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BMC AMI Operational Insight Unveiled

BMC unveiled BMC AMI Operational Insight, an AI-driven, forward-looking solution that uses machine learning to detect anomalies and maximize lead time for remediation to mitigate mainframe issues before they become business problems.

The BMC AMI Operational Insight solution provides the intelligence for mainframe experts and newer employees alike to support every organization's journey to an Autonomous Digital Enterprise with the modern mainframe.

"Managing the mainframe has never been more critical to serving customers and ensuring uptime. It is imperative that companies have the capabilities to proactively manage the platform and anticipate problems before they happen," said John McKenny, SVP of ZSolutions Strategy and Innovation at BMC. "By applying AIOps to the mainframe for better availability and performance with BMC AMI Operational Insight, our customers can reclaim their valuable time and shift resources to focus on the strategic priorities that will allow them to become Autonomous Digital Enterprises."

The BMC Automated Mainframe Intelligence (AMI) AIOps suite envisions a three-part workflow – detect, find, and fix – designed to greatly reduce mean time to repair (MTTR) so operations teams spend less time reacting to issues and more time advancing high-level business initiatives. With BMC AMI Operational Insight, users gain a solution that utilizes machine learning to learn what is normal, detect anomalies, and maximize lead time for remediation, avoiding downtime or system degradation.

Key benefits of BMC AMI AIOps include:

- Faster detection: Notifications alert users of anomalies, allowing them to proactively solve problems impacting systems before they cause any downtime.

- More accurate predictions: Multivariate analysis looks across all KPIs simultaneously instead of in silos, to ensure no KPI anomalies are missed, resulting in fewer false positives.

- Data science and domain expertise built-in: Knowledge of which metrics to watch quickly fills the gaps left by a retiring workforce and expedites the learning curve for new staff. In addition, getting rid of the guesswork of collecting and evaluating extraneous metrics eliminates the waste of costly MIPS.

- Out-of-the-box predictive problem detection: Minimal configuration required means users can install, add data, and realize value immediately.

- Improved and adaptive intelligence for systems: Continuous consumption of deep and broad data sources helps add intelligence to complex systems, while continuous learning ensures teams can keep up with rapid pace of change.

As part of the new BMC AMI AIOps suite, the BMC AMI Operational Insight solution ensures mainframe uptime that allows organizations to meet the growing demands of digital business growth. BMC continues to invest and innovate for the mainframe with new product introductions, as well as the recent acquisition of Compuware. BMC now offers a full suite of mainframe software development, delivery, and performance solutions that empower organizations to scale Agile and DevOps with a fully integrated toolchain.

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BMC AMI Operational Insight Unveiled

BMC unveiled BMC AMI Operational Insight, an AI-driven, forward-looking solution that uses machine learning to detect anomalies and maximize lead time for remediation to mitigate mainframe issues before they become business problems.

The BMC AMI Operational Insight solution provides the intelligence for mainframe experts and newer employees alike to support every organization's journey to an Autonomous Digital Enterprise with the modern mainframe.

"Managing the mainframe has never been more critical to serving customers and ensuring uptime. It is imperative that companies have the capabilities to proactively manage the platform and anticipate problems before they happen," said John McKenny, SVP of ZSolutions Strategy and Innovation at BMC. "By applying AIOps to the mainframe for better availability and performance with BMC AMI Operational Insight, our customers can reclaim their valuable time and shift resources to focus on the strategic priorities that will allow them to become Autonomous Digital Enterprises."

The BMC Automated Mainframe Intelligence (AMI) AIOps suite envisions a three-part workflow – detect, find, and fix – designed to greatly reduce mean time to repair (MTTR) so operations teams spend less time reacting to issues and more time advancing high-level business initiatives. With BMC AMI Operational Insight, users gain a solution that utilizes machine learning to learn what is normal, detect anomalies, and maximize lead time for remediation, avoiding downtime or system degradation.

Key benefits of BMC AMI AIOps include:

- Faster detection: Notifications alert users of anomalies, allowing them to proactively solve problems impacting systems before they cause any downtime.

- More accurate predictions: Multivariate analysis looks across all KPIs simultaneously instead of in silos, to ensure no KPI anomalies are missed, resulting in fewer false positives.

- Data science and domain expertise built-in: Knowledge of which metrics to watch quickly fills the gaps left by a retiring workforce and expedites the learning curve for new staff. In addition, getting rid of the guesswork of collecting and evaluating extraneous metrics eliminates the waste of costly MIPS.

- Out-of-the-box predictive problem detection: Minimal configuration required means users can install, add data, and realize value immediately.

- Improved and adaptive intelligence for systems: Continuous consumption of deep and broad data sources helps add intelligence to complex systems, while continuous learning ensures teams can keep up with rapid pace of change.

As part of the new BMC AMI AIOps suite, the BMC AMI Operational Insight solution ensures mainframe uptime that allows organizations to meet the growing demands of digital business growth. BMC continues to invest and innovate for the mainframe with new product introductions, as well as the recent acquisition of Compuware. BMC now offers a full suite of mainframe software development, delivery, and performance solutions that empower organizations to scale Agile and DevOps with a fully integrated toolchain.

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 ...