Case Study — Internal Operations

From Reactive Resourcing to Real-Time Capacity Intelligence

How Design Industries deployed Tempo Timesheets and three purpose-built AI skills to give 17 team members across four countries real-time visibility over sprint capacity and billable utilisation.

 

✓ Atlassian Solution Partner
✓ Verified Q1 2026 Data
✓ Live Jira & Tempo Data
✓ Melbourne HQ, Nationwide Service
Atlassian Blue Hero Banner for Team Capacity Dashboard

At a Glance

Real outcomes from a real deployment. All metrics sourced from live Jira worklog data, Q1 2026 (verified 16 April 2026).

 

17

Team Members Logging Time

Across Red, Gold, and White teams in four countries

12+

Clients Tracked Monthly

Digital Factory clients tracked in a single month (March 2026)

88

Issues with Time Logged

Digital Factory issues with worklogs in March 2026 alone

3

AI Skills in Production

Custom Claude AI skills replacing manual reporting workflows

3

Delivery Teams Covered

Red, Gold, and White teams across Melbourne, Manila, Cebu, and Pune

5

Jira Projects with Worklogs

DF, DI, DIO, DIS, and DISD all actively tracked

 

*Source: Live Jira worklog queries, Q1 2026 — verified 16 April 2026

The Challenge

Growing a distributed delivery team without real-time capacity data creates a predictable problem: you only see the resourcing gap after it has already hit the sprint.

 

Backward-Looking Reporting

Manual spreadsheets and end-of-period estimates meant resourcing data was always stale. By the time an issue was visible, it had already impacted sprint delivery.

No Visibility Across Teams

With three delivery teams operating across four countries, there was no reliable way to see who was underutilised, who was consistently overloaded, or how capacity was distributed.

Reactive Sprint Planning

Sprint planning was largely intuitive. New work was allocated without a reliable capacity baseline, making it difficult to balance workloads fairly or commit to delivery timelines with confidence.

Accountability Without Measurement

Without consistent time-logging and real-time oversight, data gaps were common, utilisation targets were difficult to enforce, and leadership reporting relied on estimates rather than evidence.

The Solution

Tempo Timesheets across the full delivery organisation, with a custom AI reporting layer built on the Tempo API using three purpose-built Claude AI skills.

1

Organisation-Wide Tempo Deployment

Tempo Timesheets was implemented across all three delivery teams, giving every team member a consistent, structured way to log time against Jira issues. Adoption spanned all four operating locations: Melbourne, Manila, Cebu, and Pune.

2

AI-Powered Team Utilisation Reporting

The di-team-utilisation skill queries the Tempo API directly, aggregates logged hours against capacity targets, and generates an interactive utilisation dashboard with team-level and individual-level breakdowns on demand. No manual data extraction required.

3

Sprint Health Monitoring Before Issues Compound

The di-sprint-health skill combines Jira sprint data with utilisation signals to surface overloaded or underutilised team members before the sprint is underway. Team leads can act proactively rather than reactively.

4

Individual Workload Analysis Per Person

The di-individual-report skill provides per-person workload analysis including billable hours, client spread, overdue items, and RAG status, giving team leads a complete picture of each team member's delivery position at any point in the sprint.

The Results

Sprint planning shifted from reactive gut-feel to a structured, data-informed process. All metrics are sourced directly from Jira worklog data, Q1 2026.

 

Metric Detail Data Source
Active team members logging time 17 unique individuals recorded worklogs in Q1 2026 Jira worklog query, Jan-Mar 2026
Delivery teams covered 3 teams (Red, Gold, White) across 4 locations Jira team field + assignee data
Digital Factory client coverage 12+ distinct clients tracked in a single month (March 2026) Jira DF project worklog data
DF issues with time logged (monthly) 88 issues with worklogs in March 2026 alone Jira DF project worklog query
Jira projects with active worklogs 5 projects (DF, DI, DIO, DIS, DISD) Jira worklog query, March 2026
AI skills in production 3 skills replacing manual reporting workflows entirely DI skill registry
Sprint planning approach Data-informed and capacity-benchmarked, replacing intuitive and reactive planning Operational workflow

All metrics sourced from live Jira worklog data, Q1 2026 — verified 16 April 2026

"

The combination of Tempo and our own Claude skills means we can walk into any sprint planning session with a clear picture of who has capacity, who is stretched, and where the delivery risk sits. We use the same tools we recommend to our clients, and it works.

Michael Dockery

CEO, Design Industries

What our Customers say about us

swisse-logo

“Design Industries are dynamic and great at tailoring Atlassian. We've worked with them for years; I'm always confident they can support our development with enthusiasm, professionalism and benchmark support."

 

Matt Lecchi
Global IT Operations Manager,
Swisse Wellness
cLEMNGER 2

“Michael and the Design Industries team have done an incredible job managing the scope and stand-up of our Atlassian instance.
The rigour and detail in their process and documentation is incredible.”

 

Dave Keating
Chief Operating Officer,
Clemenger BBDO
Frame 231

“We reduced a number of tools across regions that caused miscommunication and inefficiency, bringing everyone into a single platform. By using the DI Configuration Patterns, we rapidly scaled Atlassian from 500 to 7,000 users, at the bare minimum doubling the efficiency of our teams, who are working on core bank payment systems such as our .com."

 

Paul Anastasopoulos
Platform Manager, ANZ

Our Clients

Australian Bureau of Statistics afterpay ANZ Australia Post Victoria Police Berry Street
Australian Bureau of Statistics afterpay ANZ Australia Post Victoria Police Berry Street

Why It Worked

Design Industries practises what it recommends. Deploying Tempo internally and building a custom AI reporting layer on top gave the team real proof before positioning it for clients.

 

We Eat Our Own Cooking

Every tool DI recommends to clients runs inside its own delivery operation first. This case study is a live proof point, built from real data, before a single prospect sees the pitch.

AI Fast Start in Practice

The three Claude AI skills are tangible examples of the AI Fast Start methodology: targeted integrations that turn existing data into operational intelligence, without a big-bang transformation programme.

Systematic, Not Accidental

17 team members, 12+ client engagements, five Jira projects, and continuous automated reporting. The data is real, the process is repeatable, and the reporting is always on. Every optimisation matters. Every day counts.

Frequently Asked Questions

Can't find what you need? Check out our Support Centre or Contact us.

What is Tempo Timesheets and how does it integrate with Jira?

Tempo Timesheets is a time-tracking application built natively on top of Jira. Team members log hours directly against Jira issues, and Tempo provides capacity planning, utilisation reporting, and billing visibility across the entire organisation. It is one of the most widely adopted time-tracking tools in the Atlassian ecosystem.

What are Claude AI skills and how were they used here?

Claude AI skills are purpose-built automation routines that connect to your existing tools via APIs. In this deployment, three skills were built on the Tempo API to automate reporting tasks that previously required manual data extraction: team utilisation dashboards, sprint health monitoring, and individual workload analysis. They run on demand and produce ready-to-act outputs without any manual effort.

Can we replicate this approach for our own team?

Yes. The same approach is available through Design Industries' AI Fast Start programme. DI can deploy Tempo Timesheets, configure it to your team structure and capacity targets, and build a custom AI reporting layer tailored to your operational needs. The engagement is systematic, fixed-scope, and designed to deliver working outputs within weeks, not months.

How long does a Tempo + AI reporting deployment take?

A focused AI Fast Start engagement typically delivers working integrations and reporting tools within two to three weeks for SMB teams and eight to twelve weeks for enterprise environments. The scope is defined upfront and the delivery is structured around your specific capacity goals and tooling context.

Does this work for teams not using Atlassian tools?

DI's AI Fast Start programme is platform-agnostic. While this case study uses Tempo Timesheets on Jira, the same AI reporting methodology can be applied to Microsoft 365, ServiceNow, or other enterprise tooling stacks. The approach stays the same: connect to your existing data sources, automate the reporting layer, and give your team leads the visibility they need to plan confidently.

Services That Work Well Together

Capacity intelligence is one piece of a larger operational picture. These services pair naturally with a Tempo and AI reporting deployment.

Strategy and Implementation

Platform Discovery

Understand where your Atlassian environment stands before you build. Platform Discovery maps your current configuration, surfaces gaps, and gives you a clear starting point for any improvement programme.

 

Find out more   >

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AI Fast Start

Systematic AI adoption using your existing tools. AI Fast Start delivers working integrations and operational AI in weeks, not months, using a proven 10-step methodology that is available as a standalone programme or within Digital Factory.

 

Find out more   >

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Digital Factory

Design Industries' Digital Factory is a three-pillar managed service partnership that combines licensing optimisation, complimentary platform support, and ongoing strategic improvements, all as a single, continuous engagement.

 

Find out more   >

DISCUSS YOUR CAPACITY MANAGEMENT

Ready to See Your Team's Capacity in Real Time?

The same approach is available for your team. Let's talk about what AI Fast Start could look like in your environment.