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Some retailers report significant improvements, such as a notable reduction in lost sales (due to better in-stock availability) and lower inventory holding costs, after implementing AI-driven demand forecasting tools. Personalized customer experiences are another domain where agentic AI is making waves. E-commerce platforms and brick-and-mortar retailers alike are using AI to tailor the shopping experience to each customer. Recommendation engines, powered by machine learning, analyze browsing and purchase history to suggest products a customer is likely to want, increasing cross-sell and upsell opportunities. In-store, some retailers have experimented with AI-driven personalized promotions — for instance, a loyalty app that greets a customer when they enter and offers a tailored discount based on their past purchases. Chatbots on retail websites serve as personal shopping assistants, handling customer queries about product details, checking stock at nearest stores and even helping with the checkout process. These chatbots operate continuously and can handle multiple customers at once, significantly enhancing online customer service responsiveness. Supply chain and logistics operations in retail also gain efficiency through agentic AI. From warehouse management to delivery routing, AI systems can optimize each step. In warehouses, AI-driven robots (a physical manifestation of agentic solutions) can autonomously pick and move goods, guided by algorithms that optimize picking routes and storage organization. When it comes to delivery, AI can plan logistics and delivery routes for shipments to minimize transit times and costs, accounting for traffic conditions and fuel usage. For global retailers, autonomous agents monitor supply chain risks – for example, by analyzing news and alerts, an AI agent might warn of a potential delay due to a port strike or a factory issue, prompting the retailer to re-route shipments or find alternate suppliers proactively. source

Almost daily, teams have requests for new tools—for database management, CI/CD, security, and collaboration—to address specific needs. Increasingly, those tools involve AI capabilities to potentially boost productivity and automate routine tasks. But proliferating tools across different teams for different uses can also balloon costs, introduce operational inefficiency, increase complexity, and actually break collaboration. Moreover, tool sprawl can increase risks for reliability, security, and compliance. As an executive, I am always seeking simplicity and efficiency to make sure the architecture of the business is as streamlined as possible. Here are five strategies executives can pursue to reduce tool sprawl, lower costs, and increase operational efficiency. Key insights for executives: Increase operational efficiency with automation and AI to foster seamless collaboration: With AI and automated workflows, teams work from shared data, automate repetitive tasks, and accelerate resolution—focusing more on business outcomes. Unify tools to eliminate redundancies, rein in costs, and ease compliance: This not only lowers the total cost of ownership but also simplifies regulatory audits and improves software quality and security. Break data silos and add context for faster, more strategic decisions: Unifying metrics, logs, traces, and user behavior within a single platform enables real-time decisions rooted in full context, not guesswork. Minimize security risks by reducing complexity with unified observability: Converging security with end-to-end observability gives security teams the deep, real-time context they need to strengthen security posture and accelerate detection and response in complex cloud environments. Simplify data ingestion and up-level storage for better, faster querying: With Dynatrace, petabytes of data are always ”hot” for real-time insights, at a “cold” cost. No delays and overhead of reindexing and rehydration. 1. Increase operational efficiency to foster seamless collaboration ❌ Reinventing the wheel: One of the biggest challenges organizations face is connecting all the dots so teams can take swift action that’s meaningful to the business. Too many signals from point solutions and DIY tools spread across multiple teams hinder collaboration. Moreover, inconsistency in the tech stack and a lack of enterprise-ready integration and authentication approaches means teams must reinvent the wheel, forcing repeated builds and solving the same problems, instead of focusing on delivering business goals. ✅ Automate and collaborate on answers from data: By uniting data from across the organization in a single platform, teams can focus on making faster, high-quality decisions in a shared context. With AI they can trust, teams can understand the real-time context of digital services, enabling automation that can predict and prevent issues before they occur, such as service-level violations or third-party software vulnerabilities. The Dynatrace AutomationEngine orchestrates workflows across teams to implement automated remediations, while with AppEngine, teams can tailor solutions to meet custom needs without creating silos. 2. Unify tools to rein in costs and ease compliance ❌ High costs: Organizations often feel the pain of tool sprawl first in the pocketbook. Multiple tools increase the total cost of ownership through the sum of license fees, reduced negotiation power, and redundant maintenance and operations efforts. For example, organizations typically utilize only 60% of their security tools. Too many tools and DIY solutions also complicate regulatory compliance and make integrations harder, which reduces agility and drives up costs through wasted time. ✅ Business-focused, unified platform approach: A unified platform approach enables platform engineering and self-service portals, simplifying operations and reducing costs. The Dynatrace AI-powered unified platform has been recognized for its ability to not only streamline operations and reduce costs but also to provide better, faster data analysis. Standardizing platforms minimizes inconsistencies, eases regulatory compliance, and enhances software quality and security. Dynatrace integrates application performance monitoring (APM), infrastructure monitoring, and real-user monitoring (RUM) into a single platform, with its Foundation & Discovery mode offering a cost-effective, unified view of the entire infrastructure, including non-critical applications previously monitored using legacy APM tools. 3. Break data silos and add context for faster, more strategic decisions ❌ Data silos: When every team adopts their own toolset, organizations wind up with different query technologies, heterogeneous datatypes, and incongruous storage speeds. Last year Dynatrace research revealed that the average multi-cloud environment spans 12 different platforms and services, exacerbating the issue of data silos. Worsened by separate tools to track metrics, logs, traces, and user behavior—crucial, interconnected details are separated into different storage. It becomes practically impossible for teams to stitch them back together to get quick answers in context and make strategic decisions. ✅ All data in context: By bringing together metrics, logs, traces, user behavior, and security events into one platform, Dynatrace eliminates silos and delivers real-time, end-to-end visibility. The Smartscape® topology map automatically tracks every component and dependency, offering precise observability across the entire stack. Davis®, the causal AI engine, instantly identifies root causes and predicts service degradation before it impacts users. Generative AI enhances response speed and clarity, accelerating incident resolution and boosting team productivity. Fully contextualized data enables faster, more strategic decisions, without jumping between tools or waiting on correlation across teams. This unified approach gives teams trustworthy, real-time answers, which is critical for navigating today’s complex digital ecosystem. 4. Strengthen security with unified observability ❌ On average, organizations rely on 10 different observability solutions and nearly 100 different security tools to manage their applications, infrastructure, and user experience. Traditional network-based security approaches are evolving. Enhanced security measures, such as encryption and zero-trust, are making it increasingly difficult to analyze security threats using network packets. This shift is forcing security teams to focus instead on the application layer. While network security remains relevant, the emphasis is now on application observability and threat detection. As a result, many organizations are facing the burden of managing separate systems for network security and application observability, leading to redundant configurations, duplicated data collection, and operational overhead. ✅ The convergence of security and observability tools is becoming essential, especially for cloud- and AI-native projects, as traditional network-based security approaches evolve. Platforms such as Dynatrace address these challenges by combining security and observability into a single platform. This integration eliminates the need for separate data collection, transfer, configuration, storage, and analytics, streamlining operations and reducing costs. From a security risk mitigation perspective, integrating security and observability not only

As enterprises embrace more distributed, multicloud, and applications-led environments, DevOps teams face growing operational, technological, and regulatory complexity, along with rising cyberthreats and increasingly demanding stakeholders. Meanwhile, cost reduction programs affect budgets, constrain technology investment, and inhibit innovation. While 77% of SME IT admins want a single tool to do their job, organizations continue to impose a wide range of tools on them. Retaining multiple tools generates huge volumes of alerts for analysis and action, slowing down the remediation and risk mitigation processes. On top of this, organizations are often unable to accurately identify root causes across their dispersed and disjointed infrastructure. It’s an issue that shows no sign of going away, with 88% of organizations saying the complexity of their technology stack has increased in the past 12 months, and 51% saying it will continue to increase. In such a fragmented landscape, having clear, real-time insights into granular data for every system is crucial. For this reason, end-to-end observability that offers a holistic understanding of problems and their impact on application performance is rising in prevalence across organizations of all sizes and industries. But first, there are five things to consider before settling on a unified observability strategy. 1. What is prompting you to change? Efforts toward business optimization and cloud modernization will almost certainly be met with some resistance from team members and stakeholders who desire the status quo. For this reason, it is imperative to communicate and establish your primary motivations for making such changes across the enterprise and beyond. Selling key stakeholders on moving away from tools they know and are comfortable using can be a challenge, as certain teams will likely have their own fixed ideas about the ideal approach. To obtain organizational buy-in for change, you will need to convince teams that it’s worth changing the way they work to provide better outcomes for the business — and their career. The most effective way to accomplish this is to tie outcomes to customer experiences. Your observability solution should provide a demonstrable improvement for the customer. If it passes impact assessments and has a compelling ROI, then stakeholders are more likely to support it. Our recommendation: Consider the outcomes you’re looking to deliver and the outcomes you want to see in your tool consolidation journey. Then, document the specifics of your desired end state. 2. What does your current estate consist of? Evaluate your tool and platform portfolio against organizational requirements to identify which tools are essential because they affect the user experience or other key business outcomes, and which are no longer relevant. Keep only those tools with unique value that contribute directly to your objectives to eliminate confusion and declutter your toolchain. Draw up a list of all current tools and the main features your team uses. If you identify duplication, redundancy, or a tool that not everyone uses, such tools are candidates for elimination. Collaborate with your teams to identify any pain points and use that to guide your decision. Our recommendation: Determine the tools you have, any related contracts, and what each tool is accomplishing for the team using it. This will enable you to accurately assess and trim down your tech stack. Also, take special note of duplicate spend with vendors that have similar capabilities, as this will be a target area for cost savings. 3. What do you want your future estate to look like? To take advantage of tool consolidation and observability successfully, you first need to have a clear vision of the desired end state of your future estate. Think about mapping dependencies and their capacity to enable advanced causal, predictive, and generative AI for driving advanced levels of automation. AI and machine learning can be used to gain deeper insights into your data, improve business outcomes, and help you pull ahead of the competition. You also need to focus on the user experience so that future toolchains are efficient, easy to use, and provide meaningful and relevant experiences to all team members. Think also about the role of cloud-native solutions and how your consolidation strategy will incorporate tools that work seamlessly in cloud environments and help your organization modernize. Don’t forget to incorporate cybersecurity and sustainability measures. Your future estate should be capable of faster threat detection and reduced time to respond to incidents. Our recommendation: Plan for consolidation, integration, and automation while staying focused on your goals. 4. How do you make this happen? Modernizing your technology stack will improve efficiency and save the organization money over time. However, a single, unified platform approach is crucial to reap these benefits. This approach streamlines licensing, which means less time handling contract renewals, which finance will appreciate. It refocuses resources on high-value tasks rather than managing legacy tools. The process should include training technical and business users to maximize the value of the platform so they can access, ingest, analyze, and act on the new observability approach. Our recommendation: Explore solutions, experiment with integrations, and deliver the final tool consolidation product. 5. How do you make your changes stick — and prevent future tool sprawl? As you seek to consolidate, streamline, and modernize, commit to finding solutions that are easy to integrate and deliver not just data, but clear, actionable answers from that data. The biggest gains will come from enhancing the reliability of your application environments during peak usage and bolstering their resilience to performance degradations, which will improve user experience. Seek out solutions that leverage real-time analytics to automatically understand how application and infrastructure conditions are changing, where new demand is coming from, and when service-level objectives are not being met. This should help you proactively address issues in real time and eliminate manual, error-prone processes. Our recommendation: There are always ways to make things better for your team and end users. The right unified solution, training, and reinforcement from leadership will make teams less inclined to adopt single-use tools and fall back into tool sprawl. Moving forward with a unified observability platform In summary, centralizing on a single, unified observability platform can help

Ransomware remains one of the most persistent threats facing enterprises and public sector organizations. The latest research from ThreatLabz confirms that attacks are not only increasing in volume, but also shifting toward more targeted, data-driven extortion tactics. The newly released Zscaler ThreatLabz 2025 Ransomware Report examines year-over-year spikes in ransomware activity blocked by the Zscaler cloud and a significant rise in public extortion cases. Together, these findings point to a critical reality: today’s ransomware threat landscape demands a new level of operational vigilance and a fundamentally different security architecture than traditional security models provide. This blog post highlights select insights from the report. For the full analysis—including attack trends, threat actor profiles, and security guidance—download the ThreatLabz 2025 Ransomware Report. 5 key ransomware findings ThreatLabz researchers analyzed ransomware activity from April 2024 to April 2025, looking at public data leak sites, Zscaler’s proprietary threat intelligence, ransomware samples, attack data, and telemetry from the Zscaler Zero Trust Exchange. Here are five important takeaways from this year’s report: 1. Ransomware attacks skyrocketed 145.9% year-over-year: This dramatic growth makes it clear that attackers are scaling campaigns faster than ever, with Zscaler blocking an unprecedented number of ransomware attacks over the past year.  2. Public extortion cases increased 70.1%: Far more organizations were listed on ransomware leak sites year-over-year as attackers escalate pressure tactics. 3. Data exfiltration volumes surged 92.7%: ThreatLabz analyzed 10 major ransomware families, uncovering a total of 238.5 TB of data exfiltrated—evidence that data theft is fueling extortion campaigns. 4. Critical industries continue to be prime targets: Manufacturing, Technology, and Healthcare experienced the highest number of ransomware attacks, while sectors like Oil & Gas (+935%) and Government (+235%) saw notable year-over-year spikes. Zscaler 5. Ransomware groups are evolving fast: Established families like RansomHub, Clop, and Akira remained dominant, while 34 new groups emerged as identified by ThreatLabz—including rebrands or offshoots of defunct groups and new groups looking to fill the void left by takedowns or other disruptions. Collectively, they reflect a dynamic, fast-moving ransomware ecosystem where threat actors continually adapt. Now trending: extortion over encryption, GenAI usage, and strategic targets The ThreatLabz 2025 Ransomware Report covers several defining trends in how ransomware attacks are executed today: In many cases, data extortion is the priority. Some attackers are skipping file encryption altogether, opting to steal sensitive data and use the threat of public exposure via leak sites. These campaigns hook onto the threat of reputational damage, regulatory violations, and loss of intellectual property to pressure victims to pay even when their data isn’t encrypted. Generative AI is accelerating ransomware operations. ThreatLabz uncovered evidence of how one notorious threat group used ChatGPT to support the execution of its attacks. GenAI enables attackers to automate key tasks and write code to streamline operations and improve their attacks’ effectiveness. Targeting is more personalized. Ransomware threat actors have largely shifted away from traditional large-scale spam campaigns that are opportunistic in favor of more tailored attacks that impersonate IT staff to target employees with privileged access. Read the full report for deeper insights into these trends. Ransomware groups behind the surge The report also offers a detailed look at threat groups driving the recent escalation in attacks. Among the most prolific over the past year were RansomHub, Clop, and Akira, responsible for a large share of leak site victims. ThreatLabz researchers also identified the top five ransomware groups to watch over the next year—families that exemplify how ransomware is becoming more scalable and focused on extortion outcomes. Tactics vary widely across groups. ThreatLabz observed strategies such as: Stealthy data theft that avoids disrupting business continuity  Affiliate-driven Ransomware-as-a-Service campaigns using shared infrastructure, tools, and services Ransom demands that exploit regulatory violations to intensify pressure on victims  Vulnerabilities remain the easy way in A constant and defining theme in ransomware attacks is the role of vulnerabilities as direct pathways to initial compromise. Widely-used enterprise technologies—including VPNs, file transfer applications, remote access tools, virtualization software, and backup platforms—continue to be weaponized by ransomware operators. The report details examples of several major vulnerabilities exploited in ransomware campaigns over the past year. In most cases, attackers gained initial access through simple scanning and automated exploitation of internet-connected systems. This reinforces a hard truth: traditional defenses like firewalls and VPNs leave too much exposed, creating ideal conditions for lateral movement, data theft, and ransomware deployment. Zero trust: the standard for stopping ransomware As ransomware groups continue to evolve their playbooks—targeting sensitive data and exploiting reputational and regulatory pressure to strengthen their extortion leverage—defending against these attacks requires a comprehensive, proactive approach. This is exactly what a zero trust architecture delivers, eliminating the very conditions ransomware threat actors rely on: discoverable infrastructure, overly permissive access, and uninspected data flows. The Zscaler Zero Trust Exchange delivers protection at every stage of the ransomware attack chain, including: Minimize exposure: The Zero Trust Exchange makes users, devices, and applications invisible from the internet—no public IPs, no exposed networks. This eliminates the attack surface during the earliest reconnaissance phase and dramatically reduces risk. Prevent initial compromise: Inline inspection of all traffic, including encrypted TLS/SSL traffic, at scale stops threats before they can cause damage. AI-driven browser isolation and cloud sandboxing add multiple layers of defense to neutralize zero-days and advanced threats—powered by advanced threat intelligence from ThreatLabz. Eliminate lateral movement: App-to-app and user-to-app segmentation enforces least-privilege access and eliminates the network from the equation, removing the paths ransomware operators rely on to spread. Integrated deception technology further disrupts attacks with decoys and false user paths. Block data exfiltration: Since today’s ransomware groups are just as (if not more) focused on stealing sensitive data as encrypting it, Zscaler’s unmatched inspection, AI-powered data classification, inline data loss prevention (DLP), and browser isolation are essential to prevent unauthorized data transfers and ensure sensitive data never leaves the organization. With a zero trust architecture in place, organizations can control what users access, how data moves, and how resources are protected—effectively shutting down the pathways ransomware depends on and reducing risk at

In addition, the simulation via Digital Twin will enable data-based decisions to be made in future, for example on the optimum storage strategy, the integration of just-in-sequence supplies and the optimum production and picking sequence. The digital twin will thus become a strategic tool for future-proof, flexible and efficient intralogistics, the electronics group announced. Warehouse as a decoupling buffer Siemens is implementing the “goods to people” principle in its new warehouse. Siemens AG The warehouse serves as a decoupling buffer between the customer-anonymous prefabrication of the stator and rotor and the order-specific assembly. According to Siemens, this decoupling enables greater flexibility and responsiveness in production. By buffering the stator and rotor, fluctuations in demand or disruptions in the upstream processes can be better absorbed without affecting the final assembly. In addition, throughput times in final assembly are shortened as the required parts are provided from the warehouse just-in-time in the production cycle via a fast-picking zone with 21 picking ports. According to the company, around 3,100 pallets and more than 3,800 transport containers can be moved every day in three-shift operation. source

Nadella isn’t the only software expert sounding the alarm about the future of the SaaS market in the age of AI agents. A couple of months after his appearance on BG2, Greg Isenberg, CEO of internet portfolio company Late Checkout, laid out his predictions in a LinkedIn post, saying he predicts an AI agent revolution within the next 18 months as AI moves from co-pilot functionality to being an autonomous operator. “The dam breaks when someone can say, ‘Analyze our Q2 performance,’ rather than clicking through Tableau, or ‘Optimize our ad campaigns,’ instead of navigating Meta’s ad manager,” he writes. “The expertise previously bundled with the software gets unbundled by agents.” Then, within three years, software becomes increasingly invisible, Isenberg adds. “The final phase happens when the agents bypass the human interfaces altogether,” he continues. “The value proposition of SaaS, bundling software, workflow, and expertise into user-friendly interfaces unravels completely. The interfaces were designed for humans, but agents don’t need them.” source

I’ve spent 15 years in IT executive leadership, watching the role of the CIO evolve from a back-office function to a board-level strategic partner. Early in my career, technology was often seen as a utility; a tool that kept the lights on and the ledgers balanced. Today, younger executives treat technology as the very fabric of their social and professional lives, seamlessly weaving it into every customer interaction and operational process. Meanwhile, some organizations still regard IT as optional, a line item to justify rather than an investment to pursue. As CIOs, it’s our responsibility to bridge that divide, planning for the long term and embedding technology at the heart of the enterprise. We must shift culture from “proving” tech’s value to embracing it as core. This is not about adopting technology for its own sake; every initiative must deliver positive NPV and ROI, but about changing the question from “if” to “when.” Recognize the critical tipping point  Earlier in my career, I worked with an organisation whose newly appointed CEO regarded IT chiefly as a cost centre and was hesitant to prioritize digital initiatives. In contrast, three of his direct reports, each under 40, live and breathe technology, treating it as instinctively as they do their mobile devices and wearables. They had already encouraged me to champion AI and other digital solutions that halved processing times. source

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. Key insights for executives AI innovation and sustainability goals can go hand in hand. As AI workloads surge—projected to exceed 50% of cloud compute by 2028—organizations must balance innovation with cost and environmental impact. Dynatrace enables both by optimizing cloud usage in real time. AI-powered observability empowers teams to cut waste, reduce emissions, and align spending with business value. By providing deep insights into idle resources, inefficient architecture, and energy-heavy workloads. Dynatrace improves efficiency and supports sustainability goals by dynamically scaling resources based on real-time data demand and business goals. 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 are 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 Figure1: Dynatrace Cost & Carbon Impact homepage Dynatrace 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. Want to learn more about all 9 use cases? Visit us here. source

As a technology executive, you’re aware that observability has become an imperative for managing the health of cloud and IT services. You may not be aware of how much untapped value is waiting to be unlocked through the right observability platform. Data with context can improve your ability to deliver on your goals, modernize your organization, and accelerate business transformation. The Dynatrace platform enables executives to drive change faster, increase IT and R&D productivity, reduce business risks, optimize costs, and decrease carbon footprint. These outcomes are made easy through the platform’s unique ability to turn data into answers and action, in contextual, real-time, and cost-effective ways that were previously impossible. Unearthing a goldmine of value As founder and CTO of Dynatrace, I must constantly drive change. I also have the privilege of being “customer zero” for our platform, which enables me to continually discover where Dynatrace can deliver on more use cases to drive my team’s productivity and innovation. Change is my only constant. Realizing that executives from other organizations are in a similar situation to mine, I want to outline three key objectives that Dynatrace’s powerful analytics can help you deliver, featuring nine use cases that you might not have thought possible. Dynatrace Drive innovation To remain competitive, executives are seeking productivity gains while simultaneously driving modernization initiatives. Observability data presents executives with new opportunities to achieve this by creating incremental value for cloud modernization, improved business analytics, and enhanced customer experience. However, technology executives face a significant challenge getting answers in time, as their needs have evolved to real-time business insights that enable faster decision-making and business automation. Exploding volumes of data must be prepared, catalogued, and stored in multiple, disconnected tools. The data must then be retrieved from data lakes and converted into rigid schemas. It can take data analysts months to extract insights and answer executives’ questions using these approaches. With the latest advances from Dynatrace, this process is instantaneous. Unlike anything before, contextual analytics in Dynatrace provides answers to any question at any time, instantaneously. That’s because it does not require any pre-prepared schemas, and access to cold/hot storage is fully automatic and with zero latency. Moreover, it is fast, powered by its massively parallel processing data lakehouse. As a result, organizations can reduce complexity, effort, and processing time to run powerful business analytics on exabytes of data in real time. Dynatrace enables executives to drive a stronger, data-driven organization by increasing automation and productivity. Mitigate risk To cope with serious business risks —including major outages, security breaches, or missing out on realizing AI’s value — executives require a modern, proactive approach. Dynatrace analytics capabilities, powered by hypermodal AI, enable executives to drive improved availability, strengthened security compliance, and heightened confidence in AI initiatives. Executives are shifting to proactive risk management, aiming to prevent availability issues and expedite remediation. However, AI introduces new risks, such as increased software complexity, accelerated cyber-attacks, and potential regressions from rapid releases. Siloed teams and the reliance on disparate tools lead to manual intervention and delays, which are unsustainable given tightening regulations, including DORA, NIS2, and the SEC’s four-day reporting rule. Dynatrace uniquely solves this conundrum, enabling executives to use a new generation of AIOps and SecOps to predict and mitigate risk, rather than reacting to availability and security incidents. It does this by combining causal, predictive, and generative AI to uncover the deep context of issues using a unified source of observability and security data. Automated root-cause analysis and real-time risk analysis are only two examples that help executives get closer to the vision of self-healing operations and security. Optimize cost With the constant pressure to do more with less — or much more, much faster — executives must control cost and complexity. Dynatrace can help executives achieve these goals by reducing tool sprawl, driving cost optimization, and meeting their sustainability goals. Optimizing costs is a proven way to free up budgets for innovation. Young talent (our future executives) has a valid interest beyond making more money, as sustainability and green coding are vital to protecting both their own and our future. As new waves of technology roll over us, executives are struggling to keep tool sprawl under control. Tool sprawl not only goes deep into our pockets but also hampers consistency and productivity. Tens or even hundreds of DIY and commercial tools are being used to handle logs, metrics, traces, security events, and vulnerabilities, all in their own way. Insights are therefore dispersed in a multitude of data lakes, storage systems, and reporting platforms. This is inefficient and creates avoidable risks. The principle of “keep it simple, stupid” is more important than ever, translating to consolidating tools and making processes more consistent at higher grades of scalability and automation. Dynatrace is uniquely placed to meet this need as it consolidates tools, storage, data, processing, and automation capabilities together in a single, unified platform. This reduces the number of moving parts and eliminates process inconsistencies, driving team productivity and increasing software delivery quality and security. As a result, organizations can streamline processes by moving towards platform engineering and developer self-service portals to unburden engineers while increasing software quality and security with higher consistency. Discover more at Dynatrace for Executives. source