70K+hourly workers supportedWorkforce technology at scale
5core functions using AIAcross the operating model
126modern awards managedIn a high-compliance environment
Multiplelive agents in productionConnected to real workflows
At a glance
AI moved from isolated experiments into operating infrastructure.
MyGig had strong conviction around AI early. The missing piece was a secure, shared model that could turn promising ideas into dependable workflows across a lean organisation.
The situation
Conviction was high. Operational consistency was not.
AI experiments were underway but remained fragmented.
There was no shared framework for secure, repeatable deployment.
A lean team needed leverage without unnecessary complexity.
The shift
Guardrails, shared practices and live workflows.
Security expectations and approved patterns introduced.
Teams trained across five core functions.
Agents connected to real internal systems and data.
We're an EOR. We hold tax file numbers, bank details and payroll records for tens of thousands of workers. So the question was never whether AI could help us. It was whether I could put company data into these tools and still look a client in the eye. Until I could answer that, everything stayed a prototype.
Strong AI conviction, without the operating structure to scale it.
MyGig supports a large hourly workforce in an environment shaped by Fair Work, WHS and payroll expectations. AI could create meaningful leverage, but disconnected experiments could not become dependable infrastructure without structure and security.
No shared framework
Experiments were underway, but teams lacked common patterns for dependable AI use.
Security was undefined
Sensitive internal data needed clear expectations before AI could move into core workflows.
No path to production
Interesting prototypes remained isolated from the systems and processes where work happened.
A lean team needed leverage
MyGig wanted to increase operating capacity without simply adding more headcount.
The turning point
MyGig chose not to stop at adding AI features. The goal became building AI into the operating model itself, with guardrails established before scale.
Solution delivered
Structure first, then deployment into the workflows that matter.
Aona helped MyGig establish a secure, usable model across the business, then move agents into live operational workflows rather than leaving them as isolated prototypes.
01
Guardrails and security foundations
Approved patterns and internal data expectations gave the team room to move quickly without losing control.
02
Team training and operating model
Teams across product, operations, HR, support and go-to-market learned a shared way of working with AI.
03
Real systems and data
Agents were designed around actual workflows and connected to the internal systems they needed to be useful.
04
Live operational deployment
AI moved beyond demonstrations into client interaction, sales, HR, operations and support.
Operational AI footprint
Live AI embedded across five core functions.
Each deployment was tied to a real operating need, a clear role and an observable business outcome.
FunctionRole of AIBusiness impactState
Function
Client interaction
Role of AI
Amy business agent
Business impact
Faster order intake and more consistent responses
Live
Function
Sales and GTM
Role of AI
Prospecting and engagement support
Business impact
Opportunity identification at greater scale
Live
Function
HR and operations
Role of AI
Internal workflow handling
Business impact
Less friction across recurring processes
Live
Function
Support
Role of AI
Worker and team assistance
Business impact
More consistent service delivery
Live
Function
Product and execution
Role of AI
Daily AI use across the team
Business impact
Shorter cycles from idea to deployment
Active
Results and impact
From promising experiments to a repeatable AI operating model.
The transformation happened in stages: establish the foundation, build team readiness, deploy into real workflows, then make AI part of day-to-day execution.
01
Initial state
Governance
None
Team use
Early experiments
Agents
No
02
Foundation
Governance
Guardrails defined
Team use
Training introduced
Agents
Design underway
03
Deployment
Governance
Controls in place
Team use
Daily use
Agents
Live
04
Today
Governance
Embedded
Team use
Part of day-to-day work
Agents
Multiple
In their words
We interpret 126 modern awards and pay 70,000+ workers. That maths only works if AI is doing real operational load, not sitting in a browser tab. Today agents handle client intake, prospecting and internal workflows within guardrails I actually trust, which is the only reason they were allowed near production.
As AI becomes operational infrastructure, security has to be designed into the operating model.
MyGig moved quickly because guardrails, team practices and real deployment were treated as one system. Aona helps organisations see workforce AI use, apply policy and protect sensitive data as adoption scales.
