Dynatrace Fuels Business Growth with Unrivaled AI-Powered Insight into Customers’ AI Initiatives

Key Terms

observability technical

Observability is a company’s ability to see and understand what its software systems are doing by collecting and analyzing signals like logs, metrics and traces. For investors it matters because strong observability reduces the risk of downtime, hidden bugs or security issues, supports faster fixes and efficient scaling, and therefore can protect revenue, lower costs and signal disciplined operations — like having clear gauges and alarms on a complex machine.

agentic AI technical

Agentic AI refers to computer systems that can make their own decisions and take actions without needing someone to tell them what to do each time. It's like giving a robot a degree of independence to solve problems or achieve goals on its own, which matters because it could change how we work and interact with technology in everyday life.

A/B testing technical

A/B testing is a controlled experiment where two versions of a product element — for example a webpage, email, or app feature — are shown to different groups to see which one performs better on a chosen metric, like clicks or purchases. For investors, A/B testing matters because it reveals how small changes can increase customer engagement, lower marketing costs or boost sales, offering concrete evidence of a company’s ability to improve growth and efficiency, much like a taste test revealing the preferred recipe.

token consumption technical

Token consumption is the process by which digital tokens are used, spent, or removed from circulation within a platform’s economy—for example, paying fees, accessing services, or being destroyed on purpose. It matters to investors because consumption changes how many tokens are actually available and how often they are used, similar to how using or retiring coupons affects their scarcity and usefulness; that can influence a token’s supply, demand, and potential value.

prompt injection technical

A prompt injection is a deliberate attempt to trick an AI system by inserting misleading or malicious instructions into the text it reads, causing the system to behave in unintended ways or reveal sensitive information. Like slipping a fake note into a stack of instructions, it matters to investors because it can lead to data breaches, regulatory breaches, faulty decisions, reputational damage, and unexpected costs for companies that rely on AI-driven tools.

policy violations regulatory

Policy violations are breaches of rules set by regulators, stock exchanges, or a company’s own governance — like breaking the house rules of the market. They matter to investors because violations can trigger fines, trading suspensions, legal costs or damaged reputation, all of which can reduce a company’s value or increase risk; think of them as warning lights that signal potential financial or operational trouble.

model versioning technical

Model versioning is the practice of tracking and managing different iterations of a predictive or analytical model—recording what changed, when, and which data or settings were used. For investors, it matters because consistent versioning helps ensure that forecasts, risk assessments or automated trading rules are reproducible and auditable; like keeping dated blueprints for a machine, it makes it easier to understand performance shifts, diagnose errors, and trust decisions driven by the model.

LLMs technical

Large language models are advanced computer programs that read and generate human-like text by learning patterns from huge amounts of written material; think of them as digital employees that can draft reports, answer questions, summarize documents, or generate code. They matter to investors because they can change a company’s costs, speed of product development, customer service, and competitive edge — and they also create new risks and regulatory questions that can affect profits and valuation.

Dynatrace Perform spotlights cross‑industry customer success with measurable ROI and business impact

LAS VEGAS--(BUSINESS WIRE)-- At Perform, its flagship annual user conference, Dynatrace (NYSE: DT), the leading AI-powered observability platform, showcased how customers are using Dynatrace AI Observability to scale AI applications safely, reliably, and cost-effectively.

Gartner® predicts that “40% of enterprise apps will be integrated with task-specific agents, up from less than 5% now” – making the shift from experimentation to production a business priority.¹ Organizations need observability solutions that can not only monitor AI, but actively optimize, govern, and secure it at scale.

With the Dynatrace platform as a control plane for AI in production, enterprises gain the visibility, automation, and governance required to adopt agentic AI with confidence. This evolution is helping customers manage complexity and compliance while optimizing performance across emerging technologies.

Customers in Action

Canadian technology giant, TELUS, has been using Dynatrace AI Observability to transform incident response and drive measurable operational ROI. By consolidating multiple monitoring tools into a single observability platform, TELUS has lowered tooling costs and achieved a 30% reduction in onboarding time for new teams. Automation and monitoring as code capabilities reduced the effort to deploy end-to-end observability from 600 minutes to just 20 minutes, delivering substantial time savings for the business.

New Advancements

Despite AI enthusiasm, according to recent findings, the majority (95%) of AI initiatives deliver zero return on investment due to failures before reaching production. Dynatrace has introduced major advancements designed to close this gap, helping enterprises scale AI initiatives with confidence while mitigating security and compliance risks such as data leakage, prompt injection, and policy violations.

Recent innovations include:

• Unified observability across the agentic AI stack. Support for a broad ecosystem of agentic frameworks and services, including Amazon Bedrock AgentCore, Amazon Bedrock Strands, Google Agent Development Kit, OpenAI Agent, Anthropic Model Context Protocol (MCP), LangChain Agents and Azure AI Factory agents. This gives organizations a single, unified view across internal and external models, services, and orchestration layers.
• Model versioning & A/B testing. Built-in comparison across models such as GPT-5, Claude, Vertex AI, Azure AI Foundry using metrics including response time, token consumption, cost, and relevancy – enabling data-driven selection and continuous optimization.
• Intelligent alerting and forecasting. AI-driven cost and performance forecasting helps teams anticipate risk early and maintain predictable, efficient AI operations.

“By combining our Agentic AI initiatives with Dynatrace’s AI Observability capabilities, we’ve successfully optimized our development and operations workflows. This collaboration has enabled us to streamline incident resolution to minutes, from detection to pull requests. Through this integration of AI technologies, we’re driving innovation and delivering measurable business impact while reducing downtime,” states Kulvir Gahunia at TELUS. “This partnership has delivered clear, measurable ROI for TELUS by accelerating innovation, reducing operational effort, and enabling us to proactively ensure the reliability and performance of our most important digital services.”

“Across industries, our customers are leading the shift from AI experimentation to AI at enterprise scale,” said Steve Tack, Chief Product Officer at Dynatrace. “Their work demonstrates how deep observability of modern AI workloads – using LLMs, agentic AI workflows, and generative AI applications – enables organizations to move faster and more confidently. By combining visibility with automation and intelligent analytics, our customers are turning AI into measurable business outcomes – faster innovation, improved reliability, higher customer satisfaction, and stronger operational efficiency.”

For more details on how Dynatrace is helping customers accelerate their AI initiatives, please visit the Dynatrace blog.

To learn more about Perform 2026 announcements, visit the Dynatrace newsroom.

About Dynatrace

Dynatrace is advancing observability for today’s digital businesses, helping to transform the complexity of modern digital ecosystems into powerful business assets. By leveraging AI-powered insights, Dynatrace enables organizations to analyze, automate, and innovate faster to drive their business forward. To learn more about how Dynatrace can help your business, visit www.dynatrace.com, visit our blog and follow us on LinkedIn and X @dynatrace.

Curious to see how you can simplify your cloud and maximize the impact of your digital teams? Let us show you. Sign up for a 15-day Dynatrace trial.

Dynatrace and the Dynatrace logo are trademarks of the Dynatrace, Inc. group of companies. All other trademarks are the property of their respective owners.

Cautionary Language Concerning Forward-Looking Statements

This press release includes certain “forward-looking statements” within the meaning of the Private Securities Litigation Reform Act of 1995, including statements regarding the capabilities of AI Observability, the expected benefits to organizations from using Dynatrace and AI Observability, and the timing for when AI Observability and any capabilities, features, or functionality are expected to be generally available. These forward-looking statements include all statements that are not historical facts and statements identified by words such as “will,” “expects,” “anticipates,” “intends,” “plans,” “believes,” “seeks,” “estimates,” and words of similar meaning. These forward-looking statements reflect our current views about our plans, intentions, expectations, strategies, and prospects, which are based on the information currently available to us and on assumptions we have made. Although we believe that our plans, intentions, expectations, strategies, and prospects as reflected in or suggested by those forward-looking statements are reasonable, we can give no assurance that the plans, intentions, expectations, or strategies will be attained or achieved. Actual results may differ materially from those described in the forward-looking statements and will be affected by a variety of risks and factors that are beyond our control, including the risks set forth under the caption “Risk Factors” in our Annual Report on Form 10-K, subsequent Quarterly Reports on Form 10-Q, and our other SEC filings. We assume no obligation to update any forward-looking statements contained in this document as a result of new information, future events, or otherwise.

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1 Gartner, Emerging Tech: The Future of Agentic AI in Enterprise Applications, Anushree Verma, Aakanksha Bansal, Alfredo Ramirez IV, Danielle Casey, 22 July 2025

GARTNER is a trademark of Gartner, Inc. and its affiliates.

View source version on businesswire.com: https://www.businesswire.com/news/home/20260128730751/en/

Investor Contact:

Noelle Faris

VP, Investor Relations

Noelle.Faris@dynatrace.com



Media Relations:

Dynatrace PR Team

pr-team@dynatrace.com

Source: Dynatrace