Democratizing Observability with AI

What happens when engineering teams can finally see the business impact of every technical decision they make?

In this episode of Tech Talks Daily, I sat down with Chris Cooney, Director of Advocacy at Coralogix, to unpack why observability is no longer just an engineering concern, but a strategic lever for the entire business. Chris joined me fresh from AWS re:Invent, where he had challenged a long-standing assumption that technical signals such as CPU usage, error rates, and logs belong only in engineering silos. Instead, he argues that these signals, when enriched and interpreted correctly, can tell a much more powerful story about revenue loss, customer experience, and competitive advantage.

We explored Coralogix's Observability Maturity Model, a four-stage framework that guides organizations from basic telemetry collection to business-level decision-making. Chris shared that many teams stall on measuring engineering health without connecting that data to customer impact or financial outcomes. The conversation became especially tangible when he explained how a single failed checkout log can be enriched with product and pricing data to reveal a bug costing thousands of dollars per day. That shift, from "fix this tech debt" to "fix this issue draining revenue," fundamentally changes how priorities are set across teams.

Chris also introduced Olly, Coralogix's AI observability agent, and explained why it is designed as an agent rather than a simple assistant. We discussed how Olly can autonomously investigate issues across logs, metrics, traces, alerts, and dashboards, enabling anyone in the organization to ask questions in plain English and receive actionable insights. From diagnosing a complex SQL injection attempt to surfacing downstream customer impact, Olly represents a move toward democratizing observability data far beyond engineering teams.

Throughout our discussion, a clear theme emerged. When technical health is directly tied to business health, observability stops being a cost center and becomes a competitive advantage. By giving autonomous engineering teams visibility into real-world impact, organizations can make faster, better decisions, foster innovation, and avoid the blind spots that have cost even well-known brands millions.

So if observability still feels like a necessary expense rather than a growth driver in your organization, what would change if every technical signal could be translated into a clear business impact, and who would make better decisions if they could finally see that connection?

Useful LInks

• Connect with Chris Cooney

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