AI x design 2026 — Afternoon

AI experiences can look flawless in a prototype. The real risk starts after you ship the feature, when real users, facing real consequences, start pushing your model into use cases you didn’t plan for. This session draws on direct experience designing AI assistants for Australia's financial services sector to explore how to design for accuracy, accountability, and the inevitable moments when the model gets it wrong.

You'll leave with 3 actionable frameworks: how to design human in the loop checkpoints that keep users genuinely in control without killing productivity; how to build a design pattern language for AI behaviour, covering how AI acts differently from deterministic systems and what that means for the patterns you design around it; and how to build a research practice that tests the model before it tests your users, covering accuracy, edge case simulation, and how to know when it's ready for real users.

AI search, LLM retrieval, and automated agents all consume your content the same way assistive technology does — by reading structure, not pixels. They don't see your hero image. They don't care about your animation. They parse your headings, your alt text, your semantic markup, and your metadata. And if those things are missing or broken, the model builds an incomplete picture of your brand — and serves that incomplete picture to millions of people. Which means somewhere in your organisation right now, someone is writing a six-figure "AI-readiness strategy" that recommends clean markup, logical heading hierarchy, structured content, and real text alternatives. Your accessibility team has been asking for the same things since 2019. They were told it wasn't a priority. This is the AI strategy your accessibility team already wrote. They just didn't have the budget line to prove it. Tori has spent years building content architecture for Australian Government digital services — environments where accessibility isn't optional and where a missing heading level means real people can't access critical information. This talk takes that experience and reframes it for the AI moment: what actually matters in your structure, what's just theatre, and how to finally get accessibility funded by walking into the budget meeting and pointing at the AI line item. This is not a talk about adding AI features. It's about recognising that the most effective AI strategy most organisations can adopt is finishing the accessibility work they quietly shelved three years ago.

Takeaways

A dual-audience audit framework you can run against your own products on Monday Why the most impactful "AI optimisation" is boring, structural, and already in WCAG How to hijack the AI-readiness budget for the accessibility work that actually needed doing

As AI becomes embedded in decision-making, design, service delivery and operations, the question is no longer simply whether humans remain ’in the loop’, but which loops genuinely require human judgement, empathy and accountability. Building on ideas first explored by Walter Benjamin in The Work of Art in the Age of Mechanical Reproduction, this session explores how technology reshapes human agency, how mediation between people and intelligent systems evolves, and where the tension between automation and perception becomes most critical. What is uniquely human at work today and how do organisations ensure those capabilities are preserved where they matter most?

Drawing on practical AI case studies across education, retail, financial services, utilities, government, and health, this talk contrasts scenarios where human–AI collaboration delivers measurable value with those where misplaced automation introduces risk, bias, or diminished customer experience. These examples highlight alternative futures, emerging governance patterns, and pragmatic frameworks for deciding where humans should lead, guide or simply oversee AI systems. Attendees will leave with grounded insights, transferable lessons and tangible signals for recognising when humans are positioned in the right loops, and when they are not.

While technology choices are critical, culture is an invisible force that holds power over the success or failure of AI integration. This session moves beyond the technology and solutions to explore how organisational culture affects AI safety, trust and ultimately adoption. We will examine the role of HCD, systems thinking and ethics in designing for AI and agent augmented futures. Join us for a session designed to sharpen the questions you might ask of your leaders, ensuring your organization’s AI journey is both innovative and accountable.