Skip to main content

Cherwell Software Launches AIOps

Cherwell Software announced the addition of AIOps to its product suite, enabling agile and autonomous IT operation processes, further enhancing service management, monitoring, and automation across the enterprise.

The new AIOps capabilities perform deep discovery of hybrid IT environments, dependency mapping between applications and underlying infrastructure, advanced event correlation, and predictive analytics. The resulting insights can trigger intelligent automations to effectively prevent outages, improve performance, and ease the burden of managing increasingly complex infrastructure.

"This past year has challenged companies to rethink their IT operations from both internal and external perspectives. Automation is no longer a futuristic option for the workplace, but now, a reality that also impacts the customer experience," said Sam Gilliland, CEO at Cherwell Software. "We are thrilled to further expand our product suite with AIOps, and remain committed to helping enterprises drive effective digital transformation initiatives and optimize their workflows to provide a seamless, positive customer experience."

Cherwell’s AIOps solution includes Discovery and Dependency Mapping (DDM), event correlation and noise reduction, predictive analytics, and anomaly detection. Customers implementing AIOps will experience the following benefits:

- Increased Pathway to Digital Maturity: Automated and operationalized facets of IT operations will help drive strong business outcomes by improving the performance, reliability, and resilience of critical services, across the enterprise.

- Streamlined Operations and Efficiency: Topology maps and comprehensive visualizations provide full-stack visibility into applications, infrastructure, and how they are connected to one another, enabling IT teams to quickly isolate the root cause of problems, accelerate incident response, save time spent on operations, and improve service quality for customers and employees.

- Anomalies, Incident Prediction, and Events: With Cherwell AIOps and Cherwell Service Management, IT teams can rely on AIOps to see when anomalies and events have been detected, and then leverage CSM to follow up with a ticket. Customers will have the opportunity to build automated Cherwell workflows that self-heal and further reduce resource allocation spent on incidents – contributing to a more proactive service desk and effective ITOps team.

With the addition of AIOps, Cherwell will strengthen its competitive foothold in the AITSM category, simplify implementation processes, and help drive ongoing value for future and current customers by facilitating their digital transformation journeys.

Enterprises interested in enabling agile and autonomous IT operation processes can begin by purchasing Cherwell’s Discovery and Dependency Mapping (DDM) Solution.

Customers interested in expanding their current Cherwell DDM solutions can seamlessly upgrade to the full Cherwell AIOps solution.

The Latest

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 ...

Cherwell Software Launches AIOps

Cherwell Software announced the addition of AIOps to its product suite, enabling agile and autonomous IT operation processes, further enhancing service management, monitoring, and automation across the enterprise.

The new AIOps capabilities perform deep discovery of hybrid IT environments, dependency mapping between applications and underlying infrastructure, advanced event correlation, and predictive analytics. The resulting insights can trigger intelligent automations to effectively prevent outages, improve performance, and ease the burden of managing increasingly complex infrastructure.

"This past year has challenged companies to rethink their IT operations from both internal and external perspectives. Automation is no longer a futuristic option for the workplace, but now, a reality that also impacts the customer experience," said Sam Gilliland, CEO at Cherwell Software. "We are thrilled to further expand our product suite with AIOps, and remain committed to helping enterprises drive effective digital transformation initiatives and optimize their workflows to provide a seamless, positive customer experience."

Cherwell’s AIOps solution includes Discovery and Dependency Mapping (DDM), event correlation and noise reduction, predictive analytics, and anomaly detection. Customers implementing AIOps will experience the following benefits:

- Increased Pathway to Digital Maturity: Automated and operationalized facets of IT operations will help drive strong business outcomes by improving the performance, reliability, and resilience of critical services, across the enterprise.

- Streamlined Operations and Efficiency: Topology maps and comprehensive visualizations provide full-stack visibility into applications, infrastructure, and how they are connected to one another, enabling IT teams to quickly isolate the root cause of problems, accelerate incident response, save time spent on operations, and improve service quality for customers and employees.

- Anomalies, Incident Prediction, and Events: With Cherwell AIOps and Cherwell Service Management, IT teams can rely on AIOps to see when anomalies and events have been detected, and then leverage CSM to follow up with a ticket. Customers will have the opportunity to build automated Cherwell workflows that self-heal and further reduce resource allocation spent on incidents – contributing to a more proactive service desk and effective ITOps team.

With the addition of AIOps, Cherwell will strengthen its competitive foothold in the AITSM category, simplify implementation processes, and help drive ongoing value for future and current customers by facilitating their digital transformation journeys.

Enterprises interested in enabling agile and autonomous IT operation processes can begin by purchasing Cherwell’s Discovery and Dependency Mapping (DDM) Solution.

Customers interested in expanding their current Cherwell DDM solutions can seamlessly upgrade to the full Cherwell AIOps solution.

The Latest

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 ...