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BMC Introduces Automated Mainframe Intelligence Solutions

BMC announced BMC AMI, automated mainframe intelligence solutions that will deliver higher performing, self-managing mainframe environments to meet the growing demands created by digital business growth.

Extending BMC's leadership in mainframe innovation, BMC AMI solutions combine built-in domain expertise, machine learning, intelligent automation, and predictive analytics to help enterprises automatically manage, diagnose, heal, secure, and optimize mainframe processes.

"Enterprise IT teams are experiencing complexity at an accelerating rate, and data and transaction volumes are exploding – all while trying to manage a changing workforce, close the skills gap, and optimize costs," said Bill Miller, President of ZSolutions at BMC. "With AMI, we are freeing up IT staff to work on high-value initiatives by removing manual processes through intelligent automation. BMC AMI will automatically analyze dozens of KPIs and millions of metrics a day, and proactively identify, predict, and fix problems before they become an issue."

The BMC AMI solutions enhance mainframe systems through intelligent automation by predicting outages before they occur, solving performance degradations, and mitigating cost increases.

With BMC AMI, customers benefit from BMC's expertise in collecting deep and broad z/OS® operational metrics from a variety of industry data sources, built-in world-class domain expertise, and multivariate analysis.

The new offerings include:

- BMC AMI Autonomous Solutions enable IT operations to automatically anticipate and repair performance degradations and disruptive outages before they occur, without manual intervention. This set of intelligent, integrated solutions extends to BMC AMI for Security Management, BMC AMI for DevOps, BMC AMI for Performance and Availability Management, and BMC AMI Cost and Capacity Management.

- BMC AMI Enterprise Connectors connect business-critical data from the mainframe to the entire enterprise and simplify the enterprise-wide management of business applications. These offer a truly holistic view of enterprise data by streaming mainframe metrics and related information in real-time to a variety of data receivers, including leading Security Information and Event Management (SIEM) solutions such as Splunk, IBM QRadar, ArcSight, LogRhythm, McAfee Enterprise Security Manager, and others. The BMC AMI Data Extractor for IMS solution is available now, and additional solutions will be available early in 2019.

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BMC Introduces Automated Mainframe Intelligence Solutions

BMC announced BMC AMI, automated mainframe intelligence solutions that will deliver higher performing, self-managing mainframe environments to meet the growing demands created by digital business growth.

Extending BMC's leadership in mainframe innovation, BMC AMI solutions combine built-in domain expertise, machine learning, intelligent automation, and predictive analytics to help enterprises automatically manage, diagnose, heal, secure, and optimize mainframe processes.

"Enterprise IT teams are experiencing complexity at an accelerating rate, and data and transaction volumes are exploding – all while trying to manage a changing workforce, close the skills gap, and optimize costs," said Bill Miller, President of ZSolutions at BMC. "With AMI, we are freeing up IT staff to work on high-value initiatives by removing manual processes through intelligent automation. BMC AMI will automatically analyze dozens of KPIs and millions of metrics a day, and proactively identify, predict, and fix problems before they become an issue."

The BMC AMI solutions enhance mainframe systems through intelligent automation by predicting outages before they occur, solving performance degradations, and mitigating cost increases.

With BMC AMI, customers benefit from BMC's expertise in collecting deep and broad z/OS® operational metrics from a variety of industry data sources, built-in world-class domain expertise, and multivariate analysis.

The new offerings include:

- BMC AMI Autonomous Solutions enable IT operations to automatically anticipate and repair performance degradations and disruptive outages before they occur, without manual intervention. This set of intelligent, integrated solutions extends to BMC AMI for Security Management, BMC AMI for DevOps, BMC AMI for Performance and Availability Management, and BMC AMI Cost and Capacity Management.

- BMC AMI Enterprise Connectors connect business-critical data from the mainframe to the entire enterprise and simplify the enterprise-wide management of business applications. These offer a truly holistic view of enterprise data by streaming mainframe metrics and related information in real-time to a variety of data receivers, including leading Security Information and Event Management (SIEM) solutions such as Splunk, IBM QRadar, ArcSight, LogRhythm, McAfee Enterprise Security Manager, and others. The BMC AMI Data Extractor for IMS solution is available now, and additional solutions will be available early in 2019.

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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 ...