Enterprise automation rarely fails because teams lack ideas. It fails because scaling automation safely is challenging, and conventional workflows often remain static after they ship, while systems and conditions continue to evolve.

The new update to Dynatrace Workflows helps teams accelerate their innovation loop and run automation like production software. Five improvements make it easier to iterate and operate at scale: workflow drafts, sub-workflows, approval requests, persistent execution data, and real-time notifications. Together, they support safer change, reusable standards, built-in governance, and data-driven refinement.

The innovation loop in enterprise automation

The innovation loop is a repeatable cycle for quickly and safely improving enterprise automation that treats workflows as living systems that evolve with your environment, self-improving as systems, teams, and requirements change, rather than remaining static:

• Teams start by designing or updating a workflow and validating it safely before it goes live.
• Next, they standardize the parts that work into reusable building blocks so future workflows are faster to create and more consistent.
• Then they add governance where it matters, such as approval checkpoints for high-impact actions, so speed does not bypass policy.
• After execution, they observe what actually happened using execution records and signals, including errors, latency, and outcomes, to understand reliability and impact.
• Finally, they refine the workflow based on those findings by tuning logic, tightening controls, improving reuse, and adjusting notifications.

The result is a compounding impact: faster delivery over time, fewer incidents at scale, automation that integrates changing circumstances, and greater organizational trust, as transparency, auditability, and tight feedback loops back improvements.

How Dynatrace Workflows accelerates the innovation loop

Dynatrace Workflows helps you accelerate your innovation loop with powerful new features designed for adaptability, collaboration, transparency, and control. Each feature is designed to empower teams to safely iterate on automation in dynamic enterprise environments.

Faster iteration with safer testing and experimentation with workflow drafts

The new workflow drafts feature allows teams to create, edit, and refine workflows in a draft state, allowing them to validate logic and configuration without triggering live automation. Workflows that remain in draft mode don’t consume workflow hours.

Workflow drafts support the first step of the innovation loop: iterate safely. Teams can make changes, review them with peers, and confirm the workflow is ready before anything runs in production.

Key benefits

• Reduce risk by testing configuration changes without production impact.
• Allow faster iteration and cleaner change management.
• Improve collaboration during the design and review stages.
• Control costs with included workflow-hour consumption for draft-only workflows.

Standardize reusable blocks for reliability and speed with sub-workflows

Sub-workflows allow a workflow to be executed as a task inside another workflow, so teams can package repeated logic into reusable sub-workflows and compose larger automations from consistent building blocks.

This supports the “standardize” step of the innovation loop. When teams standardize and reuse proven components, automation becomes more consistent, easier to maintain, and quicker to expand across teams and environments.

Key benefits

• Enhance reliability and consistency by reusing proven workflow components.
• Speed up delivery by composing workflows from standard blocks.
• Simplify maintenance by updating shared logic in a single location.
• Keep complex automations understandable by breaking them down into smaller, manageable units.

Example use cases

• Notification workflows: Automatically send notifications based on incident severity or timing.
• Data processing workflows: Run filter/transform/store stages as distinct sub-workflows, processing large datasets in stages.
• Incident response workflows: Automate repetitive tasks as reusable steps, such as creating tickets, logging incidents, notifying stakeholders, and triggering remediation steps.

Supervised human-in-the-loop control where it matters with approval requests

With Dynatrace Workflows, you can now insert an approval request into a workflow and pause execution until a human reviews and approves the next step.

This feature supports the “governance” step in the innovation loop. Approvals turn your workflows into supervised autonomous operations. This allows the automation of more processes while ensuring strict compliance with enterprise requirements for governance, risk management, and accountability, particularly for high-impact actions.

Key benefits

• Add explicit human control at high-risk decision points.
• Reduce errors in sensitive operations (changes, remediations, escalations).
• Ensure compliance with organizational standards.
• Increase trust in automation across security, ops, and platform teams.
• Minimize operational risks.

End-to-end visibility with an audit trail and persistent execution data

Dynatrace Workflows now persists workflow execution data in DQL. All workflow activities are recorded as system events in the dt.system.events table, creating a queryable execution record for tasks, states, and outcomes as a comprehensive audit trail.

Persisting execution data supports the “observe” step of the innovation loop. When workflow execution is captured as data, teams can troubleshoot failures, provide transparency, prove what happened, and improve automation based on evidence rather than anecdotes.

Key benefits

• Provide an audit trail for enterprise traceability and review.
• Monitor workflow health and reliability through execution outcomes.
• Speed up troubleshooting by pinpointing where failures occur.
• Optimize automation using DQL-driven actionable insights (error rates, trends, hotspots, and more)

Example: Workflow health overview

This example demonstrates how to use execution data to support users in identifying those workflows that need improvement.

The following DQL query…

fetch dt.system.events
| filter event.kind == "WORKFLOW_EVENT" and event.provider == "AUTOMATION_ENGINE" and event.type =="TASK_EXECUTION"
| filter dt.automation_engine.state == "ERROR"
| summarize count = count() , by:{dt.automation_engine.workflow.title}

…counts task execution errors per workflow. Using a honeycomb visualization, it displays error distribution by workflow and provides visibility into those workflows that fail too often.

Instant alerts for failures and workflow changes with real-time notifications

Easily set up and customize real-time notifications by email for all workflow errors or changes made by others, ensuring you’re always informed.

These real-time notifications support the “respond and refine” step of the innovation loop. Proactive alerting allows you to manage workflows effectively, minimize disruptions, and maintain seamless operations.

Key benefits

• Get immediate visibility into failures to address potential issues before they escalate.
• Detect workflow changes early to reduce drift and surprises.
• Improve operational coordination across teams that share workflows
• Ensure uninterrupted operations with timely, actionable alerts.

Empower your team: Build smarter, safer, and scalable workflows today

Scaling automation today is hard, changes are risky to test, automation becomes outdated and buggy as soon as one part of the organization makes changes, and when something inevitably breaks, you’re left hunting for answers across scattered logs and screenshots. But it doesn’t need to be like this.

Are you ready to make things easier, run your workflows like production software, and accelerate your innovation loop? Here’s how you get started:

• Explore our documentation to learn more about these features.
• Join the Dynatrace Community to connect with other users and share your insights in the Workflow and AutomationEngine feedback channel.

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Enterprise adoption of generative AI is showing no signs of slowing down, and it’s easy to understand why; organizations in every vertical aim to reap its benefits, including increased efficiency, routine task automation, and content generation, ultimately creating a competitive advantage. To better help organizations maximize the benefit and full potential of generative AI, Dynatrace has achieved the Amazon Web Services Generative AI (GenAI) Competency.

With this milestone, Dynatrace reinforces its position as a leading observability partner, backed by a proven track record of innovation and customer success on AWS. Building on its achievement of earning the AWS Machine Learning Competency, Dynatrace continues to drive advancements in generative AI.

Weighing the importance of this milestone for Dynatrace customers

This competency is more than just a badge; it’s a validation of how Dynatrace can help organizations safely, efficiently, and cost-effectively adopt generative AI in their business. AWS awards these competencies after rigorous technical validation and proven customer success. This means organizations can trust that Dynatrace solutions are designed to deliver measurable outcomes on AWS.

For existing customers, this competency reaffirms the Dynatrace commitment to continued innovation alongside AWS. This ensures the Dynatrace AI-powered observability platform evolves with the latest advancements in AI, future-proofing organizations’ existing investments as generative AI capabilities become core to modern cloud workloads.

For new customers, Dynatrace provides a trusted, proven foundation for observability and AI adoption on AWS. Whether an organization is exploring GenAI for customer engagement, automation, or new digital experiences, Dynatrace ensures these systems are reliable, secure, and optimized at every step.

Looking ahead with AI-powered observability on AWS

As organizations increasingly adopt generative AI, observability becomes a critical enabler. By leveraging Dynatrace causal AI, predictive insights, and seamless AWS integrations, organizations can maintain control over costs, risks, and performance while driving innovation, enhancing competitive advantage, and delivering exceptional customer experiences.

Whether you’re building, scaling, or fine-tuning GenAI application, Dynatrace and AWS Bedrock empower you to transform your observability. With end-to-end visibility into AI workloads, their interactions in full context of your business, and cloud-native applications, you can optimize performance, troubleshoot effectively, and maximize the value of your GenAI investments with greater confidence and precision.

Learn more

• Davis AI
• Dynatrace for Amazon Bedrock
• Explore the Bedrock Monitoring Playground to get hands-on with real-time dashboards that reveal how your Amazon Bedrock models behave in production.
• Install the AI Observability app to integrate Dynatrace’s AI Observability app into your environment to monitor GenAI workloads.
• Stop by booth #575 at AWS re:Invent 2025 to see Dynatrace in action.

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As organizations embrace a cloud- and AI-native future, the pressure to control infrastructure spending while meeting sustainability goals intensifies. As a CTO, I want my investments to go into people—building strong, innovative development teams—rather than overspending on cloud resources that don’t deliver business value.

This is where Dynatrace plays a crucial role: helping organizations optimize cloud costs while advancing sustainability goals and enabling AI innovation. These priorities are no longer at odds; instead, they go hand in hand.

The true cost of cloud and AI

The rapid spread of AI—including LLMs, agentic AI systems, and coding assistants—and the shift to dynamic, multicloud environments have created yet more layers of complexity. AI workloads are compute-intensive. In fact, one of our customers in the banking sector shared that GenAI tasks cost five times more than traditional cloud workloads. Gartner® predicts that by 2028, more than 50% of cloud compute will be AI-related, up from just 10% in 2023.¹

The International Energy Agency – Electricity 2024 report stated that when comparing the average electricity demand of a typical Google search (0.3 Wh of electricity) to OpenAI’s ChatGPT (2.9 Wh per request), and considering 9 billion searches daily, this would require almost 10 TWh of additional electricity in a year. That’s enough to power approximately 3 million households—or all private households in London—with energy for a year.

This growth brings significant environmental and financial implications. Yet, most organizations are beholden to opaque carbon footprint multipliers calculated by the cloud provider, which is often insufficient for actionable insights. Similarly, traditional cost reporting tools lack the depth and runtime visibility needed to drive meaningful optimization.

Dynatrace fills that gap, combining real-time observability, AI-powered insights, and topology-aware mapping to bring deep clarity into both cost and carbon impact.

Four steps to smarter cloud cost and energy management

1. Eliminate waste from idle or underutilized resources

Much of today’s cloud waste stems from overprovisioning and forgotten instances, especially in development and AI workloads. Dynatrace automatically detects underutilized or idle resources across the environments and surfaces insights that can drive decisions whether to shut down or re-size them, reducing both spend and carbon footprint.

Smartscape® automatic discovery and topology mapping adds unique value here, showing not just what’s idle, but whether it’s tied to business-critical processes or genuinely redundant.

2. Align cloud consumption to business value

Executives need more than cost data—they need to understand the why behind consumption. Dynatrace connects cloud utilization directly to applications, users, and business processes, enabling teams to assess whether resources are delivering real business value.

By linking costs to outcomes, organizations can prioritize what to keep, right-size what’s inefficient, and decommission what’s no longer serving a purpose.

3. Optimize architecture and energy efficiency

Most organizations are already taking basic steps like using contracted discount reserved instances or more flexible on-demand spot instances. The next level is architectural and source code optimization, such as green architecture and green coding. Dynatrace helps identify inefficient data flows, underperforming services, and high-cost cross-region transfers.

These insights enable teams to apply green coding techniques, reduce energy-hungry compute patterns, and bring data flows closer to where they’re needed, cutting both cost and carbon emissions.

4. Enable smart, automated orchestration

Finally, Dynatrace has a clear vision to make operations more autonomous. Its predictive, AI-driven orchestration of cloud resources enables teams to automatically scale resources up or down based on real-time demand, user behavior, and business impact.

However, autoscaling based on cloud metrics alone can’t ensure a great user experience or cost efficiency. Dynatrace links infrastructure and deep application observability to user-facing outcomes, allowing for smarter scaling that adapts dynamically to seasonal spikes, new product launches, or unexpected load while eliminating idle time and energy waste.

Accelerating sustainable innovation

Sustainability is now a strategic lever, not just a compliance checkbox. It resonates with environmentally conscious customers and a new generation of employees who want to work for conscientious companies.

By using Dynatrace Cost & Carbon Optimization and full-stack observability, organizations can:

• Gain real-time, fine-grained insights into the energy and carbon impact of workloads
• Make carbon reporting actionable and automatable instead of superficial
• Build a more efficient, resilient, and future-proof cloud environment

Imagine your cloud-native teams rapidly scaling up environments to test the scalability of new AI features, leading to a 40% spike in compute usage. Without visibility, one wouldn’t notice that this test left over idle or oversized instances, quietly driving up both cloud costs and carbon emissions. Now imagine having real-time insights from Dynatrace that reveal 200 idle instances across non-critical environments, costing thousands monthly and consuming unnecessary energy. Dynatrace AI leverages Smartscape® real-time topology to know automatically which instances can be confidently decommissioned or right-sized—cutting waste, aligning spend to business value, and advancing your sustainability goals.

The bottom line: Intelligent clouds mean a more sustainable planet

Organizations today must move beyond basic FinOps or simple sustainability checklists. The future lies in intelligent, self-optimizing clouds that balance performance, cost, and sustainability in real time.

Dynatrace empowers executives to realize this vision—transforming cloud environments into engines of innovation that are efficient, responsible, and aligned with business and environmental goals.

^{1 Gartner Press Release, “Gartner IT Symposium/Xpo 2024 Orlando: Day 3 Highlights,” October 23, 2024, https://www.gartner.com/en/newsroom/press-releases/2024-10-23-gartner-it-symposium-xpo-2024-orlando-day-3-highlights.}

^{GARTNER is a registered trademark and service mark of Gartner, Inc. and/or its affiliates in the U.S. and internationally and is used herein with permission. All rights reserved.}

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AI, especially AIOps, has emerged as a pivotal solution, promising to avoid downtime. The 2024 State of AI Report highlights this trend, with 89% of technology leaders anticipating that AI will significantly enhance incident response by learning to automate and optimize various tasks, such as performance monitoring and workload scheduling.

AIOps can identify and address potential issues before they become major incidents by learning from history and analyzing large amounts of data in real time. This approach improves operational efficiency and resilience, though it’s not without flaws. The complexity of IT environments and the changing nature of threats necessitate human oversight and ongoing adjustment of AIOps systems to handle unforeseen challenges and ensure optimal performance. Additionally, predictions based on historical data are reactive, solely relying on past information to anticipate future events, and can’t prevent all new or emerging issues. This limitation highlights the importance of continuous innovation and adaptation in IT operations and AIOps strategies.

“The shift from reactive to preventive operations represents the next evolution in AIOps.”
Bernd Greifeneder, CTO Dynatrace

When Dynatrace set out with Davis® AI over 10 years ago, pioneering AI-driven operations, we focused initially on problem identification before moving on to problem remediation. The next milestone in enhancing the capabilities of Davis AI—another pioneering step forward in AI-driven operations—is outright problem prevention. In this blog post, we explain how the unique combination of causal, predictive, and generative AI—augmented by the latest Davis AI advancements—is transforming how Dynatrace customers manage and optimize their IT infrastructure.

Automatic root cause detection

Modern, complex, and distributed environments generate a substantial number of events. This necessitates additional requirements such as minimizing the total number of issues, eliminating false positives, and conducting accurate root cause analysis.

Dynatrace has a longstanding reputation for accurately analyzing root causes and identifying related events. While other methods typically rely on mere correlation and historical data analysis, we’ve further enhanced our capabilities by implementing causational analysis, which leverages contextual information automatically gathered during data ingestion and processing in addition to historical data analysis. This is achieved using Dynatrace Grail™, our causational data lakehouse, which unifies all data in an always-up-to-date topology model. By applying causal AI to incoming data in real time, Davis instantly learns and continuously adapts to new information. This facilitates more precise root cause analysis and anomaly detection, including identifying seasonal anomalies and establishing auto-adaptive thresholds.

When applying this Davis root cause detection within our own IT environment, Davis effectively filters out over 99.9% of incoming data noise, condensing hundreds of thousands of daily system events into no more than four or five incidents that require attention from our IT operations team.

These algorithms are not limited to monitoring IT environments. At our February 2025 Dynatrace Perform session on exploratory analytics with AI-driven insights, the Performance Engineering Lead of XXXLutz—one of the world’s largest furniture retailers operating more than 370 stores across Europe—explains how XXXLutz utilizes Davis AI to proactively identify critical order drops, allowing them to respond quickly and effectively to changing market conditions and ensuring that their business remains agile and responsive to the needs of their customers.

Problem journey and reactive remediation

At the core of Dynatrace problem remediation stands the Problems app—an optimized view into opinionated insights, details, and context of each detected issue—for Operations, SREs, and developers. It filters billions of log lines, including the topology of each incident and its affected entities, for efficient problem triaging and troubleshooting, resulting in a 56% faster mean time to repair (MTTR) for critical incidents.

With the latest release, we drive this further by improving the automatic connection of relevant log and trace data for further drill down, presenting the full context of an issue in a single view. This provides comprehensive visibility into even complex architectures, simplifying the process of examining relevant details and addressing code-level issues, reducing 100 clicks and manual filtering to a single click with no loss of context.

By utilizing Davis CoPilot™, you can conduct comparative analyses of multiple issues, obtain natural language summaries of individual problems, and receive contextual recommendations along with specific remediation steps.

You can also link troubleshooting guides created in Notebooks to remediated issues, thereby building an intelligent knowledge base. Davis automatically connects additional documents as well as stored workflows. So the next time a similar problem arises, Davis brings up related guides, enabling teams to learn from previous experiences and reducing the risk of knowledge loss.

Please refer to our recent blog posts for more information on utilizing Problems for AI-driven insights and the latest Davis CoPilot advancements.

Automating the remediation

While obtaining comprehensive insights is beneficial, true transformation occurs through the use of tools that automatically execute remediation steps. To implement these “AI-driven operations,” it’s essential to forecast future requirements, including capacity demands, potential system failures, and security incidents.

Traditional forecasting engines typically depend on historical data, stored in metrics. In contrast, Davis AI generates real-time predictions, facilitating proactive operations. This capability is due to Davis’s ability to process raw data, such as logs, for forecasting, leveraging Grail to execute previously unattainable queries.

Consider the following scenario: You begin by retrieving and analyzing logs to identify relevant values for automation. Once this task is complete, you proceed to your pipelining tool to configure ingestion rules that extract these values into metrics and then wait several weeks for your prediction engine to generate alerts that can serve as triggers for your workflows.

However, when utilizing Dynatrace with its integrated anomaly detection and forecasting capabilities, you gain the advantage of schema-less data analysis and the ability to process any raw data into time series in real time. This significantly reduces the time required to establish AIOps workflows from several weeks to less than 30 minutes.

Preventive operations

The complexity of modern software environments makes it challenging to determine a service’s reliability solely through testing. It’s impractical to emulate scenarios such as generating a million tickets to assess performance capabilities. This necessitates real-time insights and operations rather than reactive problem-solving or raising alerts to notify personnel.

Preventive operations address this need by enabling proactive corrective actions before issues arise, akin to predictive maintenance. AI-supported anomaly detection identifies parameters that deviate from the norm, allowing for automatic configuration adjustment to mitigate potential problems preemptively.

Davis CoPilot combines the “power of three”:

• Davis causal AI for identifying anomalies and root cause analysis
• Davis predictive AI for precise forecasting and determining when to take action
• Generative AI capabilities that perform actions beyond simply sending notifications or restarting services

In this way, Dynatrace extends AIOps beyond traditional IT operations tasks and addresses complex scenarios, including security use cases such as threat observability. Consider the following real-world example:

At Dynatrace, we log all failed login attempts. We can predict potential threats when abnormal patterns are identified and raise a security event by utilizing seasonal baselining. The subsequent workflow involves checking the IP address and generating a threat score. Upon reaching a certain threshold, a new ruleset is automatically added to the web application firewall. This entire process is fully automated, running before a problem even occurs, significantly reducing the response time from over an hour to a fraction of a second.

In another instance, automatic log pattern analysis crawling our application logs decreased the number of bugs in the production environment by 15% and freed up time previously spent on log analysis and triaging (in pre-prod), equivalent to 17 full-time employees. Consequently, these 17 developers can now dedicate their efforts to adding more value to Dynatrace.

Summary

The State of AI report states that over 88% of technology leaders anticipate AI will enhance incident responses and improve their teams’ ability to predict and proactively resolve service-affecting issues.

With Dynatrace, organizations are prepared to evolve their ITOps and SRE departments from troubleshooting to prevention, getting proactive with forecasting, and utilizing generative AI instead of purely focusing on history-focused root cause analysis.

Start your preventive operations journey with smart automation and auto-remediation that prevents larger issues.

Are you interested in gaining more insights?

• Read about recent advancements in our Problems app
• Get insights into Davis root cause analysis
• Learn more about how Davis CoPilot helps you be more productive
• Or dig deeper into how to utilize Davis forecasting capabilities within Dashboards

The post Advancing AIOps: Preventive operations powered by Davis AI appeared first on Dynatrace news.

Ensuring smooth operations is no small feat, whether you’re in charge of application performance, IT infrastructure, or business processes. Chances are, you’re a seasoned expert who visualizes meticulously identified key metrics across several sophisticated charts. Your trained eye can interpret them at a glance, a skill that sets you apart.

However, your responsibilities might change or expand, and you need to work with unfamiliar data sets. The market is saturated with tools for building eye-catching dashboards, but ultimately, it comes down to interpreting the presented information. This is where Davis AI for exploratory analytics can make all the difference.

Davis AI can help you expand your dashboards and dive deeper into your available data to extract additional information. Our customers value the nearly unlimited possibilities for querying and joining data on the Dynatrace platform, with the option of instant, real-time visualization of query results. Whether you’re an expert or an occasional user, our recently launched Davis CoPilot will enable you to get instant results without the need to write complex queries yourself. Have a look at our recent Davis CoPilot blog post for more information and practical use cases.

If you’ve already created your dashboards, now is the time to use Davis AI to identify anomalies or predict future trends without restricting use cases.

Leverage Davis AI for anomaly detection and instant insights

“My chart shows a peak at 8:00 AM. Do I need to investigate this further?” You might be regularly confronted with this or similar questions. Davis AI machine learning capabilities will help you identify actual anomalies within seconds, enabling you to focus resources on issues that matter.

Based on your requirements, you can select one of three approaches for Davis AI anomaly detection directly from any time series chart:

• Auto-Adaptive Threshold: This dynamic, machine-learning-driven approach automatically adjusts reference thresholds based on a rolling seven-day analysis, continuously adapting to changes in metric behavior over time. For example, if you’re monitoring network traffic and the average over the past 7 days is 500 Mbps, the threshold will adapt to this baseline. An anomaly will be identified if traffic suddenly drops below 200 Mbps or above 800 Mbps, helping you identify unusual spikes or drops.
• Seasonal Baseline: Ideal for metrics with predictable seasonal patterns, this option leverages Davis AI to create a confidence band based on historical data, accounting for expected variations. For instance, in a web shop, sales might vary by day of the week. Using a seasonal baseline, you can monitor sales performance based on the past fourteen days. An anomaly is identified if sales on a Friday are significantly lower than on previous Fridays, indicating a potential issue.
• Static Threshold: This approach defines a fixed threshold suitable for well-known processes or when specific threshold values are critical. For example, if you have an SLA guaranteeing 95% uptime, you can set a static threshold to alert you whenever uptime drops below this value, ensuring you meet your service commitments.

Davis AI is particularly powerful because it can be applied to any numeric time series chart independently of data source or use case.

The following example will monitor an end-to-end order flow utilizing business events displayed on a Dynatrace dashboard. By leveraging Davis AI anomaly detection, we can identify potentially fraudulent behavior by activating anomaly detection on the Average order size chart. As shown in the chart below on the lower left, most values fall within the band of acceptable response time (highlighted in green), with only one spike occurring at 5:00 AM. Since this spike was outside the expected range, an anomaly was identified.

• Application Observability: Identify unexpected error rate increases in application performance, helping pinpoint and resolve issues quickly.
• Digital Experience Management: Monitor user interaction patterns to spot anomalies in website or app performance that could affect user experience, such as slow page load times.
• FinOps: Track irregularities in cloud spending or resource usage, enabling cost optimization and preventing budget overruns.

Forecasting: Visualize trends directly on your charts with Davis AI

Davis AI forecast analysis predicts future numeric values of any time series. It can even process external datasets or the results of any data query if it can be displayed as a numeric time series, such as occurrences over time.

The forecast is created instantly, even for large data sets, and updates dynamically whenever filter settings are changed.

In application performance management, acting with foresight is paramount. Maintaining reliability and scalability requires a good grasp of resource management; predicting future demands helps prevent resource shortages, avoid over-provisioning, and maintain cost efficiency.

On this SRE dashboard, we utilize Davis AI to forecast and visualize future resource utilization:

Other potential applications for forecasting include:

• Kubernetes: Forecasting helps dynamically scale Kubernetes clusters by predicting future resource needs. This ensures optimal resource utilization and cost efficiency. Forecasting can identify potential anomalies in node performance, helping to prevent issues before they impact the system.
• Business: Using information on past order volumes, businesses can predict future sales trends, helping to manage inventory levels and effectively plan marketing strategies.

AIOps: Utilize Davis AI to predict and prevent

Utilizing the Dynatrace AutomationEngine, Davis AI forecasting capabilities can even trigger automated actions. One of our customers’ SRE teams needed to increase disk space to avoid ongoing over- and under-provisioning, which was time-consuming and annoying. Now, with Davis AI forecasting capabilities, the target disk size is predicted automatically, and an automated task for disk resizing is triggered when necessary.

If you want to further explore the possibilities for prediction and prevention management with Dashboards, have a look at our example dashboard in the Dynatrace Playground.

Experience Davis AI in action

To experience the possibilities of Davis AI, look at this short introduction video by Andreas Grabner:

The post Better dashboarding with Dynatrace Davis AI: Instant meaningful insights appeared first on Dynatrace news.

Imagine a day when your IT team resolves critical incidents before users even notice them. In today’s multicloud world, complexity is the norm. But what if an AIOps strategy could transform that complexity into your organization’s greatest advantage?

IT operations teams must monitor, maintain, and optimize a broader mix of applications, infrastructure, and technologies, with newer digital business solutions only adding to the strain. To manage it, organizations are turning to AI to help automate tasks such as anomaly detection, root-cause analysis, and incident response. But some IT operations teams are taking this approach a step further, applying multiple forms of AI to accelerate and enhance all business operations.

As AI is evolving, it’s important for organizations to understand the different types of AI and the keys to implementing them to achieve an AIOps strategy that moves from reactive to predictive problem solving.

The three kinds of AI that are key to a successful AIOps strategy

AI has evolved beyond the traditional correlation and probability-based approaches. Now, organizations turn to multiple forms of AI, such as causal, generative, and predictive AI, to manage cloud environments, secure data, and improve business decision-making. Therefore, implementing a successful AIOps strategy requires a deeper understanding of these types of AI.

Causal AI: Think of a global retail chain instantly pinpointing the root cause of a checkout slowdown across thousands of stores. Causal AI uses real-time, contextual data and causal dependencies for precise root-cause analysis and issue prevention. This establishes a business environment safeguarded by automated health monitoring and risk remediation.

Predictive AI: Imagine anticipating server outages hours before they occur, allowing seamless customer experiences. Predictive AI analyzes data patterns and trends, using statistical algorithms and other advanced machine learning techniques to anticipate future system behavior. This means technical users and business leaders can be much more proactive and innovative in the face of constant change.

Generative AI: Consider using generative AI to automate repetitive tasks, freeing up teams for innovation. Generative AI trains on large and diverse data sources, boosting business productivity and efficiency.  But when used in combination with causal and predictive AI and trained on real-time, high-fidelity observability data, generative AI can help organizations accelerate productivity and automate workflows.

The key strategy is using GenAI in conjunction with causal and predictive AI to understand your data and environment using natural conversation, and not as a way to correlate disparate events or anticipate the future. GenAI alone has limitations in these areas.

Implementing AIOps at scale

The multicloud complexity challenge demands a new approach—one that moves beyond toolchains that are stitched together. That’s where Davis AI™ comes in, combining the strengths of three AI capabilities in one: predictive, causal, and generative AI. Davis provides advanced analytics and proactive problem-solving to deliver out-of-the-box insights. Additionally, Davis doesn’t require extensive configuration or integrations. As a result, IT teams and business decision-makers can take immediate advantage of AI and scale it across the organization.

This power-of-three AI and observability approach democratizes the value of complex data analytics for both technical and nontechnical users. By unifying operations, security, development, and business teams with a single, complete, real-time view of activity across every cloud, app, and system, teams have an intuitive, self-service observability environment for problem-solving security and business events. Unified observability and AI put users in control of finding the answers they need from data, without needing to be experts in query languages.

Take your AIOps strategy from reactive to proactive

In addition to unifying teams, Dynatrace enables organizations to cut down on redundant observability tools and vendors, simplifying the overall view and improving insight coherency. And with new standards and capabilities in AI, teams can scale these efforts across the business for a more streamlined, inclusive, and collaborative business approach to resolving operational issues and driving your business forward.

Tool sprawl is an obvious problem for organizations. But choosing the wrong end-to-end observability platform can make things worse—and more expensive. Contact us today to request a demo of the AI-powered unified observability and security platform from Dynatrace and take your first step toward eliminating tool sprawl.

The post How to implement an AIOps strategy at scale appeared first on Dynatrace news.

From managing complex IT environments to ensuring seamless customer experiences, the demands on IT departments have never been greater. To manage these complexities, organizations are turning to AIOps, an approach to IT operations that uses artificial intelligence (AI) to optimize operations, streamline processes, and deliver efficiency. One Dynatrace customer, TD Bank, placed Dynatrace at the center of its AIOps strategy to deliver seamless user experiences.

Why AIOps?

AI for IT operations (AIOps) uses AI for event correlation, anomaly detection, and root-cause analysis to automate IT processes. It plays a crucial role in managing complex multicloud environments by streamlining operations and enhancing efficiency, reducing costs, and driving innovation.

Paired with an observability platform, AIOps identifies and helps to resolve cloud application performance and security issues, preventing problems before they disrupt operations. Its adoption is growing rapidly, driven by the explosion of data complexity that accompanies modern cloud IT environments. Valued at $17 billion annually, the AIOps market reflects its importance as large companies increasingly integrate AIOps and digital experience monitoring tools, with adoption expected to rise significantly in the coming years.

As a leader and trailblazer in the AIOps space, Dynatrace uses AI-powered root-cause analysis to provide precise actionable insights, enabling businesses to automate operations across the enterprise.

TD Bank adopts an observability-based AIOps strategy

As one of the 10 largest banks in the U.S., with $1.4 trillion in assets and 27 million customers, TD Bank places customers at the center of everything it does. As its enterprise monitoring team modernized the bank’s digital ecosystem from legacy on-premises data centers to a hybrid multicloud environment, TD Bank faced significant challenges with complexities its traditional monitoring tools couldn’t handle.

TD Bank’s modernized technology stack became increasingly intricate, leading to operational inefficiencies. The bank had accumulated multiple monitoring tools, each providing fragmented insights. This disjointed approach made it difficult to collaborate effectively and resolve issues promptly.

The Dynatrace unified observability platform provided TD Bank with a single source for answers, offering end-to-end visibility across its entire technology stack. Using Dynatrace at the center of its AIOps strategy, the TD Bank team reduced the number of IT incidents they were experiencing, improving customer trust.

Faster responses for greater reliability

A standout feature of Dynatrace is its ability to deliver rapid and precise answers. For TD Bank, this meant significantly reducing the time to identify and resolve transaction failures. With AI-driven certainty, the bank could instantly pinpoint the root cause of issues, leading to a 25% increase in proactive incident identification and a 20% faster response rate. This efficiency translated to a dramatic reduction in the transaction failure rate, from 0.16% to just 0.06%.

Cost optimization and efficiency

Using Dynatrace, TD Bank was able to consolidate its observability tools and achieve substantial cost savings. With its platform-based approach to end-to-end observability, Dynatrace enabled the bank to eliminate up to seven redundant monitoring solutions, reducing infrastructure and licensing costs by up to 45%. Beyond cost savings, this consolidation freed up TD Bank’s teams to focus on innovation rather than routine maintenance, driving further efficiency.

Enhanced customer satisfaction

For TD Bank, customer satisfaction is paramount. With the efficiencies stemming from Dynatrace AI capabilities, the bank reduced customer irritants by more than 60% and sped up issue resolution by 20%. With precise answers, TD Bank’s teams can quickly understand issues and resolve customer calls , enhancing the overall customer experience and building trust in the bank’s digital services.

How Dynatrace delivers on AIOps

AI-powered root-cause analysis

At the heart of Dynatrace AIOps capabilities is its power-of-three AI engine, Davis®. Using causal, predictive, and generative AI, Davis delivers detailed insights into issues, including their root cause and impact. For TD Bank, the technology has been instrumental in quickly identifying and resolving issues, ensuring minimal disruption to customer services.

Automated remediation

By automating routine tasks and responses to common issues, Dynatrace helps businesses like TD Bank achieve zero-touch operations. This automation not only improves efficiency but also ensures teams can address critical issues promptly, minimizing downtime.

Predictive analytics

Dynatrace AI-driven predictive analytics provide foresight into potential issues before they occur. For enterprises, this means staying ahead of the curve, preventing disruptions, and ensuring seamless operations. TD Bank has used these capabilities to anticipate and mitigate risks, ensuring a smooth banking experience for its customers.

The broader effect of AIOps: Transforming IT operations

AIOps is not just a tool; it’s a transformation strategy. By integrating AI into IT operations, businesses can achieve unparalleled efficiency, agility, and resilience. It is clear that the future of IT operations lies in AI, and Dynatrace is leading the charge. With its deep-rooted AI expertise and innovative AIOps platform, Dynatrace is transforming the way businesses operate. The success TD Bank has achieved demonstrates how Dynatrace helps unlock the hidden value in customer data and maximize the tangible benefits of AI-driven operations.

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As executives, we drive change, balancing modernization speed with its risks. Technology—both a blessing and a curse—not only propels businesses forward but also adds complexity as developers introduce new innovations to enhance customer services and competitiveness.

Anticipate future customers’ needs

Anticipating customer needs three to five years ahead helps to reduce wasted investments into “wants” and directs them toward “needs” that future-proof the business.

This mentality has driven me to continuously innovate and reinvent Dynatrace®. My ongoing evaluation of how technology changes the way digital services are architected allowed me to recognize early on that change is on the horizon. The rise of cloud-native technologies, the convergence of observability and security, and the demand for actionable insights required a new approach to managing data at an exabyte scale, as existing databases could no longer keep up.

Change is constant

In our fast-paced world, success requires thinking big but acting small to create value quickly and sustainably. For cloud modernization, this means executives must change how software is built, operated, and secured; improve collaboration processes; and increase automation.

Dynatrace gives executives an indispensable platform for driving this change in the following three ways:

• Enabling a modern AIOps strategy,
• Accelerating software delivery, and
• Making scarce engineering resources more productive.

1. Go beyond traditional AIOps

The first wave of AIOps investment was about “noise reduction.” This has been helpful but falls short of the potential offered by the preventive NoOps and NoSOC approaches that many executives seek AI to enable.

With current hype causing a resurrection in AI investment, it is tempting to believe that this time, machine learning and generative AI will fulfill the promises of the past. However, while the advances in machine learning-based AI are a huge step up for many use cases, it is still problematic to apply it to prevent incidents and errors in IT systems. Why? Because training an AI requires errors, failures, and behaviors to occur many times to ‘learn’. While the exact numbers may have been reduced by the advances in generative AI, which executive wants to have service outages just to train AI to prevent them in the future? Even if it was possible to arrive at a trained model, it would quickly become obsolete as services get updated and new features introduced.

As we consider a way forward, I urge all executives to recognize that we are in the trough of disillusionment in the AI hype cycle. This is good news, as it allows us to think more rationally. We need to understand that there are multiple types of AI, each suited for different purposes.

Dynatrace is uniquely designed to help executives elevate their AIOps – and AISecOps strategy – to a different level by combining multiple types of AI in a single framework known as hypermodal AI: the power of predictive AI, causal AI, and generative AI for observability, security, and business use cases. Proven by thousands of customers in large-scale IT deployments, this approach delivers greater speed, automation, and precision.

Our hypermodal AI automatically infers the root cause of issues based on a real-time updated graph without needing to learn. Now, it is more feasible than ever to automate workflows for self-healing, security investigation, and preventive operations to deliver great software with confidence, all while enhancing security measures and boosting productivity.

2. Accelerate software delivery

One of the best features of the cloud and Kubernetes® is achieving most availability needs with minimal effort, a major improvement over the classic datacenter model. This allows executives to focus on accelerating software delivery. However, the inverse Pareto principle applies: achieving the final 20% of flawless, secure services requires 80% of the effort.

That’s why APIs have become my favorite feature of the cloud as the key to automate and orchestrate. This is where Dynatrace comes in. Dynatrace integrates with the cloud ecosystem and DevOps toolchain to enhance automation across software delivery, resilience, and security throughout the software lifecycle.<

Throughout the ten years since we embraced NoOps at Dynatrace, I understood the temptation to favor releasing new features over investing in automation. Automation always paid off. We have since developed over 100 ready-made use cases to support platform engineering across the software delivery lifecycle. From development and release to operation and flaw prevention, prediction, and resolution, Dynatrace offers a robust data analytics-driven automation platform.

We’ve seen the many benefits of investing in automation, including the following capabilities:

• Releasing faster and securely with automated quality and security gates
• Catching bugs earlier, before customers experience them
• Preventing issues with predictive operations
• Avoiding unnecessary high consumption and cost with causal and predictive auto-scaling
• Empowering developers with context-rich insights derived from self-service observability and security
• Orchestrating more intelligently with real-time user behavior and business data

In a nutshell, Dynatrace allows executives to accelerate software delivery with confidence.

3. Increase teams’ productivity

As Dynatrace CTO, one of the questions constantly on my mind is: how can I enable my team to be more productive?

Over the past 15 years, most of us have embraced the “shift left” ethos to empower software developers. The earliest iteration of this was the “you build it, you run it” mentality. However, given the responsibilities of creating enterprise-scale and secure software, the “extend left” ethos proves to be more successful and fitting for cloud modernization.

“Extend left” refers to sharing responsibility amongst developers and operations teams, through adding more self-service for developers while retaining consistency, tooling and knowledge management with central teams.

As neither full decentralization nor full centralization will be effective, a hybrid model, supported by platform engineering approaches, is much more likely to succeed. Centralizing the necessary expert knowledge within a platform engineering team enables rapid, secure, and safe software delivery. At the same time, this approach decentralizes innovation, making it accessible to many.

Dynatrace was created to enable precisely this approach, leveling up developer experience by providing self-service capabilities while allowing central safety and oversight maintenance. This gives executives the best of both worlds: decentralized autonomy supported by centralized governance and control.

Armed with the use cases across the three areas outlined here, executives can modernize their cloud operations faster and equip their teams with the capabilities they need to accelerate innovation confidently. As a result, they will be better placed to anticipate change and continuously reinvent their organization to stay ahead of the market.

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Growing AI adoption has ushered in a new reality.

As organizations turn to artificial intelligence for operational efficiency and product innovation in multicloud environments, they have to balance the benefits with skyrocketing costs associated with AI. An AI observability strategy—which monitors IT system performance and costs—may help organizations achieve that balance.

The good news is AI-augmented applications can make organizations massively more productive and efficient. Indeed, according to one recent survey, 72% of employees said AI-enhanced tasks make them more productive. And an O’Reilly Media survey indicated that two-thirds of survey respondents have already adopted generative AI—a form of AI that uses training data to create text, images, code, or other types of content that reflect its users’ natural language queries.

But a downside of increasing AI adoption is organizations can face skyrocketing costs because AI—particularly generative AI—is computationally intensive and the costs increase as the amount of data the AI models are trained on grows.

Growing AI adoption brings rising cloud costs

There are three key reasons that AI costs can spiral out of control:

1. AI consumes additional resources. Running artificial intelligence models and querying data requires massive amounts of computational resources in the cloud, which results in higher cloud costs.
2. AI requires more compute and storage. Training AI data is resource-intensive and costly, again, because of increased computational and storage requirements.
3. AI performs frequent data transfers. Additional data transfer costs may occur because AI applications require more frequent data transfers between edge devices and cloud providers. These data volumes must be transferred from edge devices to the cloud.

For organizations to succeed with their AI adoption, they need to address these sources of skyrocketing costs. They can do so by establishing a solid FinOps strategy. FinOps, where finance meets DevOps, is a public cloud management philosophy that aims to control costs.

Additionally, organizations need to consider AI observability.

What is AI observability?

AI observability is the use of artificial intelligence to capture the performance and cost details generated by various systems in an IT environment. AI observability also arms IT teams with recommendations about how to curb these costs. As a result, AI observability supports cloud FinOps efforts by identifying how AI adoption spikes costs because of increased usage of storage and compute resources.

Organizations now recognize that they can’t get the business benefits from AI without paying strict attention to costs. AI observability can help them understand the ROI of their AI investments as AI hype gives way to real adoption.

According to the McKinsey Global Survey, “The state of AI in 2023: Generative AI’s breakout year,” 40% of respondents say their organizations will increase their investment in AI overall because of advances in generative AI. But as a recent Forbes Tech Council article notes, using increasingly more AI and running more data computations comes with costs.

“Like a snowball gathering size as it rolls down a mountain, the more data pipelines you have running, the more problems, headaches and, ultimately, costs you’re likely to have,” said Kunal Agarwal in “Why Optimizing Cost Is Crucial To AI/ML Success.”

Because AI observability monitors resource utilization during all phases of AI operations—from model training and inference to tracking model performance—it enables organizations to best balance between accuracy and resource efficiency and to optimize operational costs.

Best practices for optimizing AI costs with AI observability and FinOps

• Adopt a cloud-based and edge-based approach to AI. Cloud-based AI enables organizations to run AI in the cloud without the hassle of managing, provisioning, or housing servers. Edge-based AI enables organizations to run AI functions on edge devices—such as smartphones, cameras, or even sensors—without having to send the data to the cloud. By adopting a cloud- and edge-based AI approach, teams can benefit from the flexibility, scalability, and pay-per-use model of the cloud while also reducing the latency, bandwidth, and cost of sending AI data to cloud-based operations.
• Use containerization. Containerization enables organizations to package AI applications and dependencies into a single unit, which can be easily deployed on any server with the necessary dependencies. This optimizes costs by enabling organizations to use dynamic infrastructure to run AI applications instead of designing for peak load.
• Continuously monitor AI models’ performance. Once an organization trains AI models based on its data, it is important to monitor algorithm performance over time. Monitoring AI models helps to identify areas of improvement and “drift”—or the decline of models‘ predictive power as a result of the changes in real-world environments that aren’t reflected in the models. Over time, models can easily drift from these real-world conditions and become less accurate. Teams may need to adjust models to accommodate new data.
• Optimize AI models. This task goes hand in hand with continuous monitoring of models. It involves improving the accuracy, efficiency, and reliability of an organization’s AI by using techniques such as data cleaning, model compression, and data observability to ensure the accuracy and freshness of the AI results. Optimizing AI models can help save computational resources, storage space, bandwidth, and energy.
• Proactively manage the AI lifecycle. Team tasks include creating, deploying, monitoring, and updating AI applications. Managing the AI lifecycle means ensuring that AI applications are always functional, secure, compliant, and relevant by using tools and practices such as logging, auditing, debugging, and patching. Managing an AI lifecycle helps avoid technical issues, ethical dilemmas, legal problems, and business risks. It also helps organizations maintain a competitive edge and customer loyalty.
• Use generative AI in conjunction with other technologies. Generative AI is a powerful tool, but it is not a silver bullet. The true potential of generative AI comes from using it in conjunction with predictive AI and causal AI. Predictive AI uses machine learning to identify patterns in past events and make predictions about future events. Causal AI is a technique that determines the precise root causes and effects of events or behaviors. Causal AI is critical to feed quality data inputs to the algorithms that underpin generative AI. “Composite AI” brings causal, generative, and predictive AI together to elevate the collective insights of all three. With composite AI, the precision of causal AI meets the forecasting capabilities of predictive AI to provide essential context for generative AI prompts.

Making AI observability and FinOps part of an overall cloud observability strategy

As organizations continue to adopt AI, they can easily face sticker shock in running AI models.

As a result, they need to start considering how to glean cost-effective AI insight. Organizations that can evolve beyond mere AI adoption to cost-effective AI optimization will succeed in this new AI-enabled era. Organizations need to proactively monitor and manage their AI models to ensure data accuracy and cost-effective AI models.

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Predictive AI is an important technology that busy technology and operations teams can use to ensure that applications and digital systems work seamlessly and securely.

Because IT teams handle complex infrastructure to maintain service availability, they need to respond swiftly to incidents as they arise. But when these teams work in largely manual ways, they can miss the first signs of incidents as they’re building. As a result, incidents can build into outages, and teams have to respond to crises instead of innovating on strategic projects that have more value.

By integrating predictive artificial intelligence (AI) into team workflows, they can more easily meet service-level objectives, collaborate effectively, and boost productivity.

What is predictive AI?

Predictive AI is a type of machine learning (ML) that uses advanced algorithms to analyze large datasets to identify hidden patterns and relationships between variables. In data analytics, predictive AI trains ML models to learn from historical data and make predictions about future events, scenarios, or outcomes based on patterns from that data.

Organizations in finance, health care, marketing, manufacturing, and other industries widely use predictive AI. It enables organizations to make better data-driven decisions, optimize processes, and anticipate future trends. How accurate those predictions are based on historical data depends on the training data’s quality and relevance. Predictions’ accuracy also depends on the robustness of the classification and ML models, such as decision trees, neural networks, or regression algorithms.

By analyzing patterns and trends, predictive analytics helps identify potential issues or opportunities, enabling proactive actions to prevent problems or capitalize on advantageous situations.

When teams combine predictive AI with a data lakehouse such as Dynatrace Grail, it can deliver even greater value. With access to complete data sets in context, predictive AI can automatically provide prescriptive insights using data from the digital user experience layer to the infrastructure layer. This access provides full data context using supporting data, such as relationships, dependencies, and usability data within entities and events.

While investigative techniques such as root-cause analysis are essential for teams striving to understand issues that have already occurred, predictive AI techniques such as forecasting and anomaly prediction help teams preempt issues. With the advances in causal AI (that is, AI that can explain cause and effect by identifying root-cause issues), teams want to take it to the next level and combine it with predictive AI to create a seamless foresight-to-hindsight continuum of data-driven answers and prescriptive insights.

The importance of predictive AI for ITOps, DevSecOps, and SRE teams

1. Early detection of anomalies. Predictive AI empowers site reliability engineers (SREs) and DevOps engineers to detect anomalies and irregular patterns in their systems long before they escalate into critical incidents. By identifying subtle deviations in system behavior, engineers can take preemptive measures to avert potential downtime, performance issues, or security threats.
2. Proactive resource allocation. Through predictive analytics, SREs and DevOps engineers can accurately forecast resource needs based on historical data. This enables efficient resource allocation, avoiding unnecessary expenses and ensuring optimal performance.
3. Capacity planning. Understanding future capacity requirements is crucial for maintaining system stability. Predictive AI assists engineers in predicting demand fluctuations and adjusting resource capacities accordingly, ensuring seamless user experiences.
4. Enhanced incident response. Predictive analytics can anticipate potential failures and security breaches. SREs and DevOps engineers can implement targeted remediation strategies and prioritize incident response efforts to minimize the impact on systems and users.
5. Continuous improvement. By analyzing past incidents and performance metrics, predictive analytics helps SREs and DevOps engineers identify areas for improvement. This data-driven approach fosters continuous refinement of processes and systems.

Predictive AI-based capacity management and automation

Proactive capacity management is essential for avoiding outages and ensuring that an organization’s applications and services are always available. Operators need to closely observe business-critical resource capacities such as storage, CPU, and memory to avoid outages that are driven by resource shortages. However, traditional capacity management approaches are often reactive and time-consuming. Using Dynatrace Grail and Davis AI, predictive capacity management is straightforward:

• use Notebooks to explore important capacity indicators;
• create workflows to trigger forecast reporting at regular intervals; and
• use Davis AI for Workflows to automate the prediction and remediation of future capacity demands.

Benefits of predictive AI for capacity management

Predictive capacity management is a powerful tool that can help improve the availability and performance of applications and services. By using Dynatrace Grail and Davis AI, you can gain the insights you need to make proactive decisions about capacity planning and gain the following additional benefits:

• Increased visibility into future capacity demands. Predictive capacity management can help you to anticipate what your future capacity demands will likely be. This provides organizations with the ability to make proactive decisions about capacity planning, such as adding additional resources or scaling back resources that are not being used.
• Improved decision making for capacity planning. With predictive capacity management, you can make more informed decisions about capacity planning. This is because you have a better understanding of your future capacity demands and the impact of those demands on applications and services.
• Reduced costs associated with unplanned capacity increases. Unplanned capacity increases are costly. Organizations may need to purchase additional resources or pay for overtime. Predictive capacity management can reduce these costs by enabling organizations to plan for future capacity demands.
• Increased customer satisfaction. When your applications and services are available and performing well, your customers are happy. Predictive capacity management can help you to improve customer satisfaction by reducing the number of outages and performance problems.

Predictive AI helps teams avoid costly problems

This is just one example of predictive AI in action. But for ITOps, DevSecOps, and SRE teams, predictive AI presents numerous use cases for gaining foresight into issues and pre-emptively addressing them before they escalate into costly problems. They see improved efficiency, reduced risks of security breaches, and better compliance with industry regulations.

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Today’s organizations need to solve increasingly complex human problems, making advancements in artificial intelligence (AI) more important than ever. Conventional data science approaches and analytics platforms can predict the correlation between an event and possible sources. But they often fall short when it comes to understanding why an event occurred. That’s where causal AI, also referred to as deterministic AI, makes a crucial difference.

In what follows, we’ll discuss causal AI, how it works, and how it compares to other types of artificial intelligence. We’ll also discuss why it’s essential for business success in the age of generative AI.

What is causal AI?

Causal AI is an artificial intelligence technique used to determine the exact underlying causes and effects of events or behaviors. Unlike correlation-based machine learning, which calculates probabilities based on statistics, causal AI uses fault-tree analysis to determine system-level failures based on component-level failures. With this systematic, top-down approach, causal AI and modern deterministic AIOps provide a determinative basis for automatic anomaly detection, root-cause analysis, security risk ranking, and business impact assessment.

Causal AI draws on supporting data, such as relationships, dependencies, and other context among network entities and events. With this context, causal AI determines the precise root cause of an issue. This approach helps teams to develop effective models or interventions for change while also predicting their potential effectiveness. It can increase confidence in business and IT decision making by clearly connecting events to an intended or unintended outcome.

The deterministic quality of causal AI can also form the foundation for reliable recommendations from emerging generative AI technologies.

Why is causal AI important?

Most AIOps approaches use predictive analytics that apply algorithms and machine learning to historical data to predict future outcomes. Such an outcome could be a CPU spike that progresses into a system failure. Predictive analysis helps an organization manage resources and improve incident response times.

This blind spot between the underlying cause and resulting effect can lead to unwanted bias and poor decision making. Predictive analysis can observe an event and predict an outcome will occur, but it can’t show that the outcome occurred because of the event. In other words, correlation doesn’t equal causation.

Causal AI, on the other hand, identifies the underlying cause of an event and its precise relationship to the outcome. Organizations can use causal AI frameworks and algorithms to ask questions and gain a deeper understanding of their CloudOps, DevOps, and SecOps use cases. For instance, these questions can include the following:

• Why aren’t customers completing their transactions?
• What’s causing customer churn?
• Why is this application sluggish at certain times of the day?

Additionally, the deterministic AI approach of causal AI can determine the cause-and-effect relationship of events from a combination of metrics, traces, and log data, as well as user behavior data and other details. Thus, teams can resolve incidents immediately to prevent disruptions in service and keep an organization in compliance with service-level agreements.

Correlation AI vs. causal AI: Weighing the differences

Correlation-based machine learning models predict outcomes from statistical relationships and are useful in many scenarios. For example, facial recognition, personal shopping, and predictive maintenance.

However, the shortcomings of correlation-based AI become evident when teams need to determine how an action would affect an outcome. While predictive models can identify the likelihood of certain positive or negative events happening, they’re unable to explain how they arrived at that forecast. They’re also unable to identify the underlying factors and cause-and-effect relationships.

Correlation-based AI and causal AI have a few additional differences, including the following:

How does causal AI work?

Causal AI essentially works in two steps. First, it collects information and discovers problems within the data set. Then, it looks for causal relationships that help explain those issues using a plan devised from the collected data.

To better understand how causal AI works, it’s important to understand fault-tree analysis—a data-driven, fault-tree methodology used for causality analysis. Fault-tree analysis uses boolean logic to explore system-level failures. It’s a top-down approach used to identify the component-level failure, or basic event, that caused the system-level failure, or top event.

Causal AI that uses fault-tree analysis works the following way:

1. Defines the scope of the system and what’s considered a failure.
2. Defines top-level faults and the analysis starting point with details of the failure.
3. Identifies precipitating events that could cause the top-level fault to occur, whether alone or with multiple concurring events.
4. Finds the root causes of each precipitating event and event sequence.
5. Analyzes the fault tree by looking for the events that lead to failure or are most likely to fail.

With the certainty of this systematic approach, teams can gain insight into ways to mitigate paths to failure and support system improvements, and automate resolutions.

Applying causal AI to your organization

Dynatrace Davis® AI offers continuous causal analysis to the code level that maps and understands the relationships between all of an organization’s networks, applications, and services. Using fault-tree analysis, this causal AI approach seamlessly combines topological context with metric data to quickly identify observability signals for any behavior of interest. The analysis provides insights into every entity a problem affects, enabling developers to solve problems without having to reproduce errors.

With its deterministic AI approach, causal AI provides the perfect basis for automating responses and supplying facts for reliable generative AI recommendations.

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In today’s digital world, software is everywhere. And it covers more than just applications, application programming interfaces, and microservices. Software is behind most of our human and business interactions. This, in turn, accelerates the need for businesses to implement the practice of software automation to improve and streamline processes.

In what follows, we define software automation as well as software analytics and outline their importance. We also discuss the role of AI for IT operations (AIOps) and more.

What is software automation?

Software automation is the practice of creating software applications to reduce or eliminate human intervention in repetitive, time-consuming IT tasks and cloud operations. Software automation enables digital supply chain stakeholders — such as digital operations, DevSecOps, ITOps, and CloudOps teams — to orchestrate resources across the software development lifecycle to bring innovative, high-quality products and services to market faster.

What is software analytics?

Software analytics offers the ability to gain and share insights from data emitted by software systems and related operational processes to develop higher-quality software faster while operating it efficiently and securely. This involves big data analytics and applying advanced AI and machine learning techniques, such as causal AI. It provides valuable insight into complex public, private, and hybrid cloud IT structures, systems, and frameworks.

Practical applications of software analytics include the following:

• measuring the health of software systems in relation to security, efficiency, complexity, transferability, and cloud readiness;
• determining the functional and technical size of software to be developed according to technical specifications;
• detecting software flaws to prevent potential outages, data corruption or theft, and security breaches;
• visualizing software structure and private, public, and hybrid cloud architecture; and
• assessing performance by implementing software benchmarks.

Additionally, software analytics enhances the digital customer experience by enabling faster service for high-quality offerings.

Software analytics enables software automation

Software analytics enables software automation with targeted machine learning and AI algorithms that mimic the way humans think to perform repetitive tasks. These algorithms analyze massive amounts of complex structured and unstructured data to enhance human decision making and performance.

Intelligent software automation combines cognitive and AI technologies, such as natural language processing, to build smart processes and workflows that learn, adapt, and improve with every IT software instance or digital transaction. The result is increased efficiency, reduced operating costs, and enhanced productivity.

Primary intelligent software automation use cases for any business include the following:

1. Development. Automate DevOps pipelines to create better software faster to free up critical DevOps and IT time for new initiatives and innovation. Consider how AI-enabled chatbots such as ChatGPT and Google Bard help DevOps teams write code snippets or resolve problems in custom code without time-consuming human intervention.
2. Operations. Automatically predict and resolve problems before they affect users with precise, AI-powered insights and real-time remediation.
3. Business. Boost conversions and revenue with machine learning and AI-powered deep understanding to optimize user interactions.

The importance of software automation and analytics

Software automation is the backbone of digital transformation and modern IT environments. Implementing a unified software automation and analytics platform to power observability, security, and business solutions for various practices eliminates tedious, repetitive tasks, reduces human error, and frees up valuable time to focus on high-value initiatives.

Intelligent software automation can give organizations a competitive edge by analyzing historical and compute workload data in real time to automatically provision and deprovision virtual machines and Kubernetes. This way, organizations can meet employee and customer demand 24/7 while staying optimized with respect to budget and resources.

The role of AIOps in software automation and analytics

In software automation and analytics, AIOps’ role is to apply advanced machine learning and AI analytics to automate cloud operations. DevSecOps and ITOps teams can then perform tasks with accuracy at the speed a business requires.

The core benefits of an AIOps-automated software analytics platform include the following:

• Infrastructure monitoring. Instantly analyze massive volumes of observability, security, and business data for precise, AI-powered insights.
• Applications and microservices monitoring. Automatically connect distributed traces with logs for improved application availability, performance, and agility.
• Application security. Investigate network systems and application security incidents quickly for near-real-time remediation.
• Digital experience. Improve user experience with fast, reliable application performance across all digital channels. These include mobile, web, Internet of Things, and application programming interfaces.
• Business observability. Analyze business data contextually from any source to unlock new business use cases.
• Cloud automation. Automate DevSecOps processes at scale.

AIOps also increases the quality and accuracy of the information used to evaluate the performance of IT networks, systems, and cloud-native infrastructure, resulting in faster mean time to detect and mean time to repair.

How to implement software automation

Implementing software automation will be a different process with different goals for each enterprise, depending on where the organization is on its path to digital transformation. There is no one-size-fits-all approach, but there are common goals, such as optimizing automation, self-healing, vulnerability management, and observability across the software development lifecycle.

One way to accomplish this is by implementing AIOps as part of an organization’s larger cloud adoption strategy. AIOps intelligent cloud automation provides self-service cloud observability and monitoring-as-code approaches that allow DevSecOps, ITOps, and business teams to build feedback loops into their applications automatically with just a few clicks.

Upgrade your data insights with a lakehouse for software and business data

Implementing AIOps intelligent software automation as part of any cloud adoption strategy is critical to driving innovation and achieving faster time to market for software products and digital services. Powered by big data, adding a causational data lakehouse approach to the increasing volumes of data provides greater capacity to cost-efficiently unify, store, and contextually analyze this big data in real time and near-real time.

The Dynatrace unified software automation and analytics platform, featuring the Davis AI engine, can help an organization jump-start its automation journey with precise, AI-driven insights, root-cause analysis, automated remediation, and intelligent full-stack observability.

The post What is software automation? Optimize the software lifecycle with intelligent automation appeared first on Dynatrace news.

Kubernetes has become the leading container orchestration platform for organizations adopting open source solutions to manage, scale, and automate application deployment. Adopting this powerful tool can provide strategic technological benefits to organizations — specifically DevOps teams. At the same time, it also introduces a large amount of complexity. This complexity has surfaced seven top Kubernetes challenges that strain engineering teams and ultimately slow the pace of innovation.

What is Kubernetes? And how does it benefit organizations?

Kubernetes is an open source container orchestration platform for managing, automating, and scaling containerized applications.

Containerized microservices have made it easier for organizations to create and deploy applications across multiple cloud environments without worrying about functional conflicts or software incompatibilities. This ease of deployment has led to mass adoption, with nearly 80% of organizations now using container technology for applications in production, according to the CNCF 2022 Annual Survey. However, as the use of containers has grown, so too has the need for more effective management of these highly distributed environments at scale.

To manage this complexity, teams have turned to container orchestration solutions such as Kubernetes. The platform aims to help DevOps teams optimize the allocation of compute resources across all containerized workloads in deployment.

While a handful of these solutions exist, Kubernetes has become the de facto industry standard, offering the following benefits:

• Container deployment. Container orchestration platforms automate daily operations for processes such as the re-creation of failed containers and rolling deployments. This helps to avoid downtime for end users.
• Automated scaling. Kubernetes enables efficient resource utilization by easily scaling applications and services based on demand.
• Self-healing. The platform will automatically restart, replace, or kill failed containers, as well as reschedule unhealthy pods and manage node failures. This key feature helps in maintaining availability and reduces the need for manual intervention.
• Extensibility and technology ecosystem. As an open source solution, Kubernetes has a large, active, and growing ecosystem of extensions, plug-ins, and technologies to enhance its capabilities. The ability to extend functionality across a wide range of use cases allows teams to tailor the platform to meet their organization’s specific requirements.

The top Kubernetes challenges and potential solutions

Despite its benefits, Kubernetes has some potential pitfalls that engineering leaders should consider when managing the complexity it introduces. The top seven Kubernetes challenges include the following:

1. Complexity. Kubernetes environments tend to be complex, multilayered, and dynamic. This creates limitations and blind spots when it comes to observability. Teams often need to know where to look to find issues and resolve them, but that can be time-consuming in large-scale deployments. Comprehensive platform solutions that provide full-stack monitoring with AI at their core can automatically detect anomalies and provide root-cause analysis to prevent future issues.

2. Networking. Large-scale, multicloud deployments can introduce challenges related to network visibility and interoperability. Traditional ways of operating networks using static IPs and ports simply don’t work in dynamic Kubernetes environments. Container Network Interface (CNI) provides a common way to seamlessly integrate various technologies with the underlying Kubernetes infrastructure. Additionally, service meshes — such as those offered by Istio, Linkerd, and Consul Connect — help to manage internetwork communication at the platform layer using purpose-built application programming interfaces.

3. Observability. While there are many observability and monitoring tools on the market today, most are specific in nature. A full-stack observability platform such as Dynatrace provides easy access to the three key types of monitoring signals — logs, traces, and metrics — in context. Automated data collection correlated with topology information, real-user experience, security events, and metadata makes it easier to identify issues and determine effective remediation paths. With the ability to monitor resource utilization metrics such as CPU and memory in real time, teams can optimize their operations, resulting in reduced cost and greater overall efficiency.

4. Cluster stability. Kubernetes containers are naturally short-lived and ephemeral, meaning they’re constantly being created, altered, and removed. This creates challenges in monitoring and debugging distributed applications at scale, often resulting in reliability issues. Effective monitoring, logging, and tracing mechanisms need to be in place to identify and resolve issues quickly. Additionally, ensuring cluster stability through monitoring critical components at the control plane is essential to preventing failures. To minimize negative effects, consider setting limits and alerts on CPU and memory resource requests.

5. Security. Kubernetes security incidents are primarily related to pod communications or misconfigurations that ultimately lead to delayed application deployment. Pods are not isolated, which leaves them vulnerable to malicious actors. Bad actors can use misconfigurations to gain access to sensitive data. However, Kubernetes configurations are complicated, which makes managing them at scale almost impossible. Network policies can restrict pod communications, and teams can use pod security policies to ensure pods are securely configured.

6. Logging. Logs provide critical visibility into the ongoing health of Kubernetes clusters. While Kubernetes makes it easier to generate logs from the various components and layers of a cluster, challenges remain in aggregation and analysis. Solutions that offer log management and analysis capabilities can help to streamline these efforts and provide actionable insights to teams maintaining the health of the workloads and underlying infrastructure.

7. Storage. Containers need to spin up and down easily. Therefore, they are built to be non-persistent by design. However, applications require persistent data to run successfully in production. Traditional storage solutions were not created to address these requirements, which are common among modern deployments. To ease the pain of managing storage at scale within a Kubernetes environment, Kubernetes has released features such as Container Storage Interface (CSI), StatefulSets, Persistent Volume (PV), and Persistent Volume Claim (PVC).

How a cloud-native observability and security platform can overcome Kubernetes challenges

To adequately address the complexity introduced by Kubernetes and other cloud-native technologies, organizations need more complete solutions that provide end-to-end, full-stack visibility, advanced performance and security analytics, and automated workflow capabilities all in a single, comprehensive platform.

Dynatrace integrates extensive Kubernetes observability with continuous runtime application security. This combination helps organizations more effectively meet business goals and minimize risk. The Dynatrace platform offers the following benefits:

• Automated observability at scale. As organizations deploy Kubernetes clusters across on-premises, cloud, and edge environments, end-to-end observability becomes mandatory. Dynatrace OneAgent and open ingest deliver the deepest and broadest observability on the market, with hundreds of out-of-the-box integrations covering the complete Kubernetes ecosystem. Dynatrace Grail unifies observability, security, and business data at a limitless scale for any analysis at any time.
• AI-powered analytics. Dynatrace provides precise and explainable answers in real time, identifying performance issues and anomalies across the full Kubernetes technology stack. Davis, the Dynatrace AI engine, helps organizations understand and optimize Kubernetes platform health and application performance, enabling IT teams to proactively pinpoint and rectify performance issues.
• Platform and application security. Optimized for Kubernetes, Dynatrace Application Security automatically and continuously detects vulnerabilities and protects against injection attacks that exploit critical vulnerabilities, such as Log4Shell. These capabilities remove blind spots, ensuring development teams aren’t wasting time chasing false positives and providing business leaders with confidence in the security of their organizations’ applications.
• Acceleration of innovation. Dynatrace enables IT teams to shift their effort from code maintenance and troubleshooting to innovation. Leveraging Dynatrace’s analytics and workflow automation enhances software quality, minimizes the mean time to resolve anomalies, and detects security vulnerabilities in near-real time.