AI-Powered Observability: What Developers Need to Know in 2025
The relationship between observability and AI is evolving rapidly, according to Dynatrace's State of Observability 2025 report. For developers and engineers, this shift represents both opportunity and challenge as observability transforms from a reactive operations tool into a proactive development necessity.
AI Becomes the Top Priority
For the first time in observability survey history, AI capabilities (29%) now rank as the most important factor when selecting observability platforms—surpassing traditional criteria like cloud compatibility (27%) and ease of data collection (21%). This dramatic shift reflects how developers increasingly rely on AI to manage the complexity of modern cloud-native architectures.
While 100% of surveyed organizations have adopted some form of AI, adoption remains concentrated in specific use cases. Data management leads at 57%, followed by AI governance (50%) and security operations (46%). Only 32% currently use AI in their observability programs, but this represents a significant growth area as teams recognize observability's role in making AI systems explainable and reliable.
The Investment Surge
Organizations are backing their AI and observability priorities with real budget increases. Seventy percent of respondents report observability budget increases in the past year, and 75% expect further increases next year. This investment reflects observability's expanding role beyond traditional IT operations into security, compliance, sustainability, and business analytics.
Current observability use cases span application performance optimization (59%), cloud application monitoring (57%), and security incident management (57%). However, the expected ROI from AI-powered observability focuses on optimizing model configurations (46%), automating remediation (41%), and detecting anomalies in model outputs (39%).
Trust and Transparency Challenges
Despite widespread AI adoption, trust concerns persist. Cybersecurity (41%) and data privacy (36%) top the list of AI usage concerns, followed by data quality (33%) and integration difficulties (29%). These concerns drive a cautious approach: humans verify 69% of AI-powered decisions, and 99% of AI governance leaders report taking human-monitored measures to validate AI decisions.
This verification requirement creates opportunities for developers. Observability platforms that provide clear explanations of AI decision-making processes become essential tools. Seventy percent of AI governance leaders report budget increases for AI trust and transparency initiatives, signaling sustained investment in making AI systems auditable and explainable.
Security Convergence
The convergence of observability and security creates new responsibilities for development teams. Ninety-eight percent of security leaders use AI to manage security compliance in some capacity, with 69% seeing increased budgets for AI-powered threat detection.
For developers, this means security considerations must be built into applications from the start. By 2030, organizations expect 50% adoption of AI-powered data encryption, risk assessments, and threat detection capabilities. AI-powered threat detection and automated patching are expected to double adoption rates within five years.
DevOps Automation Evolution
Real-time DevOps automation is expanding rapidly, with up to 50% of organizations currently using automation across various use cases. Mitigating security risks (49%) and real-time anomaly detection (50%) lead current adoption, with expectations to reach 70% within five years.
This automation trend introduces agentic AI—systems that can autonomously plan and execute tasks within guardrails. For developers, this means observability becomes crucial for ensuring safe agent behavior, monitoring model performance, and maintaining system health. The survey identifies detecting and mitigating security risks as the automation use case with the highest expected ROI (41%).
Sustainability and Efficiency
Environmental sustainability emerges as another key focus where observability proves essential. Seventy percent of organizations pursuing sustainability initiatives use observability platforms to monitor energy consumption, track emissions, and manage ESG-aligned operations in real time.
For developers, this creates opportunities to optimize code for energy efficiency. Energy-efficient software development is expected to grow from 28% adoption today to 51% by 2030. Observability platforms that translate utilization metrics (CPU, memory, network I/O) into energy usage and carbon emissions help developers understand the environmental impact of their code.
Business Observability
Perhaps the most significant shift is observability's evolution from IT tool to business capability. Only 28% currently use AI to align observability data with business KPIs, but 29% cite AI capabilities as the most important factor in observability solutions. This gap represents both a challenge and opportunity.
Developers who can connect their work to business outcomes gain strategic value. Observability platforms increasingly enable this connection by providing real-time insights into how technology impacts customer experience, operational performance, and financial efficiency.
What This Means for Developers
The convergence of AI and observability creates several imperatives for developers:
Build observability in from the start: With AI capabilities now the top platform selection criterion, teams that treat observability as an afterthought will fall behind.
Focus on explainability: As humans verify 69% of AI decisions, code that provides clear reasoning and audit trails becomes essential.
Embrace automation with guardrails: Real-time automation is expanding, but developers must ensure safe, monitored behavior.
Connect to business outcomes: The shift toward business observability means developers need to understand how their code impacts KPIs.
The State of Observability 2025 reveals that AI and observability are no longer separate concerns but converging capabilities that enable modern software development. Organizations that successfully unify these practices will lead the next wave of enterprise innovation.