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Micro Focus Completes Merger with HPE Software Business

Micro Focus announced the completion of its merger with Hewlett Packard Enterprise’s (HPE) software business.

This merger brings together two leaders in the software industry to form a new, combined company positioned to help customers maximize existing software investments and embrace innovation in a world of Hybrid IT.

Upon close, Chris Hsu, formerly COO of HPE and EVP and GM of HPE Software, was appointed CEO of Micro Focus.

“Today marks a significant milestone for Micro Focus, and I am honored to be leading this team,” said Chris Hsu, CEO of Micro Focus. “We are bringing together a powerful combination of technology and talent uniquely positioned to drive customer-centered innovation at enterprise scale – enabling organizations to maximize the ROI of existing software investments while embracing the new hybrid model for enterprise IT.”

Micro Focus is designed from the ground up to build, sell and support software. With more than 5,800 employees in R&D, the combined company helps solve the most complex technology problems for customers, delivering world-class, enterprise-scale solutions in key areas including:

- DevOps: enabling the rapid delivery of quality, secure applications with end-to-end visibility across a toolchain of commercial and open source offerings -- leveraging the largest portfolio in the industry.

- Hybrid IT: simplifying the management of a complex mix of platforms, delivery methods and consumption models to help organizations address business needs, control costs, and ensure availability and performance at global scale.

- Security & Risk Management: Securing data, applications and access; powering security operations and governance to mitigate risk and maintain compliance; and harnessing the power of secure DevOps practices to ensure end-to-end risk management.

- Predictive Analytics: Helping customers translate siloed data into real-time proactive analytics at scale, anchored on supporting open and cloud-based stacks to create new insights across applications, operations, security and the business.

“It is our mission to provide a best-in-class portfolio of enterprise-grade scalable software with analytics built in, and put customers at the center of our innovation building high-quality products that our teams can be proud of,” added Hsu. “Driven by this mission, Micro Focus is uniquely positioned to help customers and partners address opportunities and challenges within the new hybrid model for enterprise IT – from mainframe to mobile to cloud.”

“Our business strategy remains sound: bringing together software assets that deliver a high degree of value to our investors and an expansive solution portfolio to our customers so they can maximize the value of existing IT investments and adopt new technologies – essentially bridging the old and new,” said Kevin Loosemore, Executive Chairman of Micro Focus. “We’re excited to have Chris lead the combined company as we embark on this journey of uniting our organizations to create a world-class, pure-play enterprise software company.”

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Two years ago, almost every customer conversation about AI started with the same questions: Which model should we use? What can it do? Is it ready for the enterprise? Today, those discussions have moved on. CIOs are far more interested in how to govern AI, integrate it with existing systems, prepare their workforce and make it part of everyday operations. The challenge is no longer to prove that AI can deliver value. It's instead about how to embed AI into the business in a way that's secure, scalable and delivers measurable outcomes ...

 

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When an AI assistant gives an incomplete or incorrect answer, teams often blame the model. They adjust prompts, switch models, increase context windows or test a new retrieval strategy. However the model may not be a problem. In many enterprise AI workflows, the problem begins inside the document-ingestion pipeline ...

If you talk to any security or observability teams right now, they're all fighting the same fire: their tooling was built to ingest X, but their sources are pumping Y and soon to be doing Z. The knee-jerk reaction is always the same: we need more platform. However, this reaction is wrong. Let me explain why, because the solution to this problem is foundational, not financial. Instead of hurling yet more money at the problem, make sure you've done what's needed upstream ...

Rapid AI adoption and the unique ways AI workloads operate is redefining the scope and structure of what these teams must deliver. This shift is forcing organizations to rethink how they manage scale, automation, and control, according to The State of SRE and Platform Engineering 2026, a new report from Dynatrace ...

AI is usually talked about as a software tool, but it also depends heavily on the network behind it. Whether a company is using AI for chatbots, automation, monitoring, analytics, or employee support, all of that information has to move across the network in a reliable and secure way. That means AI is not just an application decision. It is also an infrastructure decision. Before organizations rush into AI, they should ask a simple question: Is our network ready to support it? ...

Enterprise AI often lacks governed access to where business processes actually execute. Without that access, AI agents may be able to reason, but they cannot operate reliably across enterprise workflows. For AI agents to effectively carry out workflows, they will require integration-layer context and controls. Organizations can implement these prerequisites by providing AI with managed access to the middleware layer ...

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Ask an infrastructure team how confident they are in their ability to govern AI, and most will tell you they've got it handled. A recent survey of 406 IT decision-makers and platform engineering leaders found 86% expressing exactly that confidence. Ask the same group whether they have a formal written AI governance policy, and the number drops to 30%, according to Spacelift's Infrastructure Automation Report ...

In MEAN TIME TO INSIGHT Episode 27, Shamus McGillicuddy, EMA VP of Research, Network Infrastructure and Operations, and Parker Hathcock, EMA Research Director covering IT Service/Operations (ServiceOps), discuss observability unification in modern IT operations ... 

Micro Focus Completes Merger with HPE Software Business

Micro Focus announced the completion of its merger with Hewlett Packard Enterprise’s (HPE) software business.

This merger brings together two leaders in the software industry to form a new, combined company positioned to help customers maximize existing software investments and embrace innovation in a world of Hybrid IT.

Upon close, Chris Hsu, formerly COO of HPE and EVP and GM of HPE Software, was appointed CEO of Micro Focus.

“Today marks a significant milestone for Micro Focus, and I am honored to be leading this team,” said Chris Hsu, CEO of Micro Focus. “We are bringing together a powerful combination of technology and talent uniquely positioned to drive customer-centered innovation at enterprise scale – enabling organizations to maximize the ROI of existing software investments while embracing the new hybrid model for enterprise IT.”

Micro Focus is designed from the ground up to build, sell and support software. With more than 5,800 employees in R&D, the combined company helps solve the most complex technology problems for customers, delivering world-class, enterprise-scale solutions in key areas including:

- DevOps: enabling the rapid delivery of quality, secure applications with end-to-end visibility across a toolchain of commercial and open source offerings -- leveraging the largest portfolio in the industry.

- Hybrid IT: simplifying the management of a complex mix of platforms, delivery methods and consumption models to help organizations address business needs, control costs, and ensure availability and performance at global scale.

- Security & Risk Management: Securing data, applications and access; powering security operations and governance to mitigate risk and maintain compliance; and harnessing the power of secure DevOps practices to ensure end-to-end risk management.

- Predictive Analytics: Helping customers translate siloed data into real-time proactive analytics at scale, anchored on supporting open and cloud-based stacks to create new insights across applications, operations, security and the business.

“It is our mission to provide a best-in-class portfolio of enterprise-grade scalable software with analytics built in, and put customers at the center of our innovation building high-quality products that our teams can be proud of,” added Hsu. “Driven by this mission, Micro Focus is uniquely positioned to help customers and partners address opportunities and challenges within the new hybrid model for enterprise IT – from mainframe to mobile to cloud.”

“Our business strategy remains sound: bringing together software assets that deliver a high degree of value to our investors and an expansive solution portfolio to our customers so they can maximize the value of existing IT investments and adopt new technologies – essentially bridging the old and new,” said Kevin Loosemore, Executive Chairman of Micro Focus. “We’re excited to have Chris lead the combined company as we embark on this journey of uniting our organizations to create a world-class, pure-play enterprise software company.”

The Latest

Two years ago, almost every customer conversation about AI started with the same questions: Which model should we use? What can it do? Is it ready for the enterprise? Today, those discussions have moved on. CIOs are far more interested in how to govern AI, integrate it with existing systems, prepare their workforce and make it part of everyday operations. The challenge is no longer to prove that AI can deliver value. It's instead about how to embed AI into the business in a way that's secure, scalable and delivers measurable outcomes ...

 

Two things happened to production incidents between 2023 and now, and they did not happen at the same speed. The first is that a class of dependency that barely existed three years ago now accounts for one incident in ten. Incidents disclosed by AI model and AI application providers rose from 1.7% of all disclosed unplanned incidents in 2023 to 10.7% in 2026 year to date, roughly a sixfold rise; that counts only incidents at AI companies themselves, so the true share is higher. The second is that the time to close an incident has not come down ...

When an AI assistant gives an incomplete or incorrect answer, teams often blame the model. They adjust prompts, switch models, increase context windows or test a new retrieval strategy. However the model may not be a problem. In many enterprise AI workflows, the problem begins inside the document-ingestion pipeline ...

If you talk to any security or observability teams right now, they're all fighting the same fire: their tooling was built to ingest X, but their sources are pumping Y and soon to be doing Z. The knee-jerk reaction is always the same: we need more platform. However, this reaction is wrong. Let me explain why, because the solution to this problem is foundational, not financial. Instead of hurling yet more money at the problem, make sure you've done what's needed upstream ...

Rapid AI adoption and the unique ways AI workloads operate is redefining the scope and structure of what these teams must deliver. This shift is forcing organizations to rethink how they manage scale, automation, and control, according to The State of SRE and Platform Engineering 2026, a new report from Dynatrace ...

AI is usually talked about as a software tool, but it also depends heavily on the network behind it. Whether a company is using AI for chatbots, automation, monitoring, analytics, or employee support, all of that information has to move across the network in a reliable and secure way. That means AI is not just an application decision. It is also an infrastructure decision. Before organizations rush into AI, they should ask a simple question: Is our network ready to support it? ...

Enterprise AI often lacks governed access to where business processes actually execute. Without that access, AI agents may be able to reason, but they cannot operate reliably across enterprise workflows. For AI agents to effectively carry out workflows, they will require integration-layer context and controls. Organizations can implement these prerequisites by providing AI with managed access to the middleware layer ...

Enterprise networks rarely behave the same way for very long. A routing adjustment in one region may unexpectedly alter application performance in another. A cloud migration may introduce hidden dependencies that go unnoticed until an outage occurs. All the while, the network is managed by several different teams, each of whom use different tool sets — and as a result, have different views of the network ... There’s usually an engineer who remembers why traffic fails over a certain way between sites, or which transparent firewall was added where. The problem is that human memory cannot scale alongside enterprise-scale networks ...

Ask an infrastructure team how confident they are in their ability to govern AI, and most will tell you they've got it handled. A recent survey of 406 IT decision-makers and platform engineering leaders found 86% expressing exactly that confidence. Ask the same group whether they have a formal written AI governance policy, and the number drops to 30%, according to Spacelift's Infrastructure Automation Report ...

In MEAN TIME TO INSIGHT Episode 27, Shamus McGillicuddy, EMA VP of Research, Network Infrastructure and Operations, and Parker Hathcock, EMA Research Director covering IT Service/Operations (ServiceOps), discuss observability unification in modern IT operations ...