Critical customers can’t wait: prevent tickets from getting stuck in queues

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TL;DR

High-impact incidents get lost when queues and priorities aren’t shared across teams. The 5-step fast-track pattern fixes this with context-driven routing, unified SLA/OLA timing, and cross-team visibility in Jira. Watch the short demo from ACE or read more below.

                                      There’s a moment every support team dreads

Your queue doesn’t know which customers pay the bills.

When a strategic customer reports a critical incident, it is often processed the same way as any other request. Maybe this practice sounds familiar to you. 

These requests are not the same; the difference is the impact. “Losing” a ticket from a high-impact customer can trigger executive escalations, and even high contractual penalties and churn.

We will show you how to avoid this disastrous outcome using a practical pattern we created and tested at Deviniti. It is a dedicated fast-track service path for high-priority customers and critical incidents, aligned with ITIL 4’s end-to-end service thinking.

NOTE: The concept was presented by Sebastian Sadowski (Senior Atlassian Solution Designer at Deviniti) at an Atlassian Community Event.

OLAs drive speed

Most teams have SLAs defined in Support (response time, resolution time, availability). 

But when a critical issue crosses team boundaries, what ultimately determines speed is the OLA layer, which describes how Support, Dev, and Ops collaborate internally to keep the customer promise.

• SLA (Service-level agreements) = the promise made to the customer
• OLA (Operational-level agreements) = the internal agreement that makes the promise realistic

If teams are not on the same page, your SLA becomes a nice document and a stressful dashboard.

The problem that triggered a separate path

A global enterprise software provider faced rising pressure from strategic customers who expected fast, predictable support during critical incidents. 

Support had SLAs, but Dev and Ops worked with different priorities and limited shared visibility (in native JSM, only agents can display SLA metrics).

This led to delays, frustration, and a loss of trust. 

Where the process broke

This was a time for investigation. We had discovered that the patterns were painfully consistent:

Critical requests treated like standard ones → slow, unpredictable handling for strategic customers

No shared SLA visibility across teams → more breaches, poor coordination

Different prioritization rules per team → inconsistent handling of high-impact incidents

Manual handoffs → delays, unclear ownership

Even if support wanted to move fast, the system wasn’t designed for end-to-end flow.

Sebastian Sadowski, Senior Atlassian Solution Designer at Deviniti, explains the core problem:

Before we standardized the end-to-end service path, we audited how SLA time is calculated in Jira Cloud, both native and with common add-ons. 

Tracking time is rarely the hard part. Gaps appear in governance: teams don’t share the same SLAs/OLAs, workflows drift between projects, visibility is fragmented, and contract- or asset-driven agreements don’t consistently drive the right timers.

The solution approach: fast-track service path 

The goal was straightforward: make critical incidents faster and more predictable with shared ownership and a single SLA/OLA view across Support, Dev, and Ops.

The pattern we used is based on 5 building blocks and our apps: SLA Time Management and Queues for Jira & JSM).

You can steal it and tailor it to your stack.

See how it works step-by-step. 

Step #1 Assets-driven context enrichment

At creation time, enrich the request with context like:

• customer tier/service agreement
• SLA & OLA catalog entries
• team structure and service dependencies (think CMDB).

Key point: Classification shouldn’t depend on someone noticing that this is a strategic customer.

#2 Fast-track priority routing

Automatically route critical requests to a dedicated track path based on SLA/OLA and customer tier.

Key point: You're not asking teams to treat it differently, but changing how it's built.

#3 Unified SLA/OLA timing engine

Run unified timers across Support, Dev, and Ops, so all teams work off the same clock (or coordinated clocks, depending on your model).

Key point: “Support met SLA” doesn’t help if Dev only started working on the issue after 20 hours.

#4 Cross-team visibility (shared, real-time)

Provide a shared view of:

• all fast-track issues
• SLA timers and status
• current ownership

Key point: Critical work dies in silos. Visibility is how you prevent “I thought you had it.” Native JSM doesn’t have this capability, but you can use Marketplace SLA apps that fill this gap. 

#5 Coordinated resolution flow

Use repeatable steps and automation to reduce manual transitions and unclear ownership.

Key point: Your fastest incident response is the one that doesn’t bounce between teams.

What this looked like in Jira Cloud (one implementation pattern)

The entire fast-track concept is tool-agnostic, but many teams ask how to implement it in Jira.

In our Jira Cloud example, the flow was supported by:

• Assets for service and customer context at creation time
• Queues or queue-like views for cross-team, real-time tracking
• An SLA/OLA timing layer to control unified timers across teams

The key isn’t the exact product mix, but the architecture:
context → routing → timing → visibility → coordinated execution.

Results

In ~90 days after the implementation, we observed:

1. SLA compliance improving from ~70–75% to ~82–88%.
2. An overall +12–15% SLA performance increase.
3. Faster resolution and higher predictability for critical incidents.

Plus, a stronger customer confidence from our consistent, transparent delivery was a soft but very real win. 

Quick checklist: Do you have the foundations for a fast-track pattern?

If this solution sounds like something your teams want to try, check if you’re ready. 

This is a solid starting point.

What to aim for

• SLAs and OLAs, everyone agrees on (and actually uses)
  
  
• One set of priority rules based on business impact
  
  

• The same timer logic everywhere (start/stop/pause)
  
  
• Shared visibility for Support, Dev, and Ops—one view of the work
  
  
• Automated triage and routing for high-priority requests
  
  
• Tickets linked to services/assets/teams (CMDB-style context)
  
  
• Regular check-ins to improve what’s not working
  
  

What to watch out for

• Support owns SLAs, but there are no OLAs for Dev/Ops
  
  
• Each team has its own timing rules (so metrics don’t match)
  
  
• Critical incidents get handled like normal tickets
  
  
• Too many manual handoffs and “we’ll fix it later” updates
  
  
• Reporting is split by team, with no end-to-end picture

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If strategic customers are part of your service reality, “please prioritize” can’t be the process. 

A fast-track path makes prioritization explicit: context-driven routing, shared timing, and cross-team visibility. If you want to see how it works in Jira, check out the short demo. 

I’m curious, what’s the biggest blockers you've seen in these request management scenarios: routing, ownership, timing rules, or visibility across teams?

[Elena_Communardo Products]

Hey @Zuzanna Patocka_Deviniti_ 

I like the context → routing → timing → visibility → execution approach. It makes prioritization a system thing, not just a “please hurry” request.

In my experience, the biggest delays often come from tickets sitting idle, not because ownership is unclear, but because the assigned folks aren’t aware or don’t get a nudge. Having a shared view and automated alerts can make a huge difference. Thanks!