April, 2026.- In the 2026 business ecosystem, Generative AI has moved from a novelty to critical infrastructure. Tim O’Neill, co-founder of Time Under Tension, stands at the forefront of this shift in Australia, leading the transition from simple experimentation to what he calls “production-ready applications.” With his firm’s recent appointment as an OpenAI Services Partner, O’Neill doesn’t just provide access to advanced language models; he helps organizations embed AI into their operational DNA. For Tim, success isn’t measured by how many employees use ChatGPT, but by how technology aligns with strategic goals, protected by solid governance and powered by a workforce that has moved from “scratching the surface” to mastering the art of the possible.
In this exclusive interview with Roastbrief, Tim O’Neill breaks down the three fundamental pillars for adoption: governance, architecture, and skills. Surprisingly, he highlights that the greatest challenge is not technical, but cultural; companies that attempt to impose AI from the top down often fail, while those that foster curiosity from the bottom up achieve genuine integration. Through his AI Academy, Tim is closing the confidence gap in marketing and business teams, proving that AI is a tool for growth and reinvention, not just efficiency. Discover how regulated sectors are navigating data security and why, in the global AI race, the human factor remains the most difficult—and necessary—component to integrate.
1. Beyond Gen AI Experimentation: You’ve noted that “access to gen AI is no longer the barrier; the real challenge is embedding AI into how businesses operate.” What does successful “embedding” look like in practice? How do you know when AI has moved from experimentation to true operational integration?
There are different stages of maturity, but a good baseline standard is:
- The business has identified and prioritised use cases for how gen AI can benefit the strategic goals
- A common AI Chatbot platform has been selected and rolled out with paid subscriptions (e.g. ChatGPT Business)
- Some AI governance is in place: data security, policies etc
- The staff have been trained in using the AI chatbot to achieve the priority use cases
Beyond this, there is a lot more that can be done!
2. The OpenAI Services Partnership: Time Under Tension is now an OpenAI Services Partner. What does this partnership enable that you couldn’t do before? How does it change your value proposition for Australian businesses?
This new collaboration strengthens how we support our clients using OpenAI models:
- Applying the latest capabilities in real-world use cases
- Enabling ChatGPT adoption with structured frameworks
- Building internal expertise through training and development
3. Governance, Skills, and Architecture: You identified three critical foundations for AI adoption: governance, skills, and architecture. Which of these is most commonly underestimated or overlooked by organizations rushing to deploy AI, and what are the consequences of getting it wrong?
Skills development is where we are seeing the biggest demand from Australian businesses and marketing teams. Part of the solution for skills development is hands-on training in the tools (e.g. ChatGPT, Codex, etc) but beyond this it’s inspiring people in what is possible. Generative AI unlocks some incredible potential for businesses, not just for efficiency but for growth and business model reinvention – we help to show the art of the possible.
4. Production-Ready Applications: Your work with clients like AGL, ScotPac, Baby Bunting, and Queensland Rail involves building “production-ready generative AI applications.” Can you walk us through an example of what a production-ready application looks like versus a proof-of-concept? What makes something ready for the real world?
A proof-of-concept simply demonstrates the feasibility of an idea, usually in a few weeks. There is then more work to be done to make that production ready, such as integration with other systems, data security, user management etc. These are important prerequisites before an application makes it to the hands of the end user.
5. The Australian AI Landscape: Demand for AI capability is accelerating across financial services, retail, tourism, government, and media in Australia. What makes the Australian market unique in its AI adoption journey? Are there sectors that are leading or lagging, and why?
I am not seeing much difference in the Australian market vs the US and UK where I have friends running similar businesses to Time Under Tension. I don’t think Australia is behind on AI Adoption, I think we’re on pace with the US and UK. Regulated industries (e.g. finance, health) tend to lag due to conservative approaches (rightly) with regards data security and governance. But this is not unique to Australia.
6. The AI Academy: You recently launched an AI Academy training offering. What skills gaps are you seeing in the market that the Academy is designed to address? How do you balance technical AI training with the change management and cultural shifts required for successful adoption?
Our training in the AI Academy is not technical, it’s for business users and marketing teams to increase their familiarity and confidence using off-the-shelf AI tools like ChatGPT. This is the biggest skills gap – almost everybody has tried (or regularly uses) ChatGPT, Copilot, Gemini or Claude – but without training in how to get the most of the tools, and also in a lot of cases using the free versions which are not as useful as the paid subscription. People are scratching the surface of what is possible!
Change management is really, really important – and core to our methodology. A top-down approach of making staff use AI chatbots to increase their productivity is not the way to do it. Businesses should encourage a bottom-up approach, involve the teams doing the work to identify the priority use cases, create an AI working group, and have them involved in decisions.
