AI and the OODA Loop: Reimagining Operations
Findings from the 2025 State of Application Strategy Report indicate that operations will inevitably move toward OODA-loop-based models, driven by the adoption of generative and classic AI.
You may recall last year when I noted that generative AI was redefining automation in operations. Maybe you don’t—or maybe you need a refresher. Either way, here’s a quick recap:
“In the past, the most digitally mature organizations used automation to execute scripts based on well-defined business and operational objectives—making configuration changes and pushing adjustments to policies. The introduction of generative AI has made that nearly passé, with the new goal being a more autonomous system.”
That was based on last year’s annual research. And guess what? This year’s research only reinforces that conclusion. Autonomous is no longer just the next frontier in automation—it’s what operations teams are asking for right now.
We asked respondents in our annual survey to select all the operational tasks they’d like generative AI to assist with, and the top three were a combination of generation and execution:
- 57%: Generation of scripts to deploy new/adjust existing configuration and policies
- 56%: Generation of policies, custom functions, and configurations
- 55%: Execute scripts to deploy new/adjust existing configurations and policies
Incidentally, we also asked a separate question to determine which operational functions respondents would be comfortable with generative AI executing automatically. We were a little stunned to find that 99% of respondents selected at least one function.
Autonomous operations are inevitable.
What does this have to do with OODA Loops? Well, grab some coffee, because I’m about to explain.
What is an OODA Loop?
In IT operations, the OODA Loop (Observe, Orient, Decide, Act) is a continuous, iterative framework for rapid decision-making and adaptation. It enables teams to proactively detect issues, assess their impact in real time, make informed decisions, and take corrective actions quickly—preventing operational chaos and improving resilience.
- Observe: Monitor logs, telemetry, and system health in real time.
- Orient: Analyze anomalies, correlate alerts, and assess impact in context.
- Decide: Prioritize response—automate, escalate, or mitigate.
- Act: Implement fixes, roll back changes, or deploy patches.
Fast, iterative cycles mean faster MTTR, fewer outages, and smarter automation. This isn’t just agile; it’s a continuous loop that constantly adapts. And when applied to IT operations, it changes everything.
IT operations today
Let’s face it: today, we really don’t leverage OODA loops. SRE is closely aligned with the concept, but most of IT operations still live in either a waterfall or agile state. While agile propelled us into a more continuous mode of operations, it doesn’t adequately handle real-time changes or rapid iteration.
The following table highlights the differences at a high level:
| Waterfall | Agile | OODA | |
|---|---|---|---|
| Decision Speed | Slow |
Moderate |
Fast |
Flexibility |
Rigid |
Adaptive |
Highly flexible |
Feedback Loop |
Delayed (end of cycle) |
Continuous |
Continuous, real-time |
Risk Handling |
High (late-stage failures) |
Medium (frequent iteration) |
Low (rapid correction) |
Process Flow |
Linear, sequential |
Iterative, incremental |
Loop-based, rapid iteration |
Change Response |
Resists change |
Embraces change |
Exploits change |
Primary Goal |
Predictability & structure |
Deliver value fast |
Outpace & outmaneuver |
Most organizations are still stuck in a water-scrum-fall mode—where some steps are agile, but the overall process remains waterfall. This hybrid approach slows everything down and creates inefficiencies that could be eliminated with a more adaptive, OODA-based model.
The data says it all
Our annual research strongly supports the shift to OODA loops. The challenges that organizations face align perfectly with the benefits of this approach:
- Observe – High configuration overhead:
Respondents report spending 40–50% of their time managing configurations due to multiple APIs and languages. An OODA-based approach, supported by AI, could reduce this burden by detecting redundant or conflicting systems in real time. - Orient – Data consolidation and standardization:
The move toward data lakes and OpenTelemetry is a significant step toward improving the "Orient" phase. High-quality, standardized data enhances the ability to contextualize and correlate events, leading to faster, more accurate decisions. - Decide – Cross-functional efficiency:
Many respondents highlighted SLO/SLI reporting and root cause analysis as areas that suffer from siloed processes. An AI-driven OODA loop would break down these silos and improve cross-team collaboration and decision-making. - Act – Slow, reactive processes:
Current sequential methods delay action until problems escalate. By adopting real-time analytics and AI in the "Act" phase, teams can deploy fixes immediately, reducing downtime and preventing major incidents.
Conclusion
The data makes it clear: OODA-loop-based operations are the future.
Organizations are consolidating their data, adopting AI, and striving for greater automation. Moving toward an adaptive, iterative model like OODA will eliminate inefficiencies, reduce overhead, and improve responsiveness.
The tools already exist. The only question is: When will your organization be ready to move?