BMC announced the newest release of its TrueSight AIOps platform, TrueSight 11, empowering customers to apply artificial intelligence to IT Operations.
TrueSight 11 is an AIOps platform that identifies and contextualizes patterns from virtually any data source, revealing recurring issues and repetitive tasks ideal for automation. TrueSight dynamically learns the behavior of infrastructure and manages capacity, including multi-cloud utilization in the context of applications and services. These predictive capabilities empower IT teams to reduce mean time to resolution (MTTR), identify false alarms, and align cloud spend with budget targets.
“Businesses cannot realize the true potential of a dynamic infrastructure if operations teams aren’t managing those resources in the context of business priorities,” said Shayne Higdon, President, Performance & Analytics at BMC. “BMC’s research indicates that 78 percent of IT leaders are looking to apply artificial intelligence as part of their multi-cloud management strategies. Our TrueSight platform for AIOps manages the health, performance, and cost of multi-cloud environments to improve infrastructure and operations agility, optimize service performance, and eliminate blind spots created by the explosion of digital data.”
TrueSight easily ingests, analyzes, and contextualizes data to provide actionable correlations and insights faster and more accurately than manual human analysis could produce. By dynamically learning the behavior of the infrastructure, TrueSight prioritizes issues by level of importance to the business and eliminates false alarms. With unified capacity and performance analysis augmented by AI, organizations can now understand resource utilization in the context of both performance and spend, enabling them to intelligently forecast and optimize both using TrueSight.
TrueSight 11 also introduces new solutions that address key use cases for machine learning to improve IT Operations, Service Desk, and Application Development disciplines:
- Cloud Cost Control forecasts infrastructure capacity and cost for cloud services and on-premises data centers to regain control of budgets and optimizing resources.
- Service Ticket Analytics uses machine learning to analyze tickets descriptions in real-time and intelligently categorizes them for faster resolution.
- Change and Defect Analytics uses Jira data for visibility into the performance and financial impact of defects and bugs assigned to application development teams.
- Event Stream Analytics reduces event overload to quickly identify hotspots and proactively identify service or application deterioration before users are impacted.
The Latest
In the world of digital-first business, there is no tolerance for service outages. Businesses know that outages are the quickest way to lose money and customers. For smaller organizations, unplanned downtime could even force the business to close ... A new study from PagerDuty, The State of AI-First Operations, reveals that companies actively incorporating AI into operations now view operational resilience as a growth driver rather than a cost center. But how are they achieving it? ...
In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...
Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...
In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ...
Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...
Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...
Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...
The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...
The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...
In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...