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ServiceNow Launches Intelligent Automation Engine

ServiceNow announces machine learning capabilities to tackle some of the biggest problems in IT today.

With ServiceNow Intelligent Automation Engine, companies can prevent outages before they happen, automatically categorize and route incidents, benchmark performance against IT peers and predict future performance. Capabilities will also bring machine learning to ServiceNow cloud services for Customer Service, Security and Human Resources (HR).

The ServiceNow Intelligent Automation Engine applies machine learning to four of the biggest use cases that IT has today. ServiceNow has taken the combination of massive amounts of contextual operational data, huge R&D investments, and a team of leading data scientists, to address four big challenges for today’s IT organizations ‑ preventing outages, automatically categorizing and routing work, predicting future performance and benchmarking performance against their peers.

“Intelligent automation heralds a new era in workplace productivity,” said Dave Wright, chief strategy officer, ServiceNow. “With this game changing innovation, we have embedded intelligence across our Platform. Trained with each customer’s own data, ServiceNow is enabling customers to achieve a quantum leap in the speed and economics of their business.”

Here are the innovations launched today:

- Anomaly Detection to Prevent Outages —ServiceNow has bolstered its ability to help customers predict and prevent service outages with anomaly detection. The algorithms identify patterns and outlying occurrences that are likely to lead to an outage. Combined with new dynamic threshold measures, the system learns what is the normal range of behavior and flags outliers that can indicate future errors or malfunctions. Initially delivered in Operational Intelligence for IT, the anomaly detection capabilities can correlate past events that led to outages and initiate workflows to pre‑empt future problems when the same preceding events are observed again.

- Intelligence to Categorize and Route Work – ServiceNow will make available machine‑learning algorithms to each customer’s unique data set based on the DxContinuum acquisition. By learning from past patterns, the Intelligent Automation Engine can predict future outcomes, including determining risks, assigning owners, and categorizing work. Initially, this predictive intelligence capability will be used in the IT Service Management offering to categorize and route IT requests with a high level of accuracy. Learned models set the category of the IT request and assign the task to the right team, as well as calculate associated risk of action or inaction. This capability brings new levels of speed and efficiency to IT delivery, and provides a foundation for the future, where connected devices create orders of magnitude increases in service requests.

- Benchmarks to Evaluate Performance Against Peers – Available today, ServiceNow Benchmarks enables customers to compare their service efficiency to peers ‑ such as similarly sized organizations or companies in the same industry. In the past, comparing performance to peers was difficult, if not impossible. Now, companies can not only know how they are performing against their own goals, but how their performance compares to like organizations.

- Performance Predictions to Drive Improvements—The Intelligent Automation Engine powers new algorithms in its real‑time Performance Analytics application to help customers better determine when they will achieve performance goals. Customers set a performance objective and based on the data profile, Performance Analytics uses the optimal algorithm to predict when they will reach the objective.

The Intelligent Automation Engine is part of the Now Platform, which powers cloud services to speed and automate work for IT, Security, HR, Customer Service and custom applications for any department. As the platform evolves, all departments and applications will benefit from intelligent automation. By automating both routine and complex processes and predicting outcomes, every organization can dramatically reduce costs, speed time‑to‑resolution and deliver consumer‑like experiences for employees, partners and customers.

The Latest

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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 ...

The gap is widening between what teams spend on observability tools and the value they receive amid surging data volumes and budget pressures, according to The Breaking Point for Observability Leaders, a report from Imply ...

ServiceNow Launches Intelligent Automation Engine

ServiceNow announces machine learning capabilities to tackle some of the biggest problems in IT today.

With ServiceNow Intelligent Automation Engine, companies can prevent outages before they happen, automatically categorize and route incidents, benchmark performance against IT peers and predict future performance. Capabilities will also bring machine learning to ServiceNow cloud services for Customer Service, Security and Human Resources (HR).

The ServiceNow Intelligent Automation Engine applies machine learning to four of the biggest use cases that IT has today. ServiceNow has taken the combination of massive amounts of contextual operational data, huge R&D investments, and a team of leading data scientists, to address four big challenges for today’s IT organizations ‑ preventing outages, automatically categorizing and routing work, predicting future performance and benchmarking performance against their peers.

“Intelligent automation heralds a new era in workplace productivity,” said Dave Wright, chief strategy officer, ServiceNow. “With this game changing innovation, we have embedded intelligence across our Platform. Trained with each customer’s own data, ServiceNow is enabling customers to achieve a quantum leap in the speed and economics of their business.”

Here are the innovations launched today:

- Anomaly Detection to Prevent Outages —ServiceNow has bolstered its ability to help customers predict and prevent service outages with anomaly detection. The algorithms identify patterns and outlying occurrences that are likely to lead to an outage. Combined with new dynamic threshold measures, the system learns what is the normal range of behavior and flags outliers that can indicate future errors or malfunctions. Initially delivered in Operational Intelligence for IT, the anomaly detection capabilities can correlate past events that led to outages and initiate workflows to pre‑empt future problems when the same preceding events are observed again.

- Intelligence to Categorize and Route Work – ServiceNow will make available machine‑learning algorithms to each customer’s unique data set based on the DxContinuum acquisition. By learning from past patterns, the Intelligent Automation Engine can predict future outcomes, including determining risks, assigning owners, and categorizing work. Initially, this predictive intelligence capability will be used in the IT Service Management offering to categorize and route IT requests with a high level of accuracy. Learned models set the category of the IT request and assign the task to the right team, as well as calculate associated risk of action or inaction. This capability brings new levels of speed and efficiency to IT delivery, and provides a foundation for the future, where connected devices create orders of magnitude increases in service requests.

- Benchmarks to Evaluate Performance Against Peers – Available today, ServiceNow Benchmarks enables customers to compare their service efficiency to peers ‑ such as similarly sized organizations or companies in the same industry. In the past, comparing performance to peers was difficult, if not impossible. Now, companies can not only know how they are performing against their own goals, but how their performance compares to like organizations.

- Performance Predictions to Drive Improvements—The Intelligent Automation Engine powers new algorithms in its real‑time Performance Analytics application to help customers better determine when they will achieve performance goals. Customers set a performance objective and based on the data profile, Performance Analytics uses the optimal algorithm to predict when they will reach the objective.

The Intelligent Automation Engine is part of the Now Platform, which powers cloud services to speed and automate work for IT, Security, HR, Customer Service and custom applications for any department. As the platform evolves, all departments and applications will benefit from intelligent automation. By automating both routine and complex processes and predicting outcomes, every organization can dramatically reduce costs, speed time‑to‑resolution and deliver consumer‑like experiences for employees, partners and customers.

The Latest

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 ...

The gap is widening between what teams spend on observability tools and the value they receive amid surging data volumes and budget pressures, according to The Breaking Point for Observability Leaders, a report from Imply ...