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New IBM Software Portfolio Leverages Predictive Analytics

IBM announced a broad portfolio of software that uses predictive analytics to provide greater visibility and integration between the applications that run on an IT infrastructure and key business processes.

By applying analytics expertise to the data center in this way, the new software will enable clients to make more intelligent, automated business decisions and help them embrace cloud computing.

The software enhances business decision management, predictive business service management and integration.

The shift to IT analytics is transforming decision-making for IT and business operations. By using predictive analytics, which harness the vast quantities of data a business creates, organizations can respond more quickly and accurately to customer needs, better anticipate and prevent outages and deliver fact-driven metrics to drive better business outcomes. Analytics can also make improvements across the software development and delivery lifecycle, further reducing cost, risk and time to market.

"Predictive analytics can tame IT infrastructure in much the same way it's taming big data," said Robert LeBlanc, IBM Sr. VP, Middleware Software. "This new software adds intelligence to IT and business processes by basically creating a road map showing the fastest connection between a business and its IT infrastructure. It automatically avoids potholes to ensure the best outcome, which is critical as the market continues to embrace cloud computing."

The new IBM software creates a single view for an organization to gain visibility into all of its physical and digital assets and services, including business processes:

* Decision Management automates business decisions, ensuring an organization is able to make the right decision at the right time. IBM WebSphere Operational Decision Management enables business users to capture the decision logic of experts and apply it to accurately detect and react in real time to critical situations. This also helps eliminate delays in hand-offs between business experts and IT staff.

* Predictive Business Service Management reduces service disruptions and prevents impending issues before they become a problem in the IT and network infrastructure. By applying analytics to gain greater visibility, control and automation in their data centers, organizations will be able to better manage the health of their businesses. IBM Tivoli Analytics for Service Performance, along with a new version of Tivoli Business Service Management is being previewed as part of today's announcements.

* Connectivity and Integration provides greater insight into existing assets, freeing up capital to invest in new growth opportunities. By easily and securely bringing together an organization's disparate data sources and applications, this technology ensures users can access and incorporate vital information regardless of platform, device or data format. As a result, it becomes easier to apply analytics toward business initiatives. Enhanced versions of IBM WebSphere Message Broker and IBM WebSphere MQ allow users to more effectively integrate and disseminate secure information across business processes.

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 ...

New IBM Software Portfolio Leverages Predictive Analytics

IBM announced a broad portfolio of software that uses predictive analytics to provide greater visibility and integration between the applications that run on an IT infrastructure and key business processes.

By applying analytics expertise to the data center in this way, the new software will enable clients to make more intelligent, automated business decisions and help them embrace cloud computing.

The software enhances business decision management, predictive business service management and integration.

The shift to IT analytics is transforming decision-making for IT and business operations. By using predictive analytics, which harness the vast quantities of data a business creates, organizations can respond more quickly and accurately to customer needs, better anticipate and prevent outages and deliver fact-driven metrics to drive better business outcomes. Analytics can also make improvements across the software development and delivery lifecycle, further reducing cost, risk and time to market.

"Predictive analytics can tame IT infrastructure in much the same way it's taming big data," said Robert LeBlanc, IBM Sr. VP, Middleware Software. "This new software adds intelligence to IT and business processes by basically creating a road map showing the fastest connection between a business and its IT infrastructure. It automatically avoids potholes to ensure the best outcome, which is critical as the market continues to embrace cloud computing."

The new IBM software creates a single view for an organization to gain visibility into all of its physical and digital assets and services, including business processes:

* Decision Management automates business decisions, ensuring an organization is able to make the right decision at the right time. IBM WebSphere Operational Decision Management enables business users to capture the decision logic of experts and apply it to accurately detect and react in real time to critical situations. This also helps eliminate delays in hand-offs between business experts and IT staff.

* Predictive Business Service Management reduces service disruptions and prevents impending issues before they become a problem in the IT and network infrastructure. By applying analytics to gain greater visibility, control and automation in their data centers, organizations will be able to better manage the health of their businesses. IBM Tivoli Analytics for Service Performance, along with a new version of Tivoli Business Service Management is being previewed as part of today's announcements.

* Connectivity and Integration provides greater insight into existing assets, freeing up capital to invest in new growth opportunities. By easily and securely bringing together an organization's disparate data sources and applications, this technology ensures users can access and incorporate vital information regardless of platform, device or data format. As a result, it becomes easier to apply analytics toward business initiatives. Enhanced versions of IBM WebSphere Message Broker and IBM WebSphere MQ allow users to more effectively integrate and disseminate secure information across business processes.

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 ...