Cribl, the AI Platform for Telemetry, has sponsored a new Harvard Business Review Analytic Services Pulse Survey. It observes that companies across every sector are pushing agentic AI from pilot to production, software that doesn’t just summarise and suggest, but autonomously plans, decides, and acts across live business systems.
The ambition is enormous but the infrastructure holding it up often isn’t. The survey demonstrates that the scale of the problem is impossible to ignore: 96% of senior leaders say agentic AI will be critical to their organisation within two years. Yet only 23% say they have the strategy and infrastructure in place to support it today.
The problem isn’t a lack of vision. It’s that most enterprises are trying to run a new generation of AI on legacy observability and security stacks that were never designed for it. When AI agents begin reasoning and executing across systems, at machine speed, across thousands of parallel tasks, telemetry volumes can multiply by a factor of ten or more. Dashboards built for humans typing in queries simply cannot keep up. For organisations at the leading edge of agent deployment, those legacy systems are not just struggling, they are already failing under the load.
“We passed $300M ARR in less than a year from $200M, and that growth is a direct reflection of what our customers are facing,” said Clint Sharp, co-founder & CEO at Cribl. “Data is growing at a 30% CAGR, budgets are not, and now AI agents are multiplying that problem by an order of magnitude. The infrastructure most enterprises built for the last decade simply wasn’t designed for the agentic workloads of the next one. This research validates what we see every day, organisations know they need to get ready, and the time to modernise that foundation is now, before the rest of the organisation catches up and demands they move at the speed of AI.”
The cost of infrastructure
The financial toll is already showing. Among organisations already deploying agents, 47% say infrastructure costs have exceeded expectations, and it is easy to see why. Cribl’s customers are being told they could eliminate entire tier-one security triage teams with agentic AI, only to discover they would need to quintuple their data infrastructure spend to support it. At $10M a year already, that value proposition disappears fast.
Without the right telemetry foundation, AI systems become black boxes: ungovernable, unexplainable, and ultimately unusable at scale.
In the report, Ryan Kurt, CEO at The AI Lab stated: “Without the right infrastructure, you’ll hit a ceiling. There is absolutely no way to break through it unless you have the data scaffolding, the governance, and the integrated workflows that you need.”
A shift in data understanding
The report finds that the organisations pulling ahead aren’t simply buying more AI tools. They are re-architecting their data layer, treating telemetry as a strategic input rather than an afterthought, fusing machine data with human context so agents can reason effectively, and choosing open, interoperable platforms that give them flexibility as the AI landscape continues to shift.
For IT and security teams, the urgency is acute. Agentic AI not only multiplies telemetry volume, but it also changes the nature of what that data must do. Legacy tools capture what happened. Agents need to know why, in real time, at scale, across every system they touch.
Key survey findings
- 96% expect agentic AI to be critical to business strategy within two years; only 23% have the strategy and infrastructure to support it today.
- 76% report telemetry data volumes have already increased due to agentic AI; 31% say volumes have doubled or more.
- 47% of organisations actively deploying agents say infrastructure costs exceeded expectations.
- 82% anticipate a financial cost to meet agentic AI’s data infrastructure demands.
- 46% cite unclear ROI and performance metrics as the leading consequence of unprepared infrastructure, the primary reason AI projects stall.
- Top adoption barriers: privacy risks (57%), talent shortages (53%), unprepared data architecture (53%), and workflows not configured for agentic AI (52%).
Harvard Business Review Analytic Services surveyed 351 senior business leaders in February 2026, all from organisations actively considering, piloting, or deploying agentic AI. Respondents span North America (45%), Europe (23%), and Asia Pacific (19%); 77% hold senior or executive management roles. The research was sponsored by Cribl.
