Steve Beatty, your MC for the day gets the day started.

Let’s be honest. Most of them aren’t that good. They work, but they don’t hold up. They’re usable, but not especially useful. AI has massively scaled our ability to produce output, but it hasn’t scaled the taste that differentiates great products from average ones.

Drawing on experience building and designing AI systems, I argue that taste is not a soft skill but a critical one. It is a muscle built when curiosity meets discernment, and exercised through decision-making. As making becomes easier, the challenge shifts to deciding what is good, what matters, and what should exist at all.

AI is exceptional at optimisation. Give it a goal and it will refine, accelerate, and automate within that frame. It will generate better outputs, faster workflows, cleaner systems. But it cannot decide that the question itself is wrong.

That's not a limitation of the technology. That's design's opening. The strategic value of design has never been execution. It's the power of reframing ... dissolving inherited assumptions and asking what actually needs to exist. In the AI era, this difference becomes critical. As optimisation becomes abundant in an era of intelligent systems, the most valuable design capability may be the disciplined courage to ask a different question.

AI will give you better horses indefinitely. Your job is to ask why we're still in the stable.

For many designers, "vibe coding" sits in an uncomfortable gap. We see the hype, but the reality often feels like a parlour trick: great for messy experimentation, but unreliable for professional work. It challenges everything we’ve learned about pixel perfection, forcing us into a new, non-deterministic medium where we must "guide" rather than "draw."

At Google Maps, we have moved past this disillusionment by treating AI prototyping not as a magic wand, but as a rigorous design discipline. By reviving foundational patterns from computer science and creative coding—such as state machines, parametric design, and recursion—we are moving from generating raw code to intentionally designing behaviour.

This session takes you inside the Google Maps UX pipeline to show how we integrate vibe prototyping into workflows that serve billions. We will explore how we use scrappy, AI-driven prototypes to validate the "feel" of dynamic interfaces and complex user flows long before engineering handover. You will leave with a practical framework for professionalizing your own AI prototypes, turning the unpredictable messiness of LLMs into scalable, human-centered experiences.

Personas are traditionally static artefacts: a poster, a slide, a section in a report. They can be useful, but easy to ignore once the project moves on. Persona agents change that relationship by turning a persona into an interactive interface; you can ask the persona questions, follow up to dive into details, and retrieve insight conversationally rather than hunting through repositories. In this session I’ll show how persona agents can make research more present in everyday design work, and what becomes newly possible when teams can interact with a persona as dialogue rather than documentation.

I will share practical lessons from building AI persona agents grounded in anonymised qualitative research. I will show how to improve their usefulness through curated verbatims, a coherent persona narrative and instructions that ground responses in evidence. You’ll leave with a lightweight blueprint you can adapt: a simple researchtoagent pipeline, prompt patterns that encourage sensible grounding, and an ethical framing that will help you position persona agents for adoption in your organisation as a way to democratise research, rather than a replacement for ongoing discovery.

Atlassian is rapidly pivoting into an AI-forward strategy, both in our features and our tools. I'm not going to get on stage and tell you AI is a magic technology that can do anything and everything; instead, I'll share some of the lessons already learned as we have gone down this road —not just about the technology itself, but also about how our customers are thinking about it, how you can upskill designers at scale and how to reconcile the problematic parts with the potential for real transformative value.

For thirty years, we've been designing one-way USER experiences. Now we are designing two-way Human+AI experiences. We had established principles we designed 1 user experiences with - consistency, hierarchy and removing friction. We got very good at it. And now, quietly, that mastery may be the most dangerous thing we bring to AI design. Because friction, it turns out, is precisely how humans calibrate trust. That moment of slight resistance before accepting a recommendation. The pause that lets a person feel they have agency. The explanation that slows things down but makes them feel seen. We've been trained our entire careers to sand those moments away when the experience is based on consistency, but doing so, we'll be building AI experiences that feel effortless, but remove users agency and but cannot be trusted.

This talk began not with research, but with regret; recognising my own work in a case study of AI harm during an ethics lecture at the London School of Economics. That discomfort became two years of asking other designers whether they recognised it too. Most did. Drawing on 240 interviews and eight frameworks built from that listening, this session invites designers to examine the most confronting possibility in their current practice.

Everyone in design right now is being sold the same vision: generate faster, explore more, ship sooner. And the tools genuinely deliver on that. But somewhere in the rush, a quieter question is getting lost — are we building the right thing? For the right person? And does anyone actually own that answer?

This presentation takes an unexpected route to that question. It brings together a group of designers, thinkers and builders who never wrote a prompt, never ran a sprint, never opened Figma — and makes the case that they already solved for this moment. Through Dieter Rams on the danger of endless addition, Ray Eames on what 'working good' really means, and a 1979 IBM training slide that reads like it was written last week, fast ≠ good argues that the principles that made great design great haven't changed — they've just become more urgent. Come for the dead designers. Leave with three things you can use on Monday.

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.