Although organizations are universally adopting AI, moving from pilot to production and sustainable scaling present new challenges. Results from the State of Observability 2025 report suggest some ways organizations can use observability data in a 90-day action plan to drive measurable business results.
Published October 7, 2025 4 min read
In this blog post
1.Key takeaways from The State of Observability 2025 report
2.How AI-driven insights translate into business results
3.A 90-day action plan to drive measurable results and understand your business
4.Why Dynatrace for reliable agentic AI projects
Although organizations are universally adopting AI, moving from pilot to production and sustainable scaling present new challenges. Results from the State of Observability 2025 report suggest some ways organizations can use observability data in a 90-day action plan to drive measurable business results.
Organizations are integrating artificial intelligence into their operations at a rapid pace. This transformation is changing how businesses work, innovate, and compete. The State of Observability 2025 report confirms that while 100% of responding organizations are now using AI, how they’re using it is often fragmented.
Senior IT and business leaders should pursue a unified strategy to link AI initiatives with clear business results. A practical solution that’s gaining traction is AI-powered observability, which is evolving from a technical monitoring platform or tool suite into a strategic control plane for AI transformation.
The emergence of AI technologies within observability presents a novel opportunity for leaders to drive tangible business value from data across the full stack. Insights from our research highlight several key trends that are reshaping priorities so you can create a new action plans for sustainable growth, efficiency, and resilience.
Key takeaways from The State of Observability 2025 report
- Observability is a fast-growing AI use case. With 75% of organizations increasing their observability budgets, it’s clear that leaders see it as a critical investment for managing AI. In fact, AI capabilities are now the #1 criterion for selecting an observability solution.
- The AI trust gap is real. Humans are still very much in the loop. A significant 69% of AI-powered decisions are verified by humans, and one in four leaders believes improving trust in AI should be a top priority.
- AI-powered observability encompasses application security, DevOps, and sustainability. Nearly all security leaders (98%) use AI for security compliance, and 69% have increased budgets for AI-powered threat detection. At the same time, more than 70% of organizations use observability to manage sustainability initiatives.
- Business observability is on the rise: While only 28% of organizations currently use AI to align observability data with business KPIs, the opportunity is clear. Leaders are moving toward real-time solutions that connect technical performance directly to customer experience and business agility.
These findings illustrate that observability is no longer just about keeping systems running. It’s about optimizing performance, reducing risk, and aligning every aspect of your technology stack with strategic business goals.
How AI-driven insights translate into business results
Being able to understand what’s happening in all dimensions of your operating environments presents some clear business benefits. Here are just a few.
Lower risk and faster response
With AI-assisted detection and guided remediation, teams can reduce the impact of incidents and significantly cut response times.
Lower unit cost and carbon impact
By correlating observability telemetry with cloud spend, energy usage (kWh), and CO₂ emissions, leaders can uncover operational waste and identify clear opportunities for savings.
Stronger security posture
Integrating security and observability enhances compliance, extends threat visibility, and improves the overall quality of incident response.
Greater AI trust and accountability
Human-verified guardrails and comprehensive audit trails improve the transparency and trustworthiness of AI-driven actions.
Clear KPI alignment
It’s now possible to tightly link services and customer journeys to business-critical metrics like MTTR, SLO attainment, cost per request, revenue at risk, and customer experience, enabling informed, real-time decisions.
While these insights are a good start, turning them into an action plan is the critical next step.
A 90-day action plan to drive measurable results and understand your business
For executives looking to deliver measurable ROI from AI projects by harnessing the power of AI-driven observability, here’s an actionable 90-day plan.
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Instrument what mattersBegin by mapping your top five revenue or mission-critical customer journeys. Identify and close telemetry gaps across logs, traces, metrics, and real-user experience data to create a complete picture of performance. |
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Connect to business KPIsFirst, establish a scorecard that links technical metrics to business outcomes. Include MTTR, Mean Time to Detection (MTTD), SLOs, cost per request, revenue at risk, customer experience, and a security incident score. Ingest cloud billing data and tag costs to specific services to gain financial visibility. Next, secure two quick winsSecurity. Pilot AI-assisted threat detection and guided response on one high-value service. Measure and report the improvement in time-to-contain threats. Cost. Link service utilization to cloud spend and carbon emissions (kWh and CO₂e). Identify one clear source of waste, remove it, and report the financial and environmental savings. |
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Automate with guardrailsSelect your two most frequent operational responses and add generative AI to automatically draft remediation workflows, simulate outcomes, and enhance decision-making. Implement a human-in-the-loop approval process for policy checks and rollbacks to maintain control and build trust. Track the outcomes with a live dashboard to demonstrate success. |
Why Dynatrace for reliable agentic AI projects
Dynatrace provides the context and controls leaders need to run AI like a business program:
- Contextual analytics of unified observability, security, and business data.
- Advanced predictive, causal, and generative AI to provide deterministic answers and validate generative AI results.
- Preventive operations through ecosystem workflow automation capabilities.
If you’re seeking to turn AI-driven observability into a source of competitive advantage, explore what’s possible with Dynatrace and take the next step toward resilient, agentic AI projects.
