In APMdigest's 2026 Observability Predictions Series, industry experts — from analysts and consultants to the top vendors — offer predictions on how Observability and related technologies will evolve and impact business in 2026. Part 2 covers more predictions about Observability and AIOps.
AI-POWERED AUTO TROUBLESHOOTING
AI-Powered auto troubleshooting is going to make a big move: The coming year will see AI-based troubleshooting make a big leap forward, fundamentally changing how incidents are resolved. Instead of manual debugging sessions and trial-and-error fixes, teams will increasingly rely on intelligent tools that automatically identify problems and recommend solutions. Advancements in machine learning enable these tools to analyze application behavior, logs and performance metrics in real time, detecting anomalies and correlating issues across complex, distributed systems. This means the system itself can pinpoint the root cause of an outage or performance issue within seconds — and even suggest the specific code fix or configuration change to resolve it. Such AI-powered debugging tools dramatically reduce the mean time to resolution (MTTR) by handling the heavy lifting in diagnostics.
Sam Suthar
Founding Director, Middleware
PROACTIVE OBSERVABILITY
In 2026, observability will shift from reactive troubleshooting to continuous system guidance. AI models will interpret raw telemetry as evolving risk signals, generating prioritized, proactive recommendations before outages occur. Operations will move from "responding to alerts" to "managing forecasted degradation."
Vladimir Mihailenco
CEO, Uptrace
By 2026, AI will no longer be a standalone capability. it will be embedded throughout observability, ITSM, and security ecosystems. The key differentiator will be explainability: organizations will demand AI that can justify actions in compliance and audit contexts. Expect to see AI engines that reason over policy, topology, and telemetry to predict both performance degradation and exposure risk. The convergence of predictive analytics with governance will enable truly proactive IT operations, balancing speed with accountability.
Erez Tadmor
Field CTO, Tufin
AI-ASSISTED OPERATIONS
The observability market is entering a breakthrough era where AIOps—powered by a mature ML backbone and an intuitive GenAI interface—will become the expected standard. As operators confront scale, complexity, and talent shortages, AI-driven insight and automation will no longer be optional but essential. The vendors who combine deep, domain-specific models with real-time reasoning will define the next decade of operational tooling. This marks the transition from reactive observability to true AI-assisted operations—systems that can explain, predict, and help resolve issues at the pace modern infrastructure demands.
John Capobianco
Head of DevRel , Selector
AI AUTOMATION
In 2026, Observability will see AI assistants and agents move beyond natural-language explanations and query generation. Engineering teams will increasingly rely on AI agents to take operational actions such as initiating automatic remediation, scaling services, or suppressing duplicate alerts. This shift will come after organizations define clear human-in-the-loop boundaries, specifying which decisions an agent/assistant can make autonomously and which requires human approval. The real discovery here will be the governance frameworks that make agent's actions safe and reliable. This integration will allow SREs/DevOps teams to be much more efficient.
Khushboo Nigam
Principal Cloud Architect, Oracle
AI-TO-AI OBSERVABILITY
The Next Tech Revolution Is AI-to-AI: In 2026, the most transformative conversations will not happen in boardrooms. They will happen between machines. AI systems will communicate, negotiate, and optimize autonomously. The challenge will be keeping those conversations intelligent, ethical, and efficient. The leaders who master AI-to-AI observability will unlock a new level of operational foresight where machine collaboration becomes the backbone of enterprise innovation rather than its blind spot.
Karthik Sj
GM of AI, LogicMonitor
SMART INFRASTRUCTURE
How AIOps is creating smarter infrastructure: By 2026, AIOps for the infrastructure will evolve beyond simple automation, ushering in the era of the Autonomous Cloud. Infrastructure will become intelligently self-optimizing and self-healing, using agentic AI to predict capacity demand and potential failures to prevent outages before they impact the customer experience. This shift will free up engineers from "firefighting" to focus on innovation and high-value strategic work.
Paul Constantinides
EVP of Engineering, Salesforce
As agentic systems take hold, we'll see observability shift toward self-monitoring architectures — ones that can detect anomalies, diagnose root causes, and initiate remediation without waiting for human input.
Dr. Hema Raghavan
Head of Engineering and Co-Founder, Kumo
AIOPS EVOLUTION: BUSINESS OPTIMIZATION ENGINE
AIOps with embedded AI will evolve to become the business process optimization engine. This evolution is the AIOps market to make, before other management disciplines develop this ability.
Roy Illsley MBA CEng MIET
Chief Analyst, Omdia
MODELOPS AND AIOPS CONVERGENCE
ModelOps and AIOps fully converge: As AI becomes foundational to operations, the line between ModelOps and AIOps will blur. In 2026, enterprises will manage operational AI models with the same rigor as traditional applications such as policy enforcement, continuous retraining and performance SLAs, creating a unified "Ops for both code and models" discipline.
Sunil Senan
Global Head of Data, Analytics and AI, Infosys
AIOPS AND GITOPS MERGE WITH DATAOPS
In 2026, DevOps will fully absorb the data layer. AIOps and GitOps will merge with DataOps to create unified pipelines that deploy and monitor code, databases, and infrastructure as one system. Developers will manage schemas, policies, and observability with the same CI/CD workflows they use for apps, making data-driven operations auditable, automated, and faster.
Anil Inamdar
Global Head of Data, NetApp Instaclustr
SECURE AIOPS
"Secure and responsible AI by design" becomes mandatory for operations tooling: With AIOps agents reading data, tickets, logs and in many cases executing changes which would also involve exchange of data across systems & geographies, AI security and responsible AI by design will be at the center of AIOps strategy in 2026. Organizations will architect operations platforms so that security, transparency, fairness, privacy, compliance and human oversight are built in from the outset, not added as controls after deployment.
Sunil Senan
Global Head of Data, Analytics and AI, Infosys
AIOPS ON MAC
AIOps will arrive on Mac as more complex MDM capabilities and as IT orchestration. These types of platforms, much like the tools in the broader market, will leverage localized intelligence to run on the device, report configuration drift, and enable some level of self-remediation. Additionally, back-end services will take a more proactive view of the device fleet in an effort to provide more timely updates and control.
Chris Chapman
CTO, MacStadium
AIOPS EXPLAINABILITY
AIOps will place more weight on the explanation for each automated move. Platforms will keep a short note that shows what happened and why the system took that path. Operators can read that note to judge quality in a direct way that fits day to day work.
Nuha Hashem
CTO and co-founder, Cozmo AI
AIOPS MEASURED BY BUSINESS OUTCOMES
AIOps Success Will Be Measured by Business Outcomes: In 2026, enterprises will judge AIOps success much less by MTTR and more by business-aligned outcomes such as avoided cloud spend, incident cost prevention, and customer-impact reduction. FinOps and ServiceOps teams will increasingly treat AIOps as a lever for financial and experiential optimization, not just operational reliability. This shift will drive demand for platforms that connect technical telemetry directly to business metrics. Vendors that cannot demonstrate cost transparency and value realization will rapidly lose relevance.
Parker Hathcock
Research Director, ServiceOps, Enterprise Management Associates (EMA)
AIOps in 2026 will evolve from event correlation to intent correlation. Machine learning will increasingly interpret network, cloud, and application signals within the context of business policy and compliance objectives. Rather than flooding operators with alerts, AIOps will act as a contextual engine that recommends and automates remediation while maintaining security guardrails. The result will be a tighter fusion between performance management and risk management, creating truly autonomous IT operations that remain policy-driven and auditable.
Erez Tadmor
Field CTO, Tufin