It support · Production

Australian Retail Giant Delivers Always-On Digital Experience Using PagerDuty AI-Powered Incident Management

The problem

The retailer's incident management was entirely manual: engineers had to review logs, judge severity, and determine who to call, while rapid delivery cycles made it harder over time to reach the right person. There was no systematic way to correlate incidents across APIs or measure response time.

Workflow diagram · grounded in source
1
Incident event generated
trigger
“the team built out technical services to route incidents”
2
ML event intelligence correlates
ai_action
“PagerDuty's ML-powered event management, Event Intelligence, helped automate incident response”
3
Change events surface context
ai_action
“Change events provided situational awareness, surfacing critical information about recent deployments and releases in the code repository. This was especially useful for Terraform projects, providing insight around an event like when, wh…”
4
AI narrows to correct engineer
routing
“Over time, PagerDuty could determine potential contributing factors of an incident and narrow down the correct engineer”
5
ServiceNow incident sync
integration
“A tight integration with ServiceNow immediately proved valuable for incident response—mapping priorities, syncing notes between the two, and closing incidents down on either platform”
6
Jira alert management
integration
“A Jira integration was used for alerts that didn't need to go through the formal ITSM process”
7
Business stakeholder communication
output
“Using PagerDuty's Business Services, it was able to effectively communicate information to the right business stakeholders”
Reported outcome

The retailer successfully launched its new in-house website on a new API platform.
PagerDuty delivered full-stack visibility, reduced resolution time by removing manual guesswork from incident routing, improved team health through more accurate alerting, and enabled stakeholder communications via status dashboards.

Reported metrics
Incident resolution timeReduced resolution time
Engineer alert volumereceiving less alerts
Root cause diagnosis speeddriving faster diagnosis of the root cause of API problems
Reported stack
PagerDutyEvent IntelligenceServiceNowJiraMicrosoft TeamsDynatrace
Source
https://www.pagerduty.com/customer/australian-retail-giant
Read source ↗

Frequently asked questions

What did this team achieve with this AI workflow?

The retailer successfully launched its new in-house website on a new API platform.

What tools did this team use?

PagerDuty, Event Intelligence, ServiceNow, Jira, Microsoft Teams, Dynatrace.

What results were reported?

Incident resolution time: Reduced resolution time; Engineer alert volume: receiving less alerts; Root cause diagnosis speed: driving faster diagnosis of the root cause of API problems (source-reported, not independently verified).

How is this it support AI workflow structured?

Incident event generated → ML event intelligence correlates → Change events surface context → AI narrows to correct engineer → ServiceNow incident sync → Jira alert management → Business stakeholder communication.

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