Lead AI products with judgment, not jargon — scoping, evaluation, risk and what “good” looks like, for product leaders who ship without writing the code.
The hardest part of an AI product isn’t the model. It’s the judgment around it.
What decides an AI product is the judgment around the model: what to build, how to tell if it’s working, where it breaks, and what “responsible” means in practice. This six-week cohort gives product managers that judgment without turning them into engineers.
You’ll learn to scope AI features, read an evaluation, reason about cost, latency and risk, and have credible conversations with the engineers who build them.
Taught live by people who’ve shipped AI products.
The valuable skill is recognising the features where a model adds risk and no value — and being able to argue it.
If you can't interrogate an evaluation, you are taking engineering's word for whether the thing works. That is not product management.
Probabilistic systems fail in front of customers. Designing what happens then is a product decision, not an engineering afterthought.
Movement I · Weeks 01–02ScopeWhat is different about an AI feature, and which problems justify one.
Movement II · Weeks 03–04JudgeReading an evaluation credibly, and the cost and latency trade-offs behind it.
Movement III · Weeks 05–06Ship with your teamRisk, fallbacks, briefs and the conversations that decide whether it lands.
What is actually different about an AI featureProbabilistic output, no fixed spec, and a quality bar you have to define yourself. Why the usual product playbook bends here.
A quality bar for one of your own features, written down and defensible.
Scoping: what to build and what to refuseFinding the problems where a model genuinely helps, and recognising the ones where it adds risk and no value. Saying no is most of the job.
A written case for building one feature — or for refusing it.
Reading an evaluationWhat an eval is, what the numbers mean, and how to tell a meaningful result from a benchmark chosen to flatter. The single most useful literacy in this course.
The ability to challenge an evaluation in the room without bluffing.
Cost, latency and the shape of the trade-offWhat a request costs, why it is slow, and the levers — model choice, context, caching, retrieval — your engineers will reach for.
A cost and latency envelope for your feature that engineering would accept.
Failure, risk and responsibilityWhere AI features break in front of customers, what the AI6 practices ask of you, and how to design the fallback before you need it.
A fallback designed before launch, and a clear owner for the decision.
Working with the people who build itBriefs, acceptance criteria and the conversations that go wrong. You run a real scoping session and take the critique.
A brief and acceptance criteria your engineers would actually respect.
You take one AI feature — real, from your own product or a product you know well — and produce the artefact a good team actually needs: the problem statement, why a model is the right tool, the evaluation that would prove it works, the cost and latency envelope, the failure modes, and the fallback.
You present it to the cohort and defend the decision to build or not to build. A well-argued "no" scores as highly as a yes.
For product managers and product leaders who ship software. No coding required, but you should be comfortable reading a spec and arguing about trade-offs.
Bring a real feature you are considering, or one that did not work. Six weeks live, around four hours a week outside sessions.
Frameworks are great. But understanding what they abstract — the loop, the parsing, the failure modes — is what lets you actually debug and design them under pressure.
Amir founded, funds and directs AI Tech Institute, and still teaches. He holds a PhD and has spent more than twenty years building and leading AI, machine learning and data teams in Australian industry — the kind of work the programs here are drawn from rather than adapted to.
He currently leads asset performance and analytics at Synergy, Western Australia's state-owned electricity generator and retailer. Before that he led GenAI and MLOps program delivery at EY, working on platform modernisation for tier-one Australian banks; served as Chief AI Engineer at Hancock Prospecting, where he built enterprise GenAI platforms for executive decision support, led a Snowflake migration, stood up on-premises NVIDIA H100 infrastructure and Kubernetes clusters for production AI, and built a team of twelve engineers; and spent nearly five years as Tech Lead for ML and Data Science at Woodside Energy.
He has been teaching for twenty-one years. He is an Adjunct Associate Professor at the University of Western Australia, teaching quantitative analysis and decision-making at master's level, and holds Databricks University Alliance Faculty status and the AWS Certified AI Practitioner certification. He has published peer-reviewed research in Exploration Geophysics on machine learning for reservoir characterisation, and writes and records openly about the parts of AI engineering most courses skip — agent loops, termination logic and failure modes.
Cohorts are deliberately small so every piece of work gets reviewed properly and every presentation gets heard in the room. If an intake is full we’ll say so and hold your place for the next one — we won’t quietly oversell a cohort.
See all upcoming dates →The prerequisites for this program are in “Who it’s for” above. Our foundation-level programs and workshops assume none. If you’re unsure, ask on a qualification call — we would rather redirect you than take your money for the wrong cohort.
The commitment for this program is shown in the hero and in “Who it’s for”. Cohort programs run live sessions plus practice between them; workshops are a single block with no homework.
The work you shipped — defended in front of the cohort — and an honest recommendation on your next step. Sometimes that is another cohort with us, sometimes it isn’t; we will tell you which.
No. AI Tech Institute is not a Registered Training Organisation and this is not a nationally accredited qualification. It is professional education, assessed on the work you ship, with a certificate of completion from us. We won’t claim accreditation we don’t have.
Yes — we invoice organisations directly and can supply a scope and outcomes summary for your L&D or capability budget. If three or more people from one team want in, talk to us about a private cohort instead.
Full refund any time before the program starts. Once it has started we can’t refund the place, but we will carry you into a later intake where the circumstances warrant it. Ask us — we’re reasonable.
Thirty minutes with us and you’ll know whether this is your right next step — or which of our programs is. No pitch, no payment, and we will say so if the answer is “not this one”.
