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Flow by ServiceNow Released

ServiceNow launched Flow by ServiceNow, a new conversational AI-native service desk built for teams reimagining the way they deploy ITSM for the AI era. 

It is available to any organization where speed and simplicity matter most, whether it’s a small team or an enterprise. Flow can be deployed instantly and be up and running in a day, with no implementation project and no infrastructure required. It helps resolve employee issues and requests through conversations in the chat channels teams already use. Flow delivers the speed organizations want, with instant answers and resolutions all grounded in ServiceNow's leadership in workflow automation and innovation.

With Flow, when users resolve a request correctly, they can automate that workflow with a single click, eliminating repetition. Next time the issue comes up, Flow handles it automatically. No knowledge base articles needed. That's reliable results at AI speed.

"Flow is for companies that want to work in a completely new way. It’s automation built for speed and simplicity,” said Amit Zavery, president, chief operating officer, and chief product officer at ServiceNow. “We are meeting companies where their employees work every day—in conversational channels like Slack and Teams, and investing where we see AI-native market opportunities. With Flow, every organization can have intelligent automation designed for business reality, speed, and ease.”

With Flow, support happens where work happens, and the conversation becomes the interface. Flow is a new standalone product with faster deployment and the same reliable results expected from ServiceNow's comprehensive service management portfolio. Flow doesn't just answer repetitive requests. It closes them or automatically escalates if needed. This is how ServiceNow is transforming how work gets done: AI-native, conversational support, easy-to-build automations, beginning in ITSM and expanding across every team.

Flow requires no deployment, no infrastructure, and no employee training. As ways of working and workforce needs are changing with AI, Flow provides a clear entry point for conversational service desk support. New customers can buy and activate Flow directly, with no existing ServiceNow relationship or platform prerequisite. Flow can begin handling requests as soon as it goes live.

ServiceNow's current customer base gains a new help desk experience too. Existing customers on AI-native SKUs can deploy Flow immediately at no additional procurement cost through consumption-based usage. Teams running full-scale ITSM deployments can also use Flow for specific departments, remote teams, or newly acquired business units that need rapid deployment or prefer a different operational model. No matter the company size, Flow brings teams online quickly, integrates into any existing knowledge, assets, ticketing, and AI usage, while giving confidence that everything is tied to the ServiceNow AI platform for auditability and governance when needed.

Customers in North America get priority access in the initial weeks, with general availability expected for North America and EMEA in Q4 2026. 

Flow will also be available to existing ServiceNow customers on any ServiceNow pricing plan that includes AI capabilities.

The Latest

For decades, traditional observability tools have forced IT professionals to sift through endless dashboards, turning a flood of alerts into actionable insight and driving widespread burnout. While enterprises adopted AI to eliminate this friction, most still deploy it reactively rather than proactively ... As systems become more complex and IT moves to the center of business strategy, a reactive posture is unsustainable ...

Ask an operations team what they monitor and you will hear a familiar list: latency, errors, throughput, uptime. Those signals still matter for AI, but they no longer answer the most important question. As Krishna Sai, CTO of SolarWinds, puts it: "Traditional observability tells you a system is running. AI observability tells you whether it's still reasoning soundly. Those are completely different questions" ...

AI observability serves several roles across an organization. AI engineers use it to build, evaluate, and improve AI systems. SREs use it to operate those systems reliably and efficiently in production, including managing their cost. Leaders need it to connect AI behavior and operational performance to business outcomes. The common need is a clear understanding of whether AI systems are delivering the intended result ...

Part 4 outlines the challenges enterprise face when adopting AI observability. The main challenge is that AI complexity compounds at every stage of maturity. LLM-based applications introduce new behavior, dependencies, and quality considerations, while Agentic AI adds further failure modes as systems make decisions and take actions ...

Part 3 reports on the current state of AI observability. Most experts agree: enterprises are moving faster with AI than they are with the operational discipline needed to run it safely ...

When discussing AI Observability, one of the first questions that arises: Is this just an extension of traditional observability or is this a whole new type of tool? Part 2 compares traditional observability with AI observability ...

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AI, automation, and enterprise software give organizations an extraordinary amount of leverage. Work that once required hours can happen in minutes. Information buried across systems can be analyzed almost instantly. Complex workflows can be coordinated across teams with far less manual intervention. That leverage creates a temptation to start with the technology ...

Everyone wants to talk about models. Compute clusters. Benchmark scores that climb a fraction of a point and make headlines anyway. It's an easy conversation to have because it's measurable. But it's also increasingly beside the point ...

Flow by ServiceNow Released

ServiceNow launched Flow by ServiceNow, a new conversational AI-native service desk built for teams reimagining the way they deploy ITSM for the AI era. 

It is available to any organization where speed and simplicity matter most, whether it’s a small team or an enterprise. Flow can be deployed instantly and be up and running in a day, with no implementation project and no infrastructure required. It helps resolve employee issues and requests through conversations in the chat channels teams already use. Flow delivers the speed organizations want, with instant answers and resolutions all grounded in ServiceNow's leadership in workflow automation and innovation.

With Flow, when users resolve a request correctly, they can automate that workflow with a single click, eliminating repetition. Next time the issue comes up, Flow handles it automatically. No knowledge base articles needed. That's reliable results at AI speed.

"Flow is for companies that want to work in a completely new way. It’s automation built for speed and simplicity,” said Amit Zavery, president, chief operating officer, and chief product officer at ServiceNow. “We are meeting companies where their employees work every day—in conversational channels like Slack and Teams, and investing where we see AI-native market opportunities. With Flow, every organization can have intelligent automation designed for business reality, speed, and ease.”

With Flow, support happens where work happens, and the conversation becomes the interface. Flow is a new standalone product with faster deployment and the same reliable results expected from ServiceNow's comprehensive service management portfolio. Flow doesn't just answer repetitive requests. It closes them or automatically escalates if needed. This is how ServiceNow is transforming how work gets done: AI-native, conversational support, easy-to-build automations, beginning in ITSM and expanding across every team.

Flow requires no deployment, no infrastructure, and no employee training. As ways of working and workforce needs are changing with AI, Flow provides a clear entry point for conversational service desk support. New customers can buy and activate Flow directly, with no existing ServiceNow relationship or platform prerequisite. Flow can begin handling requests as soon as it goes live.

ServiceNow's current customer base gains a new help desk experience too. Existing customers on AI-native SKUs can deploy Flow immediately at no additional procurement cost through consumption-based usage. Teams running full-scale ITSM deployments can also use Flow for specific departments, remote teams, or newly acquired business units that need rapid deployment or prefer a different operational model. No matter the company size, Flow brings teams online quickly, integrates into any existing knowledge, assets, ticketing, and AI usage, while giving confidence that everything is tied to the ServiceNow AI platform for auditability and governance when needed.

Customers in North America get priority access in the initial weeks, with general availability expected for North America and EMEA in Q4 2026. 

Flow will also be available to existing ServiceNow customers on any ServiceNow pricing plan that includes AI capabilities.

The Latest

For decades, traditional observability tools have forced IT professionals to sift through endless dashboards, turning a flood of alerts into actionable insight and driving widespread burnout. While enterprises adopted AI to eliminate this friction, most still deploy it reactively rather than proactively ... As systems become more complex and IT moves to the center of business strategy, a reactive posture is unsustainable ...

Ask an operations team what they monitor and you will hear a familiar list: latency, errors, throughput, uptime. Those signals still matter for AI, but they no longer answer the most important question. As Krishna Sai, CTO of SolarWinds, puts it: "Traditional observability tells you a system is running. AI observability tells you whether it's still reasoning soundly. Those are completely different questions" ...

AI observability serves several roles across an organization. AI engineers use it to build, evaluate, and improve AI systems. SREs use it to operate those systems reliably and efficiently in production, including managing their cost. Leaders need it to connect AI behavior and operational performance to business outcomes. The common need is a clear understanding of whether AI systems are delivering the intended result ...

Part 4 outlines the challenges enterprise face when adopting AI observability. The main challenge is that AI complexity compounds at every stage of maturity. LLM-based applications introduce new behavior, dependencies, and quality considerations, while Agentic AI adds further failure modes as systems make decisions and take actions ...

Part 3 reports on the current state of AI observability. Most experts agree: enterprises are moving faster with AI than they are with the operational discipline needed to run it safely ...

When discussing AI Observability, one of the first questions that arises: Is this just an extension of traditional observability or is this a whole new type of tool? Part 2 compares traditional observability with AI observability ...

In this extensive 14-part series that will post over the next three weeks, experts across observability and AI will provide insight and opinions on the need for AI Observability, the challenges, the benefits, the current state, and predictions for the future ...

Amid rising complexity, organizations are moving quickly toward agent-driven operations: AgenticOps, according to The Impact of Agentic AI on Network Operations, a new report from Cisco ...

AI, automation, and enterprise software give organizations an extraordinary amount of leverage. Work that once required hours can happen in minutes. Information buried across systems can be analyzed almost instantly. Complex workflows can be coordinated across teams with far less manual intervention. That leverage creates a temptation to start with the technology ...

Everyone wants to talk about models. Compute clusters. Benchmark scores that climb a fraction of a point and make headlines anyway. It's an easy conversation to have because it's measurable. But it's also increasingly beside the point ...