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BMC Helix Operations Management Adds New AIOps Capabilities

BMC announced new AIOps capabilities for the BMC Helix Operations Management solution using its BMC HelixGPT capability.

The BMC Helix Operations Management solution transforms IT operations with out-of-the-box service blueprints for dynamic service modeling, easy-to-read situation explainability, and situation fingerprinting to remember similar situations. It also improved deep container auto-detection for an in-depth understanding of containerized environments.

The BMC Helix Operations Management solution combines the power of advanced causal AI to identify the root cause of issues, predictive AI to enable customers to identify and address problems before they happen, and generative AI to automate event summaries and best action recommendations for complex problems so teams know what to do next. These innovations enable IT operations to deliver higher service availability and resilience to the business. This is because they can drive more efficient operational performance with greater visibility across tool silos and use superior AI-driven insights for significantly improved mean time to identification (MTTI) and mean time to repair (MTTR) of problems.

New BMC Helix Operations Management innovations include:

- Service blueprints: BMC Helix Operations Management is the only solution on the market to offer out-of-the-box service blueprints, making creating and maintaining dynamic service models easier than ever. With support for microservices, Kubernetes, cloud, and application performance monitoring (APM), these dynamic service models are automatically updated, ensuring accurate service models are used in today's ever-changing IT environments.

- Situation explainability powered by causal AI: This capability sets a new standard for incident resolution by correlating incidents with similar current and past occurrences to generate the best action to resolve the incident swiftly. User-driven feedback for Situations provides greater contextual understanding and allows additional Situation information to be added by a user for faster root cause isolation. This empowers your enterprise to recover from service outages and other potential risks more quickly—without the need to manually scan multiple incident logs.

- The BMC HelixGPT capability streamlines the process with concise, plain-language summaries of how issues were resolved. Probable root cause reports can be created and "remembered" for future use.

- AIOps situation fingerprinting powered by AI, GPT, and NLP: Powered by advanced causal AI, situation fingerprinting automatically identifies whether a similar situation has previously occurred, eliminating the need to (re)diagnose a situation. This enables easier future identification to help speed MTTR, reduce noise and staff toil, and improve service performance.

- Improved deep container auto-detection: With the BMC Helix platform's advanced discovery capabilities, users can benefit from automated detection and an in-depth understanding of their containerized environments. This capability enhances knowledge-sharing among site reliability engineers (SREs), services owners, and operations teams responsible for modern, dynamic containerized environments. By enabling deeper container visibility, users will speed up mean time to repair (MTTR) while significantly reducing traditionally manual efforts.

"Pushing the limits of what AI-enabled solutions can do to help our customers has been a clear focus for BMC for many years," said Margaret Lee, senior vice president and general manager of Digital Service and Operations Management at BMC. "We continue to invest in driving innovations across AIOps to help our customers secure the insights they need to make better decisions, intelligently accelerate automation, and continuously improve the operating environment."

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BMC Helix Operations Management Adds New AIOps Capabilities

BMC announced new AIOps capabilities for the BMC Helix Operations Management solution using its BMC HelixGPT capability.

The BMC Helix Operations Management solution transforms IT operations with out-of-the-box service blueprints for dynamic service modeling, easy-to-read situation explainability, and situation fingerprinting to remember similar situations. It also improved deep container auto-detection for an in-depth understanding of containerized environments.

The BMC Helix Operations Management solution combines the power of advanced causal AI to identify the root cause of issues, predictive AI to enable customers to identify and address problems before they happen, and generative AI to automate event summaries and best action recommendations for complex problems so teams know what to do next. These innovations enable IT operations to deliver higher service availability and resilience to the business. This is because they can drive more efficient operational performance with greater visibility across tool silos and use superior AI-driven insights for significantly improved mean time to identification (MTTI) and mean time to repair (MTTR) of problems.

New BMC Helix Operations Management innovations include:

- Service blueprints: BMC Helix Operations Management is the only solution on the market to offer out-of-the-box service blueprints, making creating and maintaining dynamic service models easier than ever. With support for microservices, Kubernetes, cloud, and application performance monitoring (APM), these dynamic service models are automatically updated, ensuring accurate service models are used in today's ever-changing IT environments.

- Situation explainability powered by causal AI: This capability sets a new standard for incident resolution by correlating incidents with similar current and past occurrences to generate the best action to resolve the incident swiftly. User-driven feedback for Situations provides greater contextual understanding and allows additional Situation information to be added by a user for faster root cause isolation. This empowers your enterprise to recover from service outages and other potential risks more quickly—without the need to manually scan multiple incident logs.

- The BMC HelixGPT capability streamlines the process with concise, plain-language summaries of how issues were resolved. Probable root cause reports can be created and "remembered" for future use.

- AIOps situation fingerprinting powered by AI, GPT, and NLP: Powered by advanced causal AI, situation fingerprinting automatically identifies whether a similar situation has previously occurred, eliminating the need to (re)diagnose a situation. This enables easier future identification to help speed MTTR, reduce noise and staff toil, and improve service performance.

- Improved deep container auto-detection: With the BMC Helix platform's advanced discovery capabilities, users can benefit from automated detection and an in-depth understanding of their containerized environments. This capability enhances knowledge-sharing among site reliability engineers (SREs), services owners, and operations teams responsible for modern, dynamic containerized environments. By enabling deeper container visibility, users will speed up mean time to repair (MTTR) while significantly reducing traditionally manual efforts.

"Pushing the limits of what AI-enabled solutions can do to help our customers has been a clear focus for BMC for many years," said Margaret Lee, senior vice president and general manager of Digital Service and Operations Management at BMC. "We continue to invest in driving innovations across AIOps to help our customers secure the insights they need to make better decisions, intelligently accelerate automation, and continuously improve the operating environment."

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For many B2B and B2C enterprise brands, technology isn't a core strength. Relying on overly complex architectures (like those that follow a pure MACH doctrine) has been flagged by industry leaders as a source of operational slowdown, creating bottlenecks that limit agility in volatile market conditions ...

FinOps champions crucial cross-departmental collaboration, uniting business, finance, technology and engineering leaders to demystify cloud expenses. Yet, too often, critical cost issues are softened into mere "recommendations" or "insights" — easy to ignore. But what if we adopted security's battle-tested strategy and reframed these as the urgent risks they truly are, demanding immediate action? ...

Two in three IT professionals now cite growing complexity as their top challenge — an urgent signal that the modernization curve may be getting too steep, according to the Rising to the Challenge survey from Checkmk ...

While IT leaders are becoming more comfortable and adept at balancing workloads across on-premises, colocation data centers and the public cloud, there's a key component missing: connectivity, according to the 2025 State of the Data Center Report from CoreSite ...

A perfect storm is brewing in cybersecurity — certificate lifespans shrinking to just 47 days while quantum computing threatens today's encryption. Organizations must embrace ephemeral trust and crypto-agility to survive this dual challenge ...

In MEAN TIME TO INSIGHT Episode 14, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses hybrid multi-cloud network observability... 

While companies adopt AI at a record pace, they also face the challenge of finding a smart and scalable way to manage its rapidly growing costs. This requires balancing the massive possibilities inherent in AI with the need to control cloud costs, aim for long-term profitability and optimize spending ...

Telecommunications is expanding at an unprecedented pace ... But progress brings complexity. As WanAware's 2025 Telecom Observability Benchmark Report reveals, many operators are discovering that modernization requires more than physical build outs and CapEx — it also demands the tools and insights to manage, secure, and optimize this fast-growing infrastructure in real time ...

As businesses increasingly rely on high-performance applications to deliver seamless user experiences, the demand for fast, reliable, and scalable data storage systems has never been greater. Redis — an open-source, in-memory data structure store — has emerged as a popular choice for use cases ranging from caching to real-time analytics. But with great performance comes the need for vigilant monitoring ...

Kubernetes was not initially designed with AI's vast resource variability in mind, and the rapid rise of AI has exposed Kubernetes limitations, particularly when it comes to cost and resource efficiency. Indeed, AI workloads differ from traditional applications in that they require a staggering amount and variety of compute resources, and their consumption is far less consistent than traditional workloads ... Considering the speed of AI innovation, teams cannot afford to be bogged down by these constant infrastructure concerns. A solution is needed ...