure costs and operational complexity.

Abby explains why telemetry, observability, and data management have become central to AI success. We discuss why AI systems are only as effective as the quality, accessibility, and context of the data available to them. She also shares real-world examples of how organizations are wrestling with growing infrastructure demands, rising costs, governance requirements, and the challenge of proving meaningful return on investment.

Our conversation also examines the growing importance of visibility into AI activity. As enterprises deploy large language models and AI agents across their environments, security and observability teams are facing entirely new questions around monitoring, governance, compliance, and cost control. How do you establish a baseline when the technology itself is evolving so quickly? How do you maintain trust when AI systems generate vast numbers of automated queries and interactions?

Abby offers a balanced perspective on what comes next. Rather than replacing existing systems overnight, many organizations are adding AI capabilities onto current workflows while gradually rethinking how work gets done. The result is a period of transition where businesses must support today's operations while preparing for a future that looks very different.

If you're trying to understand why infrastructure readiness may become one of the biggest factors in AI success, this conversation provides valuable context. Are organizations focusing too much on AI models and not enough on the data foundations that support them? And what happens when the cost of AI adoption extends far beyond the AI tools themselves?