
PagerDuty announced two new capabilities - Intelligent Triage and Intelligent Dashboards - for teams responsible for reducing the cost and business impact of issues such as slow downs and outages.
Today, it takes companies an average of 80 minutes to coordinate response teams to solve a customer-impacting issue, such as a failed shopping cart or broken web page. PagerDuty’s new solutions are expected to help reduce that to as little as five minutes by providing automation to get the right people working together, with the right information, to more quickly triage issues when seconds count. By adopting real-time digital operations management practices, large companies can gain upwards of $2.5 million in IT staff productivity savings.
Intelligent Triage is a new feature set within PagerDuty’s Event Intelligence product, which uses machine learning to group alerts together so teams don’t receive multiple alerts coming from related issues. Triage provides additional context into the issue; e.g., whether it has happened before, how it was resolved, how widespread it is, what teams and services are affected, who is working on it and how they can be reached. By immediately arming teams with this knowledge, PagerDuty helps organizations pull together the right people, with the right information, to solve problems faster, minimizing the cost of downtime and preventing poor customer experiences.
Intelligent Dashboards — new to PagerDuty’s Analytics product — leverages machine learning to provide teams with recommendations for how to resolve issues, as well as benchmarks against performance metrics from other teams in their organization or vertical industry so they can continually improve. Its Spotlights recommendation engine leverages 10 years of machine and human response data to give teams context for improvements, such as stopping unactionable alerts and recognizing repeat issues.
“Nearly half of companies experience a major technology issue at least monthly,” said PagerDuty’s SVP Product, Jonathan Rende. “In today’s always-on world, slow responses damage a company’s brand, impact employees and erode the bottom line. Companies urgently need insights into how they are handling these issues so they know how to improve. With Spotlights, we are automating the provision of knowledge that is crucial to both solving problems in the moment and continually improving performance.”
Intelligent Triage:
- Provides context into an issue e.g., whether it has happened before, how it was resolved, how widespread it is, what services and teams are affected, who is working on it and how they can be reached.
- Provides automation to ensure teams have the knowledge required to effectively triage issues in real-time (e.g. is this a major incident? Who is needed to help?).
- Reduces the impact of unplanned work by giving adjacent teams visibility so they don’t duplicate efforts or interfere with each other.
- Creates significant time and cost savings — the majority of tech employees will lose 100-plus hours of productivity due to unplanned work this year4.
- Now available for Event Intelligence customers.
Intelligent Dashboards:
- Leverages 10 years of machine data and human response patterns, applied through Spotlight, PagerDuty’s recommendation engine that learns from past issues to make suggestions that teams can use for future improvements, such as stopping unactionable alerts, fixing repeat issues and improving escalation practices.
- Includes interactive charts and graphs that, unlike static status reports, let customers drill into details by team to show incident volume, response effort, interruption volume and more.
- Provides managers with built-in benchmarks to see how their teams compare to peers in the organization and their vertical industry when it comes to spotting issues, mobilizing teams and achieving resolutions.
- Translates the impact of issues into business outcomes, such as total cost of incidents or response team fatigue where other solutions only have basic metrics, such as mean time to response (MTTR).
The Latest
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 ...
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.