Pushing the Boundaries of AI

Kat Jade Robinson is the Global CEO of Miroma Project Factory, a digital development studio specialising in behaviour change, gamification, and innovative technology solutions across health, wellbeing, and social impact sectors. Based in Sydney, Kat leads a global team delivering bespoke digital products that drive real-world outcomes for clients. With over 15 years of experience in strategy, digital, and innovation, she has been instrumental in expanding Miroma Project Factory’s footprint, aligning cross-functional teams across APAC, the UK, and the US. Passionate about human-centred design, Kat champions technology that engages, educates, and empowers, ensuring every solution is both meaningful and sustainable.

Thank you so much for joining us in this interview series. Before we dive in, our readers would love to learn a bit more about you. Can you tell us a bit about your childhood backstory and how you grew up?

I grew up in a family that valued practicality and curiosity, with a strong emphasis on being resourceful and independent. I always loved making things, learning how they worked and how they looked. I loved the dress-up box, craft box, garden, cooking, and building things with my brothers and sisters (cue the epic go-kart).

Interestingly, I initially wanted to pursue a career in marketing and design, not technology. I was seduced by the fantasy of it all. Over time, I realised the problems I wanted to solve were often core fundamental system, operational or logic-based issues wrapped in pretty interfaces. That’s how I found myself drawn to digital services, where creative problem-solving meets technology. It wasn’t a straight path, but it was always about fixing the invisible parts of systems to improve the output.

Can you share the most interesting story that happened to you since you began your career?

Not really a story that spans my entire career, but an interesting challenge we faced recently. A large non-profit organisation that mentors early career professionals in Banking and Finance reached out to us with a tough challenge. 

Could we build a groundbreaking AI prototype that is designed to bring ethical reflection into the everyday workflow of banking and finance professionals?

They didn’t want a flashy AI project. They needed a real, working solution to help people stay on track with their decision-making quandaries. 

We proposed a browser-based widget plugin, designed to meet people where they already are. Feedback from the users expressed a need for tools for self-reflection and guidance to help think through difficult professional challenges. This was no off-the-shelf solution, so we built and tested it in rapid sprints with their internal teams.

This was not about having AI replace human touch-points; it was about amplifying them and helping humans make better decisions. That project really forced us to genuinely understand ethics, bias, how to build, when and why and what to leave out. Dealing with decisions, young people, and challenging industries requires great problem-solving. 

None of us are able to achieve success without some help along the way. Is there a particular person who you are grateful towards who helped get you to where you are? Can you share a story about that?

The founder of our agency was pivotal. Early in my leadership journey, I was pushing ideas and concepts that, at the time, weren’t mainstream and frankly made everyone feel uncomfortable as change was coming quick and fast. 

Rather than pushing back, she said something that stuck with me: “If you see where the future is heading, lead the way, don’t wait for permission.” That endorsement gave me the runway to invest in ideas before they became a client demand.

When AI adoption surged we weren’t scrambling to catch up; we were already delivering AI-driven projects with real case studies.  Forward-thinking experimentation-based leadership still shapes how I mentor my team today.

Can you please give us your favourite "Life Lesson Quote"? Can you share how that was relevant to you in your life?

“Luck is when preparation meets opportunity.”

Anyone who works with me knows I love a list, I follow up and really love what I do. I am organised and love a good spreadsheet, so I feel very lucky in turn. But I think you own luck if you put the energy in. 

Opportunities don’t wait for you to be “ready.” You have to stay ahead of the curve, continuously preparing, so that when the door opens, even if briefly, you’re equipped to walk through it at full speed.

You are a successful business leader. Which three character traits do you think were most instrumental to your success? Can you please share a story or example for each?

I am not done yet, and my version of success is mostly different from yours.

1. Curiosity Always 

You never know the intent or direction of something or someone if you don't ask. We assume so much as humans and frankly, are pretty rubbish mind readers. Stop, ask, dig, question. Go back and ask why.

2. Resilience can be slow and steady 

Consistency allows others to falter. If you're a safe harbour for your team and clients, they will use you as a sounding board and place to test and fail. However, don’t forget to find your own place of solace along the way.

3. Empowering Others through Trust

When delivering a complex behavioural change platform for a public health client, we empowered a cross-functional team to lead solution design. By trusting their expertise, they developed engagement strategies I wouldn’t have conceived on my own. Their ownership not only enhanced the final product but also set a template for how we now structure teams on projects with autonomy driving innovation.

Share the story of what inspired you to start working with AI. Was there a particular problem or opportunity that motivated you?

We started working on AI years ago when we were doing skunkworks projects around IBM Watson, looking at how tools can bring personalisation and compelling story telling into the digital world. 

Traditional CMS-driven updates weren’t agile enough, and personalising messages manually at scale wasn’t feasible. That’s when we explored AI-driven personalisation, small, adaptive content modules that responded to user behaviour in real time. It wasn’t about flashy AI features; it was about making the user experience feel human and relevant when they needed support the most.

That project was a turning point. Since then, we have looked at when to apply technology solutions that might not be mainstream, but that solve practical, high-impact problems, especially where human behaviour and technology intersect.

Can you describe a moment when AI achieved something you once thought impossible. What was the breakthrough, and how did it impact your approach going forward?

One breakthrough moment for me was seeing how AI could change people’s eating habits in ways I once thought were impossible.

We’re often lazy about our diet sticking to the same meals, missing out on new tastes, experiences, and often healthier options. The challenge was: how do we encourage people to “eat outside the box”?

Our solution was an AI-powered “surprise engine.” It considered individual food likes, dislikes, and assumptions, then pushed users toward dishes they’d likely enjoy but might not naturally choose. We built this into a cross-platform app with weekly meals, recipes, ratings, and reviews, encouraging participants to explore new foods.

In a clinical study with 40 participants, users committed to trying a minimum number of new meals weekly, rating and uploading photos of their creations. The AI adapted based on their feedback, fine-tuning suggestions over time. Small but mighty changes!

Watching people expand their diets and enjoy it, was a turning point. It showed me AI can be more than functional; it can be persuasive, playful, and transformative. That insight continues to shape how I design digital health and behavior-change solutions.

Please talk about a challenge you faced when working with AI. How did you overcome it, and what was the outcome?

One major challenge is the classic “black box” problem, which involves building trust in AI-driven decisions when stakeholders don’t understand how the AI reached its conclusions. We faced this when rolling out an AI-driven health concierge tool.

The breakthrough came when we shifted focus from just accuracy to explainability. We re-architected the system to provide transparent reasoning for its recommendations, even if it meant compromising slightly on performance metrics. Transparency can be as important as technical superiority.

Can you share an example of how your work with AI has had a meaningful impact (on others, on business results, etc)? What was the situation, and what difference did it make?

One of the most meaningful AI projects we’ve delivered was the development of HI, an AI assistant co-created with The Ethics Centre to help finance professionals make ethical decisions in real time. Finance workers face ethical dilemmas daily but often lack the tools or time to reflect deeply. HI is designed not to replace human judgment, but to enhance it by prompting thoughtful reflection within existing workflows like Slack and Teams.

By blending natural language processing with sector-specific insight, HI guides users through complex, value-driven decisions, supporting empathy and ethical awareness rather than offering prescriptive answers. Early beta testing showed that 95% of users felt it helped clarify how their values influenced decisions, and 82% believed it would be valuable industry-wide.

This project demonstrated how AI can make a real difference by embedding ethical thinking into everyday work rather than treating it as an afterthought. It transformed a conceptual challenge into a practical, scalable solution that improves decision quality and builds ethical capability over time. For me, it reinforced that AI’s true impact lies in augmenting human strengths, especially in complex, sensitive areas like finance ethics.

Here is the main question for our discussion. Based on your experience and success, can you please share “Five Things You Need To Know To Help Shape The Future of AI”? (Please share a story or an example, for each.)

1. AI is a Partner, Not a Replacement

In a legal firm collaboration, AI document review reduced admin hours, but lawyers focused more on strategic analysis, a more efficient way of delivering the same or better results overall.

1. Data Quality is Everything

In one AI chatbot rollout, the biggest gains came after we fixed inconsistent data tags, not after fancy model tuning.

1. Ethics Can’t Be an Afterthought

We once caught a bias in an AI model’s outputs during a prototype phase. Embedding fairness audits early is now a standard process for us.

1. Explainability Drives Adoption

We deployed “why this recommendation” tooltips, even though the AI’s accuracy hadn’t changed, giving people insight and transparency. 

1. Continuous Learning is Non-Negotiable

Feedback loops for human staff to flag misinterpretations, keeping accuracy high with minimal overhead, is mandatory for a good product. 

When you think about the future of AI, what excites you the most, and how do you see your work contributing to that future?

The potential for AI to integrate invisibly into workflows, where it empowers rather than overwhelms. We’re working on projects where AI quietly augments day-to-day tasks, whether it’s in health, public sector services, or internal enterprise tools. The future isn’t about AI replacing people; it’s about AI making human work more meaningful and frictionless.

What advice would you give to other entrepreneurs who want to innovate in AI? Can you share a story from your experience that illustrates your advice?

Solve a real problem. One of our early AI wins wasn’t glamorous; it was an AI training tool designed to help doctors deliver bad news in a more patient-friendly way. We built an AI-driven “personality” that combined video, freeform text, and natural speech, creating realistic scenarios where doctors had to interact with the virtual patient and achieve the goal of delivering news with empathy and care. The system used emotion-driven video prompts and self-assessment to reinforce learning.

It worked, doctors reported feeling more prepared and confident in handling those difficult conversations.

Is there a person in the world, or in the US with whom you would like to have a private breakfast or lunch, and why? He or she might just see this, especially if we tag them. :-)

Dr. Fei-Fei Li. Her advocacy for human-centred AI resonates deeply with how we approach projects at MPF. I’d love to hear her views on the next evolution of AI-human collaboration, especially as we are about to embark on a large computer vision feature for a health product.