In APMdigest's 2026 Observability Predictions Series, industry experts offer predictions on how Observability and related technologies will evolve and impact business in 2026. Part 7 covers Observability data ...

In APMdigest's 2026 Observability Predictions Series, industry experts offer predictions on how Observability and related technologies will evolve and impact business in 2026. Part 2 covers predictions about Observability and AIOps ...

The Holiday Season means it is time for APMdigest's annual list of predictions, covering Observability and other IT performance topics. Industry experts — from analysts and consultants to the top vendors — offer thoughtful, insightful, and often controversial predictions on how Observability, AIOps, APM and related technologies will evolve and impact business in 2026 ...

The rise of hybrid cloud environments, the explosion of IoT devices, the proliferation of remote work, and advanced cyber threats have created a monitoring challenge that traditional approaches simply cannot meet. IT teams find themselves drowning in a sea of data, struggling to identify critical threats amidst a deluge of alerts, and often reacting to incidents long after they've begun. This is where AI and ML are leveraged ...

Three practices, chaos testing, incident retrospectives, and AIOps-driven monitoring, are transforming platform teams from reactive responders into proactive builders of resilient, self-healing systems. The evolution is not just technical; it's cultural. The modern platform engineer isn't just maintaining infrastructure. They're product owners designing for reliability, observability, and continuous improvement ...

Traditional monitoring often stops at uptime and server health without any integrated insights. Cross-platform observability covers not just infrastructure telemetry but also client-side behavior, distributed service interactions, and the contextual data that connects them. Emerging technologies like OpenTelemetry, eBPF, and AI-driven anomaly detection have made this vision more achievable, but only if organizations ground their observability strategy in well-defined pillars. Here are the five foundational pillars of cross-platform observability that modern engineering teams should focus on for seamless platform performance ...

In today's fast-paced AI landscape, CIOs, IT leaders, and engineers are constantly challenged to manage increasingly complex and interconnected systems. The sheer scale and velocity of data generated by modern infrastructure can be overwhelming, making it difficult to maintain uptime, prevent outages, and create a seamless customer experience. This complexity is magnified by the industry's shift towards agentic AI ...

The biggest change in Cloud Managed Services 2.0 is how it unites domains that once operated in isolation. CloudOps, FinOps, DevOps, SecOps, and AIOps now work as a single, cohesive team instead of separate departments competing for resources and priorities. This matters because modern businesses operate at a pace that leaves traditional methods behind ...

For years, IT Operations has been caught in a loop of reacting to incidents after they've already caused disruption. Even with monitoring, observability, and AIOps 1.0 (legacy AIOps solutions) in place, teams still face overwhelming alert volumes, fragmented data, and slow mean time to resolution (MTTR). The move to Predictive IT Operations offers a better path ...

In Part 12, the final installment in the series, the experts present some final predictions about AI's future impact on APM and Observability ...

AI plays a transformative role in both APM and observability by turning raw data into actionable insights, enabling faster, more accurate detection and resolution of issues ...

Enterprises are turning to AI-powered software platforms to make IT management more intelligent and ensure their systems and technology meet business needs for efficiency, lowers costs and innovation, according to new research from Information Services Group ...

While there is high enthusiasm for AI — with 92% of those surveyed in the manufacturing industry confirming AI is a top C-Suite priority and 92% agree it provides a competitive advantage — only 32% of manufacturers are fully prepared to implement AI projects now, 5% lower than the overall industry average, according to Manufacturing sector results of the Riverbed Global AI & Digital Experience Survey ...

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Today's enterprises exist in rapidly growing, complex IT landscapes that can inadvertently create silos and lead to the accumulation of disparate tools. To successfully manage such growth, these organizations must realize the requisite shift in corporate culture and workflow management needed to build trust in new technologies. This is particularly true in cases where enterprises are turning to automation and autonomic IT to offload the burden from IT professionals. This interplay between technology and culture is crucial in guiding teams using AIOps and observability solutions to proactively manage operations and transition toward a machine-driven IT ecosystem ...

The surge in AI adoption amplifies the need for robust data center infrastructure to handle the terabytes of data being generated daily ... Still, as much as AI will benefit from data centers, data centers need observability solutions to ensure resiliency and sustainability so businesses can operate to their full potential and provide seamless experiences to customers ...

Today's IT environments are more complex than ever, with organizations managing an increasing number of applications, platforms, and systems. To maintain peak performance and ensure seamless digital experiences, businesses are turning to Artificial Intelligence for IT Operations (AIOps) ...

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The adoption of artificial intelligence (AI) is accelerating across the telecoms industry, with 88% of fixed broadband service providers now investigating or trialing AI automation to enhance their fixed broadband services, according to new research from Incognito Software Systems and Omdia ...

In APMdigest's 2025 Predictions Series, industry experts offer predictions on how Observability and related technologies will evolve and impact business in 2025. Part 2 covers AI's impact on Observability, including AI Observability, AI-Powered Observability and AIOps ...

Organizations recognize the benefits of generative AI (GenAI) yet need help to implement the infrastructure necessary to deploy it, according to The Future of AI in IT Operations: Benefits and Challenges, a new report commissioned by ScienceLogic ...

Splunk's latest research reveals that companies embracing observability aren't just keeping up, they're pulling ahead. Whether it's unlocking advantages across their digital infrastructure, achieving deeper understanding of their IT environments or uncovering faster insights, organizations are slashing through resolution times like never before ...

Today's digital business landscape evolves rapidly, pushing businesses consistently to optimize operations and elevate user satisfaction. Among the areas primed for innovation, the long-standing ticket-based IT support model stands out as particularly outdated. Emerging as a game-changer, the concept of the "ticketless enterprise" promises to shift IT management from a reactive stance to a proactive approach ...

As AI improves and strengthens various product innovations and technology functions, it's also influencing and infiltrating the observability space ... Observability helps translate technical stability into customer satisfaction and business success and AI amplifies this by driving continuous improvement at scale ...

Optimizing the performance of applications remains a top challenge for most organizations, particularly when those applications are spread across a hybrid IT estate that includes core, public cloud, and edge. Even if there were a magic performance wand, organizations would still need to know there was a problem and then do something about it — at Internet speed ... 

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