Artificial intelligence is moving from hype to action, but not all AI is created equal. The current wave of interest primarily focuses on large language models (LLMs) and generative AI tools that create content, summarize data, and automate human workflows. Useful as they are, these systems still depend on people to guide and apply them. Instead of waiting for prompts, agentic AI can decide on a course of action, connect with other systems, and carry out tasks on its own. That level of independence is already drawing interest from attackers, who are testing ways to use automation and adaptation to gain an edge in cyberattacks ...

According to the Cisco AI Readiness Index, a small but consistent group of companies surveyed — the "Pacesetters," about 13% of organizations for the last three years — outperform their peers across every measure of AI value in the global study of over 8,000 AI leaders across 30 markets and 26 industries ...

A recent MIT study reveals that 95% of enterprise AI solutions fail, with 85% of AI project failures attributed to data readiness issues ... The reality is stark: AI effectiveness depends primarily on data quality, and organizations consistently struggle with data discovery, access, quality, structure, readiness, security, and governance. These challenges demand expert solutions, yet they often receive less attention than the flashy "AI will change everything" narratives that dominate industry discourse ...

In the age of AI, digital friction threatens to undermine AI's potential, exacerbate tech problems and have a corrosive effect on employee productivity. Office workers already endure 3.6 tech interruptions and 2.7 security update disruptions per month. This equates to nearly $4 million in lost productivity annually for a company with 2,000 employees ...

AI can be a critical part of the IT puzzle by helping to accelerate incident response, reduce downtime and keep customers happy. These gains can shift executive attitudes, as leaders come to see AI agents not just as experimental tools, but as reliable partners in mission-critical situations. It's no surprise, then, that 81% of IT and business executives now trust AI agents to take action during a crisis ...

Every week, a new AI tool claims to reinvent the enterprise. But beneath the hype, many enterprises are grappling with a sobering reality: the GenAI solutions they've deployed are falling far short of expectations ...

While AI adoption is accelerating, concerns about reliability and trust make it challenging to transition initiatives from concept to production, according to the 2025 State of Observability Report from Dynatrace ...

New Relic's 2025 Observability Forecast ... found that with a median annual cost of high-impact IT outages reaching $76 million, organizations are investing in AI-strengthened observability to detect and resolve issues faster. Here are 5 key takeaways from this year's report ...

Artificial intelligence is transforming network operations (NetOps), supercharging automation, enabling new predictive capabilities, improving visibility and powering nearly continuous optimization ... When trained on live network data and large libraries of validated automations, GenAI and agentic AI can help NetOps teams be more productive while making networks more reliable, easier to manage and secure. And as agentic AI becomes more powerful, AI's overall usefulness to the NetOps team will increase exponentially ...

Organizations across the globe face unprecedented cybersecurity challenges as their digital footprints expand across cloud, on-premises, and remote environments. Ransomware continues to surge as one of the top global cyber threats, with attacks increasing(link is external) by 33% globally in 2024 and organizations experiencing an average of 1,200 weekly attacks — the highest in three years ...

Every organization is scrambling to adopt AI, but many are missing out on one of its most transformative benefits: the ability to increase creativity within and across teams ... To carve a better path forward, organizations should approach AI rollouts in a strategic way that keeps people at the center of work. Here, I share three ways to build better rollouts that can help organizations harness AI as a creativity multiplier ...

In MEAN TIME TO INSIGHT Episode 18, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses networking for artificial intelligence ... 

Executive trust in AI agents and reliance on AI across business operations is growing, according to the PagerDuty AI Resilience Survey — 81% of executives trust AI agents to take action on the company's behalf during a crisis, such as a service outage or security event ...

Aryaka recently conducted a series of surveys in three distinct industries (manufacturing, transportation, and business services), looking deeper at key trends and pain points in networking and security. Despite differences in industry priorities and digital maturity, IT leaders in these sectors are all clearly facing an uphill battle to secure increasingly hybrid, cloud-connected, and distributed infrastructure without overwhelming their limited resources ...

IT Asset Management (ITAM) has undergone significant transformations in response to the evolving IT landscape ... The rise of the cloud has introduced new complexities, such as the need to manage SaaS applications and maintain visibility into cloud assets. The integration of AI has further accelerated these changes, driving ITAM teams to collaborate more closely with other IT stakeholders ... This shift is essential for addressing the multifaceted challenges of modern IT environments ...

AI roles now dominate tech market growth, according to ICT in Motion: The Next Wave of AI Integration, a new report from the AI Workforce Consortium ...

The observability landscape has transformed dramatically over the past decade. What began as traditional application performance monitoring (APM) has evolved into something more sophisticated and deeply essential to business operations. As we look at where the industry is headed, three themes have emerged that will define the future of how organizations monitor and manage their digital infrastructure ...

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 ...

Most teams go straight to the usual suspects when performance tanks in the cloud: app bugs, code regressions, maybe even the cloud provider itself. However, the real bottleneck is hiding in plain sight more often than not: the network ...

Your company's knowledge base is the engine that will power your AI agents. It's the structured data repository that an AI system uses to understand, reason, and make decisions ... Thus, transferring accurate knowledge is crucial. Incomplete or poor-quality data can reduce agent performance, accuracy, and even increase agent bias. Here are five steps to ensuring that your company's knowledge base transfer is optimal ...

Enterprises experience consistent year-over-year revenue growth from their generative AI initiatives and steady investment into AI and agentic projects, according to a new study from Google Cloud ...

AI agents are already transforming the enterprise ... But while the models are advancing fast, most enterprise systems still aren't ready for agent-to-agent AI. The reason is simple but consequential: the environments we've built don't support autonomous action ...

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 ...

The next generation of AI is already here. It may have been mere months since organizations adopted generative AI (GenAI), but now there's a new kid on the block and it promises to offer even greater benefits to businesses and IT operations teams in particular ... The key to success will be to avoid repeating the adoption mistakes of the past and to start small with manageable projects ...