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BMC Software Empowers Business Users with Self Service

Introducing New BMC Control-M Self Service

BMC Software has introduced BMC Control-M Self Service, the industry’s first workload automation solution designed specifically with the business user in mind. Now through the use of Control-M, BMC’s workload automation solution, business users can check the status of their work, make workload processing changes for their business transactions and access a service catalog of predefined workloads, all through a simple and secure web-based interface.

Some organizations have sought to enable business users to perform routine requests on their own by providing restricted access to workload automation tools, a process that requires extensive training on technical applications. With the BMC Control-M Self Service solution, armed with only a browser, business users can easily view and request IT workload services needed to support internal and external customers without the need for expert knowledge of multiple workload automation tools.

Organizations need a way for business users to quickly, securely and easily get status information on their event driven and scheduled processing transactions with the ability to make policy managed changes to those transactions. BMC Control-M Self Service provides access to a service catalog that enables business users to initiate services that are important to the business, such as changes to transactions, all on a controlled and secure platform.

And with its simple and secure user interface, the BMC Control-M Self Service solution eliminates the delay of information caused with service requests, allowing the business user to provide better and more timely service to internal and external customers.

The new capabilities also empower IT to greatly reduce costs as Control-M Self Service eliminates service desk tickets (often accruing hundreds per day at some organizations) and the work associated with them, providing IT administrators the ability to maintain ultimate control over the workload automation process and their IT priorities.

Playing a key role in BMC’s BSM strategy, BMC Control-M manages and runs workloads in physical, virtual and cloud environments with Dynamic Workload Management and workload policies that leverage virtual resources and private and public clouds as needed to complete workload processing and ensure SLAs are met.

Over 2,000 organizations have converted more than 5 million automation tasks to BMC Control-M.

The Latest

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 ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.

The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...

BMC Software Empowers Business Users with Self Service

Introducing New BMC Control-M Self Service

BMC Software has introduced BMC Control-M Self Service, the industry’s first workload automation solution designed specifically with the business user in mind. Now through the use of Control-M, BMC’s workload automation solution, business users can check the status of their work, make workload processing changes for their business transactions and access a service catalog of predefined workloads, all through a simple and secure web-based interface.

Some organizations have sought to enable business users to perform routine requests on their own by providing restricted access to workload automation tools, a process that requires extensive training on technical applications. With the BMC Control-M Self Service solution, armed with only a browser, business users can easily view and request IT workload services needed to support internal and external customers without the need for expert knowledge of multiple workload automation tools.

Organizations need a way for business users to quickly, securely and easily get status information on their event driven and scheduled processing transactions with the ability to make policy managed changes to those transactions. BMC Control-M Self Service provides access to a service catalog that enables business users to initiate services that are important to the business, such as changes to transactions, all on a controlled and secure platform.

And with its simple and secure user interface, the BMC Control-M Self Service solution eliminates the delay of information caused with service requests, allowing the business user to provide better and more timely service to internal and external customers.

The new capabilities also empower IT to greatly reduce costs as Control-M Self Service eliminates service desk tickets (often accruing hundreds per day at some organizations) and the work associated with them, providing IT administrators the ability to maintain ultimate control over the workload automation process and their IT priorities.

Playing a key role in BMC’s BSM strategy, BMC Control-M manages and runs workloads in physical, virtual and cloud environments with Dynamic Workload Management and workload policies that leverage virtual resources and private and public clouds as needed to complete workload processing and ensure SLAs are met.

Over 2,000 organizations have converted more than 5 million automation tasks to BMC Control-M.

The Latest

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 ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.

The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...