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Can Dynatrace And ServiceNow Truly Deliver Autonomous IT?
The new multi-year collaboration marries Dynatrace’s Causal AI-powered observability with ServiceNow’s workflow automation to enable autonomous IT operations.
Key Highlights
- The partnership is architected to create a closed-loop system that moves incident detection to full resolution without mandatory human intervention.
- Dynatrace’s Davis AI provides precise, high-fidelity root cause analysis which feeds directly into the ServiceNow platform for actionable intelligence.
- The integration focuses on combining real-time application and infrastructure telemetry with ServiceNow's IT Service Management processes.
- Both organizations are committing to deploy each other’s platforms internally to accelerate integration enhancements and demonstrate proven joint value.
- This strategic alignment escalates the battle for the center of the AIOps market against vendors pushing unified, single-data-platform strategies.
The News
Dynatrace and ServiceNow announced a multi-year strategic collaboration aimed at advancing autonomous IT operations and scaling intelligent automation for their joint enterprise customers. The partnership aligns Dynatrace’s AI-powered observability capabilities with ServiceNow’s AI-powered IT Service and Operations Management suite. The goal is to facilitate proactive, self-healing systems that improve digital experiences and optimize service delivery. This agreement also includes both companies using each other's products for their own internal digital operations. Find out more by clicking here to read the press release
Analyst Take
This announcement, which deepens an existing technology alliance, is far more significant than a typical partnership. It acknowledges a fundamental reality: observability platforms are phenomenal at understanding the health of the digital factory, but they often lack native competence in managing the human or automated workflows that govern the factory floor. Similarly, IT service management (ITSM) platforms excel at governing workflows and the Configuration Management Database (CMDB), yet historically struggle to ingest and analyze the sheer volume of high-fidelity telemetry required for real-time root cause analysis.
This collaboration is a pragmatic, yet necessary, response to the market’s push toward full-stack consolidation. While competitors like Datadog or Splunk attempt to own the entire pipeline—from telemetry ingestion through service management—this combined offering essentially asserts a "best-of-breed via tight integration" strategy. It seeks to combine the world’s most renowned causal AI for observability with the most established platform for IT workflow automation. My analysis indicates this move is less about competition and more about survival in an environment where enterprises are no longer satisfied with mere alerts; they demand autonomous action.
The IT landscape has become incredibly complex, dominated by ephemeral microservices, Kubernetes clusters, and multi-cloud architectures. This complexity creates data noise, leading to alert fatigue and slower mean time to resolution (MTTR). The only way out is automation powered by precise answers. This is what this partnership aims to deliver. It creates a closed loop where Dynatrace acts as the hyper-accurate sensing and diagnosis layer, while ServiceNow serves as the central command-and-control system for remediation. This is truly the key metric I am looking at: is the integration tight enough to allow for trusted, autonomous execution?
What was Announced
The strategic collaboration focuses heavily on integrating the core AI and data fabrics of both platforms to support a "Zero Outage" mandate.
Dynatrace’s platform, fueled by its Grail data lakehouse technology, is designed to ingest and unify metrics, logs, traces, security, and user experience data at scale. The core intellectual property here is the Davis AI engine. Davis is architected to utilize Causal AI, which automatically eliminates event noise and determines the precise root cause of any performance, availability, or security problem in real time. This process includes dynamic topology mapping provided by Smartscape, ensuring the context of every performance issue is tied directly to the relevant service, infrastructure, and business impact.
The integration is built to leverage this pre-analyzed, high-fidelity data. Instead of flooding ServiceNow with millions of raw events, Dynatrace is designed to push only a handful of enriched, actionable problems. ServiceNow’s IT Operations Management (ITOM) product suite, specifically its AIOps capabilities, consumes these precise problems via an enhanced bi-directional API connection.
ServiceNow’s Now Platform is designed to use this enriched data to trigger high-confidence workflows. The AI Agents within Now Assist are architected to perform autonomous tasks based on the definitive root cause provided by Dynatrace. For instance, a Dynatrace problem indicating a specific memory leak in a critical microservice on a Kubernetes cluster can automatically initiate a remediation workflow in ServiceNow ITOM. This workflow can include updating the incident record in ITSM, enriching the record with the Dynatrace Smartscape map, initiating a safe restart or rollback command via automation engine features, and finally, updating the CMDB to reflect any change in the environment.
This detailed, automated data exchange is critical. It bypasses the old model where IT staff would manually copy data between systems. The result is a system that aims to deliver autonomous prevention, remediation, and optimization across the entire software delivery lifecycle by coordinating deterministic AI from the observability side with agentic AI on the workflow side. Furthermore, the commitment to internal deployment—Dynatrace using ServiceNow for enterprise services and ServiceNow using Dynatrace for its own observability—serves as a crucial, real-time testing and feedback loop to continuously optimize the integration and experience for joint customers.
Looking Ahead
The IT operations world is bifurcating into two distinct camps: the unified single-platform approach and the deeply integrated federated approach. This Dynatrace-ServiceNow announcement is the most powerful endorsement yet of the latter. For many large enterprises already heavily invested in both ecosystems, this integration eliminates the primary friction point—the lack of trusted automation between the two domains. The key trend that I am going to be looking out for is how well this integration manages the latency challenge inherent in spanning two separate enterprise platforms. Even with fast API calls, there is always a risk that the speed of action might lag behind the single-data-platform model advocated by Datadog.
The announcement looks to set a new standard for collaboration depth. It compels other AIOps and observability vendors to rethink their ITSM strategy. For instance, New Relic and Splunk must now demonstrate an equally automated, Causal AI-driven, closed-loop integration with ServiceNow, or push much harder on their own native ITSM/workflow capabilities. My perspective is that ServiceNow, by partnering with a leader in deep observability like Dynatrace, maintains its role as the dominant system of action, allowing it to leverage the best sensing technology available without having to build it all internally. Going forward, I am going to be closely monitoring how the company performs on MTTR metrics derived from joint customer success stories, as this is the ultimate measure of autonomous IT value. HyperFRAME will be tracking how the company does in future quarters regarding the adoption rate of these enhanced integration features.
Stephanie Walter | Analyst In Residence - AI Tech Stack
Stephanie Walter is a results-driven technology executive and analyst in residence with over 20 years leading innovation in Cloud, SaaS, Middleware, Data, and AI. She has guided product life cycles from concept to go-to-market in both senior roles at IBM and fractional executive capacities, blending engineering expertise with business strategy and market insights. From software engineering and architecture to executive product management, Stephanie has driven large-scale transformations, developed technical talent, and solved complex challenges across startup, growth-stage, and enterprise environments.