
Bell Integration unveiled its Intelligent Managed Services platform, developed in collaboration with IBM.
This solution is designed to help organizations advance their IT operations through increased automation, predictive insights and intelligent orchestration capabilities, helping businesses to move from reactive to proactive, resilient operations.
Central to this collaboration is the development of the Intelligent Managed Services platform, powered by IBM technology with predictive analytics and agentic AI orchestration. This combination of technologies, including IBM Cloud Pak for AIOps, Instana for observability, and watsonx Orchestrate for agentic control, supports Bell Integration's Intelligent AI-first managed services and will be available to customers to accelerate the transformation of IT operations.
Martin Mersey, AI Automation Director, Bell Integration, said: "With Bell's agentic AI and automation underpinning the solution, we can help businesses move from reactive firefighting towards more proactive and resilient operations, transforming IT from an operational necessity to a business enabler. For IT leaders navigating cloud complexity, battling with resilience, and increasing performance expectations, our IMS platform is designed to help reduce operational risk and improve response times, providing organisations with greater visibility and resilience."
The Latest
For decades, traditional observability tools have forced IT professionals to sift through endless dashboards, turning a flood of alerts into actionable insight and driving widespread burnout. While enterprises adopted AI to eliminate this friction, most still deploy it reactively rather than proactively ... As systems become more complex and IT moves to the center of business strategy, a reactive posture is unsustainable ...
Ask an operations team what they monitor and you will hear a familiar list: latency, errors, throughput, uptime. Those signals still matter for AI, but they no longer answer the most important question. As Krishna Sai, CTO of SolarWinds, puts it: "Traditional observability tells you a system is running. AI observability tells you whether it's still reasoning soundly. Those are completely different questions" ...
AI observability serves several roles across an organization. AI engineers use it to build, evaluate, and improve AI systems. SREs use it to operate those systems reliably and efficiently in production, including managing their cost. Leaders need it to connect AI behavior and operational performance to business outcomes. The common need is a clear understanding of whether AI systems are delivering the intended result ...
Part 4 outlines the challenges enterprise face when adopting AI observability. The main challenge is that AI complexity compounds at every stage of maturity. LLM-based applications introduce new behavior, dependencies, and quality considerations, while Agentic AI adds further failure modes as systems make decisions and take actions ...
Part 3 reports on the current state of AI observability. Most experts agree: enterprises are moving faster with AI than they are with the operational discipline needed to run it safely ...
When discussing AI Observability, one of the first questions that arises: Is this just an extension of traditional observability or is this a whole new type of tool? Part 2 compares traditional observability with AI observability ...
In this extensive 14-part series that will post over the next three weeks, experts across observability and AI will provide insight and opinions on the need for AI Observability, the challenges, the benefits, the current state, and predictions for the future ...
Amid rising complexity, organizations are moving quickly toward agent-driven operations: AgenticOps, according to The Impact of Agentic AI on Network Operations, a new report from Cisco ...
AI, automation, and enterprise software give organizations an extraordinary amount of leverage. Work that once required hours can happen in minutes. Information buried across systems can be analyzed almost instantly. Complex workflows can be coordinated across teams with far less manual intervention. That leverage creates a temptation to start with the technology ...
Everyone wants to talk about models. Compute clusters. Benchmark scores that climb a fraction of a point and make headlines anyway. It's an easy conversation to have because it's measurable. But it's also increasingly beside the point ...