Data / AI / Automation for finance

Helping build machines that work

Data, AI and automation for Australian financial services. We help leadership teams set the direction, build what matters, and bring the whole organisation with them.

Get the discussion paper Executive education

There is a difference between knowing the path and walking the path.

Most of the industry has plenty of people who can describe what good looks like. We measure ourselves on whether it is running on Monday morning, and whether the people around it know why it is there.

Executive education · for boards and leadership teams

Start with the people who set the direction.

Boards and executives are being asked to make decisions about AI faster than they can build confidence in it. Every other decision follows from theirs: what to invest in, what to protect, and what to tell their people.

Our executive programme gives leadership teams enough working knowledge to set direction, ask the right questions and lead the change. Nobody needs to become an engineer.

01
Board briefing
What AI can and cannot do today, where the risks sit, and the questions directors should be asking management.
02
Executive workshop
Hands-on time with the tools, on your own documents and processes in a safe sandbox, so leaders know how it feels to use and not only what it promises.
03
Leading the change
How to talk with your people about AI: what is changing, what is not, and where each team fits. The communication is planned as carefully as the technology.
04
First priorities
Turning what the leadership team has learnt into a short list of priorities, each with an owner and a first step.

→ A leadership team that can set the direction and bring the organisation with it.

Ask about the programme

Why now

AI magnifies the team you have.

AI and automation raise what each person can get done. That makes the people who understand your members and clients, your data and your controls more valuable. Cutting the team to pay for the tools gives away the very thing the tools multiply.

Every organisation in the market can buy the same models. What separates them is execution: how quickly they adopt, how safely, and how many of their people can use it well. The ones that execute best will scale best, and execution depends on people.

Capacity to deliver · illustrative

Cut the team to fund the tools
Keep the team, hold off on the tools
Keep the team and give it the tools

Trading people for tools leaves capacity about where it started, with less of the organisation's knowledge in the building. Keeping the team and giving it the tools is the only option that grows.

Now is not the time to pull resources. It is the time to equip them.

Whole organisation

AI change is a team sport.

A model can be live in weeks. The organisation around it takes longer. Every function has a part to play, and each one needs to hear about the change in its own terms.

Board: “Are we taking the right risks, at the right pace?”; Sets the appetite for change and needs enough fluency to challenge management on it.
Executive team: “Where do we invest, and what do we ask of our people?”; Owns the story, the sequence and the promise made to staff about what the change means for them.
Risk, compliance and legal: “Can we stand behind this with the regulator?”; Brought in early, guardrails speed the work up. Brought in late, they stop it.
Member and client services: “What changes for the people we serve, and for us?”; Knows where members and clients get stuck, and is first to see whether a change actually helps them.
Investments: “Will this make our decisions better, or only faster?”; Brings the judgement that AI supports and cannot replace, and tests whether the outputs hold up.
Operations: “Which parts of my day change?”; Holds the process knowledge every automation depends on. Without it, the automation encodes the wrong process.
Technology and data: “Can we build it, run it and secure it?”; Turns pilots into systems that last, with the controls that a regulated business needs.
People and culture: “What skills will our people need next?”; Shapes roles, training and the way the change is talked about across the organisation.

We help every part of the business find where it fits.

We work with leaders to run the conversation across the whole organisation: briefing the board, helping executives tell a consistent story, and working with each team on what its work looks like when AI is part of it.

The aim is a workforce that knows where it is heading and has a hand in shaping it. People should hear about AI in their own jobs from their leaders, early and plainly, not from a rumour.

Approach

Complicated is not complex.

The difference decides how the work should be run.

Complicated

Hard, but knowable.

The inputs and outputs are known, or can be known. The work is difficult, but it can be planned, estimated and controlled from an implementation perspective.

Known or knowable inputs and outputs
Can be scoped, sequenced and estimated
Controlled through implementation discipline

Complex

Emergent by nature.

Not all inputs are known or understood, and the outputs cannot be specified in advance. The solution emerges from working the problem with the people who own it.

Inputs are incomplete or not yet understood
Outputs are discovered, not specified
Progress comes from short experiments and tight feedback

Most delivery models are built for complicated work. Data and AI work usually lives on the complex side, where the plan has to be discovered as you go.

We still run it like delivery: short experiments, clear decision points, working systems. That is Autourga's specialty.

For technology and risk teamsHow we judge a data process, and which controls AI-assisted work needs.How we build →

Discussion paper

One core, many boutiques.

How a shared data platform helps a diversified group of investment boutiques scale, while each keeps its own way of investing. What AI changes at the core and at the edge, who owns what, and three small steps to start.

→ 15 slides. Sent straight to your inbox.

Services

Decide. Build. Sustain.

Take one service or all three. Each stands alone, and together they cover the whole path from the first decision to a team that runs it themselves.

01Decide

Consulting

Straight answers on what to build, in what order, and how to run it. We work out where data and AI will pay off in your business and where they will not, then hand you a plan that your team can start on.

→ A plan you can start on Monday.

Data and AI strategy tied to your business plan
Operating model and workforce design
Change and communication planning across the organisation
Data governance and responsible-AI standards
Platform and architecture review
AI readiness and use-case prioritisation

02Build

Implementation

We build alongside your team and put working systems into production, with the controls a regulated business needs. Not a slide deck about what could be built.

→ Systems in production, documented and handed over.

Modern data platforms and pipelines
Automation that gives operations teams time back for higher-value work
LLM tools for research, documents and workflow
Sandboxed environments for testing AI safely
Governance built into the system, not bolted on

03Sustain

Training

So the capability stays after we leave. Training starts with the leadership team and reaches every team the change touches. It is built around the systems and data your people actually use, and it runs as hands-on work, not lectures.

→ Teams who can run what was built and build the next thing.

Executive and board education programmes
Data literacy for investment, member and operations teams
Applied generative AI in a regulated business
Automation workshops that find and scope real candidates

Experience

We have run data and AI inside a firm like yours.

Autourga comes out of running data and AI at one of Australia's largest global equities managers. We have been on the receiving end of the roadmap, the budget and the audit, so we know what it takes to get this work live in a regulated investment business, and what it takes to keep it running.

Built it: Data teams, platforms and operating models, from nothing to running.
Ran it: Automation and AI tools in daily use by investment and operations teams.
Governed it: Controls, standards and sandboxes that a regulated business can stand behind.
Brought people with it: Investment, operations, risk and technology teams taken through the change together, in language each of them could use.

Why the name

A machine that works.

auto·urga

noun · coined from Classical Greek

auto- — self, by itself
-urga — work, working

→ a thing that works by itself; a machine that works.

The name is built from the same Greek roots behind automatic and words like surgery and metallurgy: self-acting, and work. It is the standard we hold ourselves to. The systems we help teams build should be reliable enough to run, and people capable enough to run them.

Who it is for

Built for financial services.

We work with organisations across Australian financial services, including superannuation, banking, insurance, wealth and asset management.

Built around your work: Exercises and builds use your problems and your data, not borrowed case studies.
Practical over theoretical: Every engagement ends with something that runs or someone who can run it.
Governance aware: Designed for regulated environments, with risk, privacy and audit in mind from day one.
Whole organisation: Change is planned with every team it touches, not announced to them afterwards.

Contact

Tell us what you want running.

A short note on the problem is enough. We will come back with how we would approach it.

tom@autourga.com