AI systems are still prone to hallucinations and misjudgments ... To build the trust needed for adoption, AI must be paired with human-in-the-loop (HITL) oversight, or checkpoints where humans verify, guide, and decide what actions are taken. The balance between autonomy and accountability is what will allow AI to deliver on its promise without sacrificing human trust ...
Enterprise IT has become increasingly complex and fragmented. Organizations are juggling dozens — sometimes hundreds — of different tools for endpoint management, security, app delivery, and employee experience. Each one needs its own license, its own maintenance, and its own integration. The result is a patchwork of overlapping tools, data stuck in silos, security vulnerabilities, and IT teams are spending more time managing software than actually getting work done ...
2025 was the year everybody finally saw the cracks in the foundation. If you were running production workloads, you probably lived through at least one outage you could not explain to your executives without pulling up a diagram and a whiteboard ...
Data has never been more central to a greater portion of enterprise operations than it is today. From software development to marketing strategy, data has become an essential component for success. But as data use cases multiply, so too does the diversity of the data itself. This shift is pushing organizations toward increasingly complex data infrastructure ...
For most of the cloud era, site reliability engineers (SREs) were measured by their ability to protect availability, maintain performance, and reduce the operational risk of change. Cost management was someone else's responsibility, typically finance, procurement, or a dedicated FinOps team. That separation of duties made sense when infrastructure was relatively static and cloud bills grew in predictable ways. But modern cloud-native systems don't behave that way ...
Every business today depends on real-time connectivity — for meetings, cloud apps, customer transactions, and increasingly, AI-driven workloads. Yet one of the most common reasons performance feels inconsistent has nothing to do with servers or software. It's packet loss — the silent destroyer of digital experience ...
Modern distributed architectures, hybrid clouds, microservices, and edge computing are generating unprecedented amounts of telemetry data. While this data is crucial for observability, many organizations are discovering that purchasing multiple high-cost application performance management (APM) and observability platforms has become economically unsustainable. The challenge for CTOs is not whether to invest in APM, but rather how to do so wisely — ensuring a balance between visibility, cost, and scalability while avoiding tool sprawl ...
Most organizations approach OpenTelemetry as a collection of individual tools they need to assemble from scratch. This view misses the bigger picture. OpenTelemetry is a complete telemetry framework with composable components that address specific problems at different stages of organizational maturity. You start with what you need today and adopt additional pieces as your observability practices evolve ...
As discussions around AI "autonomous coworkers" accelerate, many industry projections assume that agents will soon operate alongside human staff in making decisions, taking actions, and managing tasks with minimal oversight. But a growing number of critics (including some of the developers building these systems) argue that the industry still has a long way to go to be able to treat AI agents like fully trusted teammates ...
Enterprise AI has entered a transformational phase where, according to Digitate's recently released survey, Agentic AI and the Future of Enterprise IT, companies are moving beyond traditional automation toward Agentic AI systems designed to reason, adapt, and collaborate alongside human teams ...
The numbers back this urgency up. A recent Zapier survey shows that 92% of enterprises now treat AI as a top priority. Leaders want it, and teams are clamoring for it. But if you look closer at the operations of these companies, you see a different picture. The rollout is slow. The results are often delayed. There's a disconnect between what leaders want and what their technical infrastructure can handle ...
Cloudflare's disruption illustrates how quickly a single provider's issue cascades into widespread exposure. Many organizations don't fully realize how tightly their systems are coupled to third party services, or how quickly availability and security concerns align when those services falter ... You can't avoid these dependencies, but you can understand them ...
If you work with AI, you know this story. A model performs during testing, looks great in early reviews, works perfectly in production and then slowly loses relevance after operating for a while. Everything on the surface looks perfect — pipelines are running, predictions or recommendations are error-free, data quality checks show green; yet outcomes don't meet the ground reality. This pattern often repeats across enterprise AI programs. Take for example, a mid-sized retail banking and wealth-management firm with heavy investments in AI-powered risk analytics, fraud detection and personalized credit-decisioning systems. The model worked well for a while, but transactions increased, so did false positives by 18% ...
Basic uptime is no longer the gold standard. By 2026, network monitoring must do more than report status, it must explain performance in a hybrid-first world. Networks are no longer just static support systems; they are agile, distributed architectures that sit at the very heart of the customer experience and the business outcomes ... The following five trends represent the new standard for network health, providing a blueprint for teams to move from reactive troubleshooting to a proactive, integrated future ...
The conversation around AI in the enterprise has officially shifted from "if" to "how fast." But according to the State of Network Operations 2026 report from Broadcom, most organizations are unknowingly building their AI strategies on sand. The data is clear: CIOs and network teams are putting the cart before the horse. AI cannot improve what the network cannot see, predict issues without historical context, automate processes that aren't standardized, or recommend fixes when the underlying telemetry is incomplete. If AI is the brain, then network observability is the nervous system that makes intelligent action possible ...
SolarWinds data shows that one in three DBAs are contemplating leaving their positions — a striking indicator of workforce pressure in this role. This is likely due to the technical and interpersonal frustrations plaguing today's DBAs. Hybrid IT environments provide widespread organizational benefits but also present growing complexity. Simultaneously, AI presents a paradox of benefits and pain points ...
Over the last year, we've seen enterprises stop treating AI as “special projects.” It is no longer confined to pilots or side experiments. AI is now embedded in production, shaping decisions, powering new business models, and changing how employees and customers experience work every day. So, the debate of "should we adopt AI" is settled. The real question is how quickly and how deeply it can be applied ...
AI continues to be the top story across the industry, but a big test is coming up as retailers make the final preparations before the holiday season starts. Will new AI powered features help load up Santa's sleigh this year? Or are early adopters in for unpleasant surprises in the form of unexpected high costs, poor performance, or even service outages? ...
IT organizations are preparing for 2026 with increased expectations around modernization, cloud maturity, and data readiness. At the same time, many teams continue to operate with limited staffing and are trying to maintain complex environments with small internal groups. These conditions are creating a distinct set of priorities for the year ahead. The DataStrike 2026 Data Infrastructure Survey Report, based on responses from nearly 280 IT leaders across industries, points to five trends that are shaping data infrastructure planning for 2026 ...
Developers building AI applications are not just looking for fault patterns after deployment; they must detect issues quickly during development and have the ability to prevent issues after going live. Unfortunately, traditional observability tools can no longer meet the needs of AI-driven enterprise application development. AI-powered detection and auto-remediation tools designed to keep pace with rapid development are now emerging to proactively manage performance and prevent downtime ...