Correlation was never enough.
The era of Causal AI with Dynatrace is here.
98% of technology leaders fear bias in generative AI. In cloud environments processing millions of events per second, knowing what happened is no longer enough. Dynatrace Davis® AI knows exactly why — with deterministic certainty, not probabilities.
The problem traditional AI cannot solve
Most AI systems today find correlations and make predictions from them. However, correlation does not imply causation. When teams need to understand how an action affects an outcome, predictive models cannot explain how they reached that forecast, nor identify the underlying cause-and-effect factors. This creates four critical risks for any organization that depends on digital systems:
Rootless alerts
Teams spend hours on manual investigation in complex modern IT environments without finding the real source of the problem. Causal AI pinpoints the exact root cause, saving substantial time compared to manual resolution.
Biased decisions
Correlational predictions are informed guesses, not certainties, and can be completely wrong in novel situations. This leads to investments in incorrect remediations with direct P&L impact.
No traceability
Probabilistic models cannot explain their reasoning, making regulatory audits impossible. AI systems able to explain their recommendations through Causal AI directly address the growing demand for responsible AI.
Fragmented data
The effectiveness of any AI depends on accurate, complete, consistent, relevant, and real-time data. Siloed, manually tagged, or inconsistent data introduces errors no model can correct on its own.
“Correlation does not equal causation. A predictive model can observe an event and predict that an outcome will occur, but it cannot demonstrate that the outcome occurred because of the event.”
What is Causal AI and how does it work in Dynatrace?
Causal AI
Davis® AI performs continuous causal analysis down to the code level, mapping and understanding relationships across all networks, applications, and services in the organization. It combines topological context with metrics, traces, and log data to identify observability signals for any behavior of interest in real time.
Dynatrace rapidly identifies the source issues behind user experience, application performance, and infrastructure problems before they result in outages. Through dependency mapping, Davis® AI contextualizes and explains incident alerts. According to Dynatrace, this saves substantial time compared to manual troubleshooting across complex modern IT environments.
Dynatrace automatically determines the likely business effects of service issues, prioritizing alerts and notifying the relevant teams while enabling auto-remediation on routine alerts. For well-defined processes, teams can automate tasks such as server restarts, code rollbacks, and configuration changes based on Causal AI determination — all without manual intervention.
Dynatrace uses causality graphs and event-sequence analysis to determine how chains of dependent application or infrastructure events could lead to slowdowns, failures, and outages. This enables preventive actions such as resource autoscaling, traffic shifting, or rollbacks before the problem occurs — not after.
Davis® AI detects more than 80 built-in system event types, including process crashes, deployment configuration changes, and VM migration events. It correlates all events sharing the same root cause into a single trackable problem with a defined lifecycle, updated in real time. Problems have configurable analysis budgets, severity levels (1–5 on the ITIL scale), and identified root-cause entity fields.
Correlational AI vs. Causal AI — The executive difference
| Dimension | Correlational AI | Causal AI — Dynatrace |
|---|---|---|
| Analysis basis | Statistical assumptions on historical data | Exact traceability to real-time contextual data |
| Output type | Probabilistic — requires constant human verification | Fact-based — fully automatable |
| Explainability | Prediction without reasoning explanation | Full detail of how each conclusion was reached |
| Data bias | Prone to bias from algorithm and sample limitations | Works on real data, not historical training data |
| Novel situations | Can fail completely on unseen scenarios | Adapts and discovers “unknown unknowns” |
| Quality prerequisite | Large volumes of historical data | Accurate, complete, consistent, relevant, real-time data |
How to measure the value of Causal AI
Assessing business impact requires classic metrics reinterpreted for the AI world, new native KPIs, and one critical foundation underpinning everything: data quality.
Reduction in engineering hours per incident plus full automation of remediations. Identifying problems before outages reduces direct and indirect disruption costs. Teams focus on strategic initiatives instead of firefighting.
Classic: (benefit − cost) / cost × 100
Root-cause identification through Davis® AI’s dependency mapping drastically compresses MTTR by contextualizing and explaining incident alerts. Continuous causal analysis down to code level eliminates the need to manually reproduce errors.
Classic: total resolution time / number of incidents
Elimination of redundant monitoring tools and reduction of engineering hours on manual investigation. Dynatrace connects user experience, application, and infrastructure events to business KPIs to optimize cloud scaling decisions and reduce costs.
Classic: acquisition + operations + maintenance
Full traceability of every automated decision eliminates manual audit costs. Causal AI provides deterministic evidence that directly addresses responsible AI regulations and SLA compliance in critical environments.
Classic: auditor hours × rate + remediation
Capabilities enabled by Causal AI in Dynatrace Davis® AI — maturity level by category:
Percentage of incidents where the root cause automatically identified by Davis® AI matches subsequent team verification. The central trust benchmark for the deterministic system.
Reduction in fragmented alerts as Davis® AI groups all events sharing the same root cause into a single trackable problem, eliminating notification floods.
Proportion of incidents resolved without human intervention thanks to Causal AI’s deterministic foundation. Higher automation rate means lower operational cost and more engineering time available for innovation.
Indicator of system behavior deviation from its causal baseline. Causal AI detects drifts in previously unseen situations — the so-called “unknown unknowns” — that correlational AI cannot identify.
Causal AI delivers precise, continuous, and actionable real-time insights. But there is one non-negotiable prerequisite: data quality. Without it, even the best deterministic engine produces incorrect results. These are the five pillars to evaluate in your organization:
Data must correctly represent real-world scenarios, considering all relevant variables. Inaccurate data produces an incorrect root cause — and an equally incorrect automation.
Any omission in the dataset can create wrong conclusions. In AIOps, this means having the full range of logs, events, metrics, and traces from the system.
Discrepancies confuse AI models and increase error risk. Data fragmented across silos with multiple copies creates inconsistencies and redundancies that compromise causal analysis.
Data must be appropriate for the questions being asked. Causal AI requires complete system context: not just infrastructure metrics, but also user behavior and business data.
IT systems change frequently. Models trained solely on historical data struggle to diagnose novel events. Causal AI requires real-time model updates, not just historical snapshots.
“High-quality data creates the foundation for credible insights that organizations can use to make sound decisions. Causal AI can reduce false-positive alerts by 95%, according to a Forrester Consulting report.”
Executive questions for this week
Every C-Suite role has a specific question that Dynatrace Causal AI can answer immediately:
Can we demonstrate to the board that our automation decisions are grounded in verified causality rather than statistical assumptions? Are we accelerating digital transformation with self-maintaining systems without increasing operational risk?
Can our monitoring stack answer “why” an incident occurred without spending hours on manual investigation? Do we have a causal foundation to trust the auto-remediation of critical systems? How much MTTR are we losing today from the absence of automated root-cause analysis?
Does our data quality meet the five pillars Causal AI requires: accuracy, completeness, consistency, relevance, and timeliness? Can we scale generative AI safely with a verified deterministic foundation underneath?
How much do we spend annually on engineering hours investigating alerts with no verified root cause? Is Forrester’s 95% false-alert reduction already factored into our ROI model? What is the real cost of outages caused by correlational versus causal AI?
The right question changes everything.
From “what happened?” to “why did it happen?” — that leap separates organizations that react from those that prevent. Dynatrace Davis® AI has the deterministic answer leaders need.