Data overload: The flood of telemetry is overwhelming. 86% of tech leaders say cloud-native stacks produce data “beyond humans’ ability to manage”[7]. Thousands of alerts and logs arrive continuously, creating noise and fatigue. 

Rising complexity: Modern systems spread across many clouds and containers. With 51% expecting complexity to rise, understanding root causes without AI is nearly impossible[1]. 

Tool sprawl: Juggling numerous monitoring and security tools (on average 10) adds confusion. 85% of leaders agree that more dashboards only increase management overhead[3]. 

Manual bottlenecks: 81% report that hand-crafted analytics and legacy APM simply can’t keep pace[4]. SREs lose hours on data preparation and dashboard tuning that could be spent building features. 

AI-driven insights and actions: Dynatrace’s causal AI (Davis) analyzes telemetry to pinpoint the exact root cause (e.g., the service, code release, or process responsible). These precise answers become triggers for AutomationEngine. In Dynatrace’s words, you can “turn precise answers into intelligent, extensible automations that free up time for innovation”[11]. For example, a service outage alert enriched with full call-stack context can automatically trigger a remediation script or a ticket workflow. 

No-/low-code workflow creation: AutomationEngine lets teams build workflows graphically or define them as code. You can drag-and-drop tasks (queries, notifications, API calls, etc.) and link them with triggers (alerts, schedules, REST calls)[12]. Predefined actions cover common operations (such as restarting services, scaling infrastructure, or creating tickets), so teams don’t start from scratch. This visual, “answer-driven” model means DevOps and SREs can automate processes without writing bulky scripts, speeding up rollout. 

Broad integration and scale: As a SaaS product, Dynatrace instantly scales to handle massive data rates, so automations run reliably at cloud scale. Plus, AutomationEngine ships with secure integrations across your ecosystem[13]. For instance, it can bi-directionally connect with ITSM systems, container platforms, or custom APIs. A detected anomaly can thus automatically open a ServiceNow incident or trigger a Kubernetes horizontal scaler, all in context. This avoids “integration toil” and lets teams leverage their existing tools more intelligently. 

Intelligent alert and vulnerability routing: Dynatrace automatically enriches issues with precise context (code, service topology, business impact) and then uses ownership metadata to route notifications. Workflows ensure each alert or vulnerability notice goes straight to the right engineer. For example, when a critical Apache process fails or a new vulnerability is detected, Dynatrace generates a context-rich alert and “escalates [it] to the right owner for speedy resolution”[15]. This targeted routing cuts through alert fatigue – the right person sees the right info instantly. 

Automated CI/CD quality gates: By integrating into release pipelines, Dynatrace enforces automated quality and security checks on every build. AutomationEngine can automatically evaluate a new deployment against service-level objectives (performance, error rates) and known security vulnerabilities. If a release fails these gates, the pipeline is stopped or rolled back. In Dynatrace’s own terms, “every push automatically runs through quality and security gates, resulting in high-quality, secure, and reliable releases”[16]. This ensures only safe, performant code reaches production. 

Predictive autoscaling: Dynatrace leverages AI to forecast resource bottlenecks before they happen. For example, a workflow can run Davis AI to predict CPU or memory exhaustion on a Kubernetes deployment and then automatically adjust its replicas or resource limits. As Dynatrace explains, you can “predict resource bottlenecks and automatically open pull requests to scale applications”[17]. This proactive scaling minimizes downtime and optimizes costs by adding capacity exactly when it’s needed.