Take part in the Technology Exchange
Designing the hybrid AI estate to build proprietary intelligence and improve economics
The AI landscape has evolved so rapidly that enterprise adoption has taken shape incrementally, model by model and team by team. With model performance, pricing and availability continually shifting, managing AI as a portfolio has become a strategic imperative.
Central to this is the learning loop generated through enterprise AI use: the data, feedback and evaluation assets that shape how AI systems improve over time. Retaining ownership and governance of that learning loop helps ensure intelligence compounds within the enterprise rather than outside it.
Done well, this approach builds proprietary intelligence, improves token unit economics and gives enterprises greater control over how their estate evolves.
This Technecology Exchange will explore the critical choices shaping the hybrid AI estate:
- Where frontier capability creates genuine advantage and where it is over-engineered.
- Where specialist intelligence must be owned to build defensible value.
- How to protect the learning generated through enterprise AI use.
- How to balance performance, flexibility and cost as the model landscape shifts.
Event details
Date: Thursday, 29 October 2026
Time: 5:30 - 9:00pm AEST
Location: Sydney CBD (to be confirmed upon registration)
Speakers
Andy Nolan
Global Vice President of AI Factory, Thoughtworks
Andy helps enterprises move AI from experimentation into reliable, production-scale capability. Through Thoughtworks’ AI Factory, he focuses on the strategic and engineering choices needed to scale AI safely and economically, including agentic AI, AI-ready data foundations, model selection, managed inference, GPU infrastructure and governance.
Luke Gerner
Principal Product & Experience Strategist | Director of Product - AI Factory, Thoughtworks
In the role of Director of Product for AI Factory, Luke helps shape product portfolio and roadmap. He's focused on enabling customer success for our clients, with a clear goal to ensure the value generated from client investment in AI scales disproportionately in relation to their spend, engagement, adoption and enablement built into the product rather than bolted on after the fact.
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