About
Where this comes from, and why it is done from outside
Sixteen years in signals intelligence, then a decade delivering production AI inside Australian government operations: systems that make decisions about real people, under real accountability, and are still running on Monday.
Built and ran
The applied AI and data science branch of an Australian national security agency. Ten years, a team of up to 45, five locations. Led the Border Five data analytics working group, which brings together the border agencies of Australia, Canada, New Zealand, the UK and the US.
Delivered and held
Production systems in border, migration and mail. One assessed at approximately $2 billion in community benefit, and winner of the 2024 APS Data Science, Analytics and AI Award. Another took validated identity fraud detections past 20,000 a year.
Before that
Sixteen years in signals intelligence, building analytic capability across both the people and the technical systems, against a technology base that kept changing.
How an engagement works
Three steps, and you can start or stop at any of them
1 · Six-Pillar Self-Check
Free and about ten minutes, for the leader responsible for AI. Take the self-check.
2 · AI Capability Review
Two to three weeks, fixed fee, with an executive sponsor. Ends in a readout and a plan. Ask about the review.
3 · Modules
Fixed-fee modules, each of which can also be taken on its own, or an ongoing Fractional CAIO Office. See the services.
Independence
Inside an organisation, a question is never only a question
Some years ago RAND ran an AI readiness assessment on an organisation I was working in. I was one of many people they interviewed. I gave them a great deal, and I was quietly annoyed about it at the time. Not about the findings, but about the access. They could ask questions I could not ask. People made time for them who would not have made time for me. My analysis was not weaker than theirs. My position on the org chart was the problem.
The report they produced agreed with my own read at about ninety per cent. The other ten per cent was worth the engagement on its own, and I could not have reached it from where I sat.
That is the case for bringing in someone from outside, and it is narrower than the case usually made for it. It is not that the outsider knows more. It is that inside an organisation, anyone with a view on AI is also understood to have a stake in the answer: a team to protect, a budget to defend, a position taken in a meeting eighteen months ago that they are now expected to hold. Questions are heard as moves. Findings are read as bids. People calibrate what they say to who is asking, and the internal diagnosis comes out partial in a way that cannot be seen from inside it.
Someone with no history in the building, no team in the restructure and nothing to win from the conclusion gets different answers to the same questions.
Much of what comes back will be what somebody internally already suspected. That is not a wasted engagement. It is the first time it has been sayable.
The record
What was built, and what it cost to learn
- National security
- Intelligence
- Law enforcement
- Border security
- Migration
- Supply chain
- International trade and mail
I built the applied AI and data science branch behind that work and ran it for a decade. One system it delivered received a national data science award. Another delivered a 228% productivity improvement for a small team of specialists doing work that could not be scaled any other way. The branch also built the risk models supporting Australia's migration stream. Where an automated decision affects a person's circumstances, someone has to be able to intervene and answer for it. That is not a principle read somewhere. It is one that had to be designed for.
Alongside the systems sat the people: an organisation-wide AI and data credentialing program that more than 400 people completed, later extended to partner organisations here and overseas.
The failure modes in that work are not unique to government: a control routed around by Thursday, a capability that left with the team that built it, an escalation path nobody had walked. They appear wherever decisions are automated at scale under scrutiny. Regulated financial services and critical infrastructure are arriving at them now from a different direction, through prudential expectations and security obligations rather than operational exposure, with the same questions unanswered. The obligations are new. The failure modes are not.
The less flattering half of the record is the more useful half. I have killed projects that should not have shipped. I have been in the room when a production model degraded and the escalation path turned out to be theoretical. Frameworks are straightforward to write and I have written some. Having owned a system while it was going wrong is the part that is difficult to fake, and it is the part that shapes how Emeris works.
Emeris is not a vendor sales channel or a policy-writing exercise. The work starts with finding out what is actually true, then deciding what needs to change. When an engagement needs design, training or delivery capacity, it comes from partners we have chosen and stand behind.
Get in touch
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