ServiceNow has agreed to acquire DxContinuum in an all-cash transaction expected to close this month.
ServiceNow can further increase productivity for its customers by applying machine-learning capabilities and data models developed by DxContinuum. As more Internet of Things devices make service requests, it is increasingly important that those requests be categorized, routed and responded to. Hundreds of thousands of machine and manual work requests can now be effectively and automatically categorized and routed for each ServiceNow customer, bringing the intelligent automation of today’s manual processes one step closer.
ServiceNow is acquiring DxContinuum, a machine-learning company, to embed its technology in the ServiceNow platform and across its products. DxContinuum’s predictive models will add greater efficiency in categorizing incoming requests from people and machines automatically.
By applying DxContinuum’s machine-learning algorithms to each customer’s unique data set, ServiceNow can train machines on how to route IT, HR, customer service or other requests with a high level of accuracy. For example, the models could set the category of the inquiry and assign the ticket to the right team, as well as calculate associated risks. When enterprises better predict outcomes and automate actions, they can reduce costs dramatically and speed time-to-resolution.
“ServiceNow is at the forefront of intelligent automation,” said Dave Wright, Chief Strategy Officer, ServiceNow. “Adding DxContinuum to the ServiceNow platform will move much more of the heavy lifting of work processes to machines, freeing people to focus on the highest value work.”
“ServiceNow already offers the industry’s most advanced software platform for automating enterprise work, and our technology will make it the smartest by far,” said Debu Chatterjee, founder and CEO of DxContinuum. “Their customers’ rich operational data sets will produce highly accurate predictions to speed work across the enterprise.”
The advanced capabilities for automated categorization will be available this year.
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 ...