Agenda

AI can now generate most of the code in a product, but speed comes with a risk: messy architecture, security gaps, and technical debt that makes teams slower over time.
In this workshop, Rory breaks down the architecture basics every non developer needs when building with AI.
You will learn how to think about modularisation, security and scale so that prompt built apps do not turn into brittle, non maintainable systems.
As designers and developers converge and single builders can ship full products, knowing what to protect, how to structure work, and where debt hides has become a core leadership skill.

By the end of the workshop, you will be able to:

• Spot the most common architecture mistakes in AI generated products before they become expensive to fix
• Apply simple modularisation patterns that keep features decoupled and easier to change
• Define baseline security checks and guardrails for prompt to production workflows
• Ask better technical questions and review AI output with more confidence, even if you do not code
• Create a lightweight architecture checklist to keep fast shipping scalable over time

In this strategic workshop, product leaders will use Agent Experience (AX) testing as a high-level diagnostic tool to audit their organization’s website, SaaS, or online app. We will test live, public interfaces to reveal not just where they break for AI agents, but what those breakages signal about your organization’s readiness for agent traffic. Rather than discussing tactical design fixes, we will focus on the commercial and operational risks exposed by these failures—from hallucination liabilities to lost revenue channels. You will leave with a clear view of the "Agent Gap" in your product strategy and a framework for orchestrating the cross-functional response required to close it.

Outcomes:

• Assess Risk: Witness firsthand how current public-facing journeys fail for agents, translating technical friction into business risk and projected revenue loss.
• Uncover Organizational Blind Spots: Identify systemic blockers that interface tweaks cannot solve, isolating root causes that require intervention from Legal, Security, Engineering, or other departmental leadership.
• Establish Governance: Leave with a framework to benchmark progress, justify headcount or budget shifts, and drive alignment across conflicting stakeholder incentives.

In this session, you’ll become an AI-Enhanced Product Creator. We'll start with how Product Creators have moved beyond basic prompting into higher-leverage activities like tooling, agents and prototyping. Then, we'll go deep into AI prototyping with a hands-on AI Hackathon.

Attendees will learn how non-engineers can quickly turn ideas into interactive experiences with an intro to vibe coding, a live demo and then a hands-on hackathon. Each attendee will pick an idea (or bring their own), craft a strong prompt, “code” an interactive prototype (optionally wiring in real APIs), and share prototypes and learnings with the group.

Key Topics:

• The AI-Enhanced Product Creator. Learn the four personas of AI Product Creators.
• AI Techniques for Product Creators. Progress from prompting to higher level AI activities such as tooling, agents, prototyping and coding.
• Intro to AI Prototyping/Vibe Coding. Learn how non-engineers with vibe coding tools can quickly manifest ideas into interactive experiences and test with customers faster. Includes a live demo creating a vibe-coded app.
• AI Hackathon: Creating Prototypes with Vibe Prototyping Tools. Bring an idea or pick an idea, develop a prompt, and “code” an interactive prototype. Add real APIs for more realistic interactivity. Cross-pollinate progress by sharing prototypes with the group. All in one session.

*Note: We will provide credits for a popular vibe coding platform you can use during the session.

Design within teams is hard: constraints, shifting scope, busy stakeholders, and a calendar that does not care about your ideal process. In this workshop, Evan takes a “real-world design operating system” and gives a practical method for a human-first, AI-accelerated feedback loop using AI personas.
You will start with real human input, then build a lightweight synthetic user based on that context, run live design feedback against it, and compare the synthetic output to the human response to spot where it aligns, where it diverges, and where it is just confidently wrong. The goal is not to replace research. It is building a faster decision-making workflow that helps small or stretched teams test, refine and communicate clearly with PMs and stakeholders.
Practical, realistic and intentionally un-hypey. Augment, do not abdicate.

What you’ll walk away with:

• A step-by-step synthetic user workflow to capture real stakeholder input, turn it into an agent, and use it to speed up iteration without pretending it is “real users.”
• How to configure an AI persona through better setup and prompts: define the right context and boundaries, then generate critique that flags clarity vs friction (less garbage, more signal).
• A simple validation loop: how to compare AI feedback to human feedback, spot hallucinations and overconfidence, and use the overlap to prioritise what to fix next.
• Faster collaboration with PMs and reduce reruns: How to use the loop to show progress, iterate live, and communicate decisions in outcome language that builds trust across product, design and engineering.

Design leaders today are under pressure. You must prove impact, manage growth, scale, and operate with the clarity and maturity the business expects. Meanwhile, most design organizations are navigating constant change. You need to evolve your focus beyond the craft. In order to expand your influence within your organizations, you must showcase the impact design has in business and not the methods and processes you use. Jose Coronado will take participants through three areas of design leadership development to build a strong foundation of impact. Participants will learn frameworks that will help them develop their own playbook for the road ahead in their professional journeys.
The workshop is based on lessons learned from leading the transformation of in-house design teams to drive a change from tactical to strategic impact. The stories and the approaches Jose shares are based on his leadership journey and are supplemented by interviewing and working with emerging and established design leaders from around the world. You will have key takeaways for your own journey to advance your professional development and influence in your organizations.
Who is this workshop for?
Emerging and growing design leaders will learn in this workshop a flexible blueprint that you can apply for your own career development and it will also help you develop your team.

What you will learn:

• Expanding your influence in the organization
• Aligning your work with areas of strategic impact
• Articulating and promoting the impact of design in the organization

AI can now generate most of the code in a product, but speed comes with a risk: messy architecture, security gaps, and technical debt that makes teams slower over time.
In this workshop, Rory breaks down the architecture basics every non developer needs when building with AI.
You will learn how to think about modularisation, security and scale so that prompt built apps do not turn into brittle, non maintainable systems.
As designers and developers converge and single builders can ship full products, knowing what to protect, how to structure work, and where debt hides has become a core leadership skill.

By the end of the workshop, you will be able to:

• Spot the most common architecture mistakes in AI generated products before they become expensive to fix
• Apply simple modularisation patterns that keep features decoupled and easier to change
• Define baseline security checks and guardrails for prompt to production workflows
• Ask better technical questions and review AI output with more confidence, even if you do not code
• Create a lightweight architecture checklist to keep fast shipping scalable over time

Stakeholder alignment sounds simple until priorities collide, politics show up, and your roadmap starts to wobble. In this highly interactive workshop, Melissa Appel uses a fast paced simulation to help teams practise the real work of alignment: understanding incentives, negotiating trade offs, and landing a decision people will actually support.
Participants work in small groups as a fictional executive team, with each person playing a role with their own perspective and agenda. Across multiple rounds, you will build a shared recommendation for a CEO who is out of action, learning practical tactics for aligning peers and senior stakeholders along the way. The debrief turns the experience into repeatable tools you can apply immediately inside your own organisation.
Outcomes

• Diagnose why alignment breaks down (competing incentives, unclear goals, misread power dynamics) and what to do about it
• Practise influence techniques that build trust and reduce politics, even with difficult stakeholders
• Learn how to align groups around outcomes and trade-offs, not just a list of requests
• Create a simple stakeholder map and messaging plan that speaks to what different leaders care about
• Leave with a set of next steps to drive executive and cross-functional alignment back at work

In this session, you’ll become an AI-Enhanced Product Creator. We'll start with how Product Creators have moved beyond basic prompting into higher-leverage activities like tooling, agents and prototyping. Then, we'll go deep into AI prototyping with a hands-on AI Hackathon.

Attendees will learn how non-engineers can quickly turn ideas into interactive experiences with an intro to vibe coding, a live demo and then a hands-on hackathon. Each attendee will pick an idea (or bring their own), craft a strong prompt, “code” an interactive prototype (optionally wiring in real APIs), and share prototypes and learnings with the group.

Key Topics:

• The AI-Enhanced Product Creator. Learn the four personas of AI Product Creators.
• AI Techniques for Product Creators. Progress from prompting to higher level AI activities such as tooling, agents, prototyping and coding.
• Intro to AI Prototyping/Vibe Coding. Learn how non-engineers with vibe coding tools can quickly manifest ideas into interactive experiences and test with customers faster. Includes a live demo creating a vibe-coded app.
• AI Hackathon: Creating Prototypes with Vibe Prototyping Tools. Bring an idea or pick an idea, develop a prompt, and “code” an interactive prototype. Add real APIs for more realistic interactivity. Cross-pollinate progress by sharing prototypes with the group. All in one session.

*Note: We will provide credits for a popular vibe coding platform you can use during the session.

Traditional prototyping creates organizational friction—siloed teams, competing interpretations, and validation cycles that stall innovation momentum. This hands-on workshop with Shruti Gupta demonstrates how AI-assisted prototyping transforms your innovation velocity, reducing design validation cycles by 40% while strengthening cross-functional alignment. You'll discover where AI creates exponential time advantages, which strategic decisions still require human oversight, and how to architect a repeatable workflow that scales across your organization. Then you'll build a working prototype using rapid iteration techniques, intelligent guardrails, and cost governance frameworks designed for enterprise innovation teams.

Outcome:

• Reduce design validation cycles by up to 40% using AI assisted prototyping techniques that work in enterprise environments
• Build stronger cross functional alignment by removing ambiguity, competing interpretations and siloed feedback loops
• Identify where AI delivers the biggest time advantages, and where human oversight still matters most
• Apply a repeatable workflow with guardrails and cost governance that scales across teams and programs

AI doesn’t just change products. It changes how teams collaborate.
In this interactive workshop, we explore how product managers, designers and engineers can work together more effectively when building with generative AI. Using real-world examples, including lessons from the Gatorade AI project, we will look at how teams can align around customer needs, apply AI responsibly, and validate decisions earlier in the product lifecycle.
As AI accelerates how ideas turn into outputs, the challenge shifts from execution to coordination, judgement, and shared understanding. This session focuses on how teams can stay aligned, ask better questions, and avoid common pitfalls when integrating AI into their workflows.

By the end of the workshop, you will be able to:

• Identify where AI can support collaboration across product, design and engineering
• Align teams around customer and business problems and how to use generative AI to solve them
• Apply practical approaches to responsible AI use within teams
• Validate ideas earlier using AI to reduce rework
• Facilitate better conversations and decision-making when AI is part of the process

For years, we built product teams around a simple assumption: building software was expensive, slow, and required deep specialisation.
We decomposed the work across functions; product managers to define value, designers to shape experiences, engineers to deliver solutions, QA to catch issues, and project managers to hold it all together.
That model made sense when execution was the bottleneck. But AI has fundamentally changed the cost of building.
Today, AI can design interfaces, generate code, write tests, support deployment, and even help shape architecture. Shipping something has never been faster. The challenge is that it’s also never been easier to ship something confidently wrong.
The bottleneck is no longer just building the product right. It’s building the right product.
In this talk, Rory Madden explores what AI can, and can’t, do across the software delivery lifecycle, where human judgment still creates the most value, and how product teams must evolve as roles begin to merge and coordination becomes the new constraint.

Losing nearly half your team forces a reckoning. Josh Payton shares how Wise turned crisis into renewal, rebuilding its design organisation from burnout and late-night firefighting to a culture of autonomy, balance, and impact. Discover how mindset, maturity, and metrics turned attrition into advocacy.

Key Outcomes:

• Identifying the cultural and operational triggers that drive attrition.
• Learning actionable tactics for rebuilding trust and redesigning team workflows.
• Exploring frameworks that balance delivery pressure with long-term maturity.
• Seeing how transparency and empowerment rebuild both people and performance.

As AI agents begin to design, code, and deploy products, the boundaries between design and development are vanishing. Netlify’s Chief Technology Officer, Dana Lawson argues that a new discipline is emerging, Agent Experience (AX), where UX and DX merge into one continuous creative flow.
In this provocative talk, Dana explores what happens when your “users” and your “developers” are increasingly AI-driven, and why human taste and values are becoming the most important design tools of all.

Challenges:

• Building for AI agents that don’t interact through traditional UIs
• Keeping humans in the loop as automation reshapes workflows
• Maintaining creativity and quality when “anyone” can generate design or code

Key Learnings:

• How UX and DX are converging into a unified discipline powered by AI
• Why taste and judgment are the new competitive advantages
• How to structure workflows that keep human purpose central in an AI-driven world

AI is compressing the distance between idea and execution. Engineers design. Designers generate code. Product managers prototype. The traditional boundaries of product development are shifting and so is accountability.
When the cost of delivery tends towards zero and iteration is instant, the bottleneck moves from code delivery to product judgment.
How do we make sure our features are adding value and not just complexity and bloat?
Who defines quality?
Who is accountable for coherence across teams and products?
How do leaders maintain standards as workflows become fluid?
What does a high-performing product organization look like when the linear team model no longer applies?
This executive session brings together product, design, and technology leaders to examine how AI is reshaping ownership, governance, and competitive advantage.

Working with AI can often feel intimidating and opaque, but thoughtful UX can change that. This talk highlights how Kent’s team leveraged timeless design methods to create more intuitive, informative, and engaging AI interfaces through visualization. Drawing on his work with Google’s Personal Health Coach, Kent will walk through the process of integrating visual data into a chatbot environment. He will share guiding principles and practical takeaways for anyone building human-centered AI data experiences, created by humans, for humans.

What you’ll learn:

• A behind-the-scenes look at integrating co-design and UX research into AI workflows.
• Why data visualization is an essential component of a generative AI interface.
• Key lessons learned when applying data visualization to generative AI products.

AI increases speed. Boards love speed. Investors love speed. But speed without validation creates expensive mistakes at scale.
AI has collapsed the time between idea and execution. Teams can generate concepts, prototypes, and even fully functional experiences in days. But acceleration increases risk. Assumptions scale faster. Confident decisions are made on thinner evidence.
In an AI-driven product environment, the real risk is not moving too slowly. It is moving quickly in the wrong direction.
How do leaders ensure customer truth keeps pace with technical velocity?
How do you prevent beautifully executed irrelevance?
What does rigorous discovery look like when iteration cycles shrink dramatically?
This session explores how leading product organizations balance acceleration with evidence to protect long-term advantage.
This speaks directly to business risk and capital allocation. Very executive.

Electronic Arts is working to shift from product-centric roadmaps to more experience-led journeys. In this session, Andra Bond and Olivia Lucas will share how tools like the Experience Atlas and evolution mapping help their team keep the customer at the center of the work, create shared understanding across teams, and carry that alignment through delivery.
They will walk through how EA frames broad business asks in terms of real customer needs, uses journey-level signals to better understand problem spaces, and turns future-state visions into complete iterative solutions that can actually be built across multiple teams. The session will offer practical examples and honest lessons from working in a large, complex organization where clarity, coordination, and value can easily get lost.

Key Learnings

• Frame problems faster by grounding broad business requests in real customer needs
• Understand the role of journey-level signals in shaping and sizing problem spaces
• See how EA moves from future-state vision to iterative, complete solutions
• Learn how to preserve value creation across multiple teams during delivery

If products are never finished, why do we treat UX teams like they are? Ben Hewett shares the real story of co-founding UX at Allied Solutions and growing from 3 people into a team of 25. You will see how the team shifted from a project based “internal agency” to being embedded with product teams as trusted partners. Along the way: resistance, missteps, and the practical changes that turned “they make it look pretty” into “they deliver business value”.
This is a talk for leaders building, rebuilding, or repositioning UX inside complex organisations, using the same mindset we apply to products: iterate, learn, and evolve.

Outcomes

• Spot the signals that your team’s operating model is limiting influence, and know when to move from project delivery to embedded product partnership
• Build a team vision, values, and internal identity that creates alignment inside UX and credibility across the organisation
• Translate UX work into business outcomes (revenue, cost, risk, time) using case studies, metrics, and outcome focused storytelling
• Create advocates and earn trust at scale through practical behaviours that make collaboration stick, even when the organisation pushes back

Most enterprise transformations stall in meetings. At EMD Digital, change is shipped as software. Workflow platforms and creative automation make better practices the default, so teams adopt new ways of working through use. With AI, these tools become adaptive systems that guide, learn, and scale faster than top-down plans. In this session, Paul Svoboda shows how a product-first, AI-accelerated approach makes transformation tangible, repeatable, and self-sustaining.

Key learnings:

• Lead transformation through products, not presentations
• Use AI to amplify adoption and learning
• Build tools that make change self-reinforcing
• Replace alignment meetings with systems that do the teaching

AI has made it easier than ever to build quickly, but speed alone doesn’t lead to better decisions. As teams move faster, the need for real, in-the-moment user understanding only grows.

In this session, Bhavik Gandecha from Dscout breaks down what AI can (and can’t) do for modern product and research teams—and where human judgment still plays a critical role.

You’ll see how teams are using AI to speed up study creation, analysis, and decision-making—while relying on real human feedback to stay accurate and relevant.

Come ready to share your biggest wins (and toughest misses) using AI in research. Where has it helped you move faster and where did it miss the mark?

As McKesson advances its three-year AI transformation, the focus across oncology and multispecialty care is on exploring how AI can genuinely improve clinician productivity and patient outcomes, while understanding where it must be constrained.
Anya Gerasimchuk shares how her team leverages AI to reduce administrative burden, protect trust through strong UX guardrails, and operate within the data and compliance constraints of high-risk healthcare environments.

Key Outcomes

• Identify high-value, low-risk entry points for AI in oncology and EHR workflows that truly reduce clinicians’ administrative burden.
• Design AI-assisted experiences that preserve context, transparency, and trust for care teams using UX guardrails and explainability patterns.
• Use AI to accelerate the product development lifecycle, from synthesizing user research into design specifications to driving actionable insights.
• Explore the constraints that healthcare environments impose on successful AI implementation, such as the availability of structured data, a unique challenge in its own right.

Research is becoming faster, more connected, and easier to access, but the gap between insight and action hasn't closed. Tucker will open with what Great Question is seeing since rolling out its MCP earlier this year (one of the first UXR platforms to do so). What's emerging is a split: most teams are using MCPs to speed up insight access, while a smaller set are using them to route insights into the tools where decisions actually happen.
From there, Tucker will lead an interactive discussion with the audience. Why does research still get stored and forgotten? Why do product conversations fall back on opinion or speed, even when customer insights are available? What's actually different in an AI-driven workflow and what isn't?
Through shared experiences, we'll look at how teams are adapting, where MCPs are helping (and where they're creating new failure modes), and what it takes to get customer voice into decisions at the moment it matters.

As AI reshapes how we search, shop, and create, Pinterest’s design-led teams are redefining what it means to stay human in the loop. Larkin Brown shares how UX research and design leadership at Pinterest anchor innovation in user empathy while harnessing generative AI to amplify, not replace, human understanding.

Outcomes:

• Learn how to integrate generative AI into design workflows without losing connection to real users
• Discover collaborative practices that unite research, design, and engineering around shared insights
• Explore case studies from Pinterest Assistant and visual search innovation
• Gain strategies to navigate ethical and practical challenges in human-AI interaction

For expert users, the job is not to make your UI sticky, it is to make it disappear. Join Benjamin Humphrey as he interviews Fahad Osmani. They'll break down how platform context changes everything, the hiring profile for designers, the role of domain expertise, and the metric shift from clicks to completion and time to value.

• Diagnostic, five questions to confirm you are in platform territory
• Metrics that matter, how to set up time to value and task completion the right way
• Interface strategy, cockpit patterns, progressive exposure, and safe defaults for expert work

AI is changing product work fast. It can write code, accelerate research, support design decisions and remove hours of repetitive work. But is it taking your job, or changing it?
In this debate, Dana Lawson and Christina Goldschmidt take opposing sides on one of the most urgent questions facing modern teams. One side argues that AI is reducing the need for specialists by collapsing roles, raising expectations and putting more pressure on fewer individuals. The other argues that AI is changing the job, not replacing it, freeing skilled practitioners to focus on judgement, systems thinking, strategy, creativity, and the decisions that matter most.
Moderated by Ryan Leffel, this session explores what happens as AI reshapes not just coding, but also design, research, delivery, and product decision-making. What does that mean for the future of cross-functional teams? Will experienced product managers, designers, and engineers become more valuable, or will the pressure on individuals become unsustainable? And if AI takes on more of the work across the product lifecycle, who stays accountable for what gets built, why it gets built, and whether it should exist at all?

When growth accelerates, hierarchies slow you down. Randy Hunt shares how Notion’s design organization engineered itself around skills and behaviors rather than titles and functions, a shift he calls a return to the fundamentals of “making and building.” As adoption soared from personal users to global enterprises, this model helped the team stay fluid, creative, and connected to the craft of design. The talk reveals how a skill-based culture fosters adaptability, ownership, and speed when predictability disappears.
Takeaways:

• How to structure design teams around skills, not roles
• Preserving creative DNA through growth, unpredictability, and complexity.
• Why rediscovering fundamentals — curiosity, craft, and shared behaviors, enables sustainable scaling.

Channels have multiplied (web, app, store, and now chat interfaces like ChatGPT and Perplexity) but customer fundamentals still read: search, discover, purchase, receive. This talk shares how Sephora revisits flows to remove cognitive load, prioritises tech investment, and uses experimentation to keep journeys simple as features scale. The result: faster delivery, fewer regressions, happier customers.
Outcomes:

• Map the core “search→discover→purchase→receive” loop and spot channel-specific friction.
• Apply a simplicity audit to tame feature creep in basket, checkout, and omni hand-offs.
• Prioritise tech investments with an experimentation backlog tied to customer impact and ROI.
• Operationalise conversational commerce (intent models, guardrails, hand-off patterns) without fragmenting your stack.

This session examines two opposing views on unifying product and engineering under a single CPTO. One side argues that combining functions removes friction, clarifies priorities and strengthens customer focus. The other side argues that separating product and engineering creates healthier tension, clearer ownership and better technical depth. Drawing on Ellen’s experience stepping into a unified CPTO role at Synctera, and Deb Kawamoto’s experience at Vanta leading through hypergrowth, shifting design into a more strategic role, and navigating blurred ownership across AI, data, product and engineering, the debate challenges assumptions about alignment, autonomy and organisational design. Together, they will explore what happens when traditional boundaries stop reflecting how modern teams actually build.

Debate Outcomes:

• Compare the benefits and risks of unifying product and engineering under one leader.
• Explore how ownership shifts when teams are building across AI, data, compliance and product at the same time.
• Understand how customer problem ownership can support or undermine functional clarity.
• Gain insight into when organisational unity accelerates delivery and when it creates new bottlenecks.
• Consider how strategic design leadership can reshape decision-making in cross-functional teams.

Open source transforms product development, the roadmap is visible and feedback arrives early. Vicky Chin shares how Firefox leverages this visibility to accelerate practical delivery. Learn how engaging the community and previewing in-progress features through Firefox Labs generate quick insights, allowing teams to minimize rework, concentrate engineering efforts on user-validated needs, and improve quality before launch.

Outcomes:

• Build a “preview to learn” loop that validates demand early and keeps delivery focused.
• Decide what to expose and when using clear criteria for readiness, while assessing for risk and user impact.
• Turn community feedback into actionable product decisions through lightweight triage and signal scoring.
• Apply open development patterns that strengthen product, design, and engineering alignment without slowing velocity.

As AI starts generating more of the interface layer, design systems are no longer just component libraries. They are becoming the rules, constraints, and infrastructure behind dynamic experiences. In this candid discussion moderated by Jim Morris, Donnie D’Amato and Alexander Wilson debate what the next generation of design systems looks like, where they disagree, and what that means for the teams building them.

How do modern product teams actually work together day-to-day? This interactive session explores how collaboration is evolving across product, design, research, and engineering, with a short introduction to concepts like teaming and mobbing before opening the conversation to the audience. Together, we’ll unpack real practices, trade-offs, and patterns shaping cross-functional product development today.

AI products only succeed when they are useful, understandable, and grounded in real human needs. In this Q&A deep dive, Kent Eisenhuth, Anya Gerasimchuk, and Larkin Brown will help you explore how leading teams are building AI experiences that balance innovation with clarity, trust, and strong design principles, with plenty of space for you to ask direct questions and dig into the practical realities behind the work.

Kent will share how data visualization can make generative AI experiences more intuitive, informative, and human-centered. Anya will bring the healthcare perspective, showing how AI can reduce administrative burden, improve productivity, and support better outcomes while still operating within strict constraints around trust, compliance, and data. Larkin will show how Pinterest is combining UX research, design leadership, and generative AI to create visual experiences that keep human insight at the center.

Together, they will unpack how you can design AI experiences that are not only technically impressive, but genuinely usable, trustworthy, and valuable. Come ready to raise your questions, test your assumptions, and get into the detail of what actually works when building human-centered AI products.

Everyone is racing to plug AI into their workflows. But what happens when your product is built on a promise to never see user data in the first place? As CTO of a zero knowledge product, Frederic Rivain has to reconcile two strong forces: teams who want to use AI everywhere, and an architecture that is designed to reveal nothing.
In this talk, Frederic unpacks how Dashlane uses AI internally in a secure and responsible way, and how they choose and integrate AI into the product while keeping their zero knowledge model intact. He will share the trade offs, the architectural patterns, and the guardrails that let teams experiment with AI without weakening the trust the product is built on.
Challenges:

• Leveraging AI internally when sensitive customer and company data must remain encrypted or out of scope
• Choosing AI providers and architectures that align with strict privacy, security and compliance requirements, not just convenience
• Embedding AI into critical user journeys without breaking the zero knowledge promise or eroding customer trust
  Key Learnings:
• How to design AI use cases around a zero knowledge mindset, including what to avoid, what to transform, and where AI genuinely adds value
• Practical patterns for using AI internally while protecting data, from governance and access controls to what you never put in a prompt
• A decision framework for selecting and integrating AI partners in high trust products so that privacy and security stay first class, not an afterthought

How do you modernize a recommendation system while the product keeps shipping? In this talk, Katerina will share how the team at ESPN evolved the app's personalization stack from heuristics to an ML-first recommender system, where each stage delivered value on its own while building toward the next.
She'll show where AI tooling acted as a force multiplier, compressing iteration cycles across documentation, evaluation, and debugging, and helping them spot impact, so they could focus on the right levers.
You'll leave with key takeaways on incremental modernization and where AI can truly accelerate your team's velocity.

Transformation fails when new ways of working never make it into day-to-day behavior. In this Q&A deep dive, Ben Hewett and Paul Svoboda will help you explore what it really takes to make change stick, with plenty of space for you to ask direct questions and dig into the practical realities behind lasting transformation.

Ben will share the leadership perspective on growing and repositioning UX for greater influence, showing how teams can earn trust, prove value, and move from service providers to embedded product partners. Paul will bring a product-led transformation lens, showing how change can happen faster when it is built into tools, workflows, and adaptive systems instead of being talked about in endless meetings.

Together, they will unpack how you can move beyond alignment theater and create structures, behaviors, and systems that lead to better decisions, stronger collaboration, and lasting business impact. Come ready to raise the challenges you are facing in your own organization and get into the detail of what actually works.

Every board is asking how product teams are using AI, while designers, engineers and data scientists juggle fatigue, fragmented experiments and legacy stacks. Using BuzzFeed’s AI journey across news, entertainment and subscriptions as a case study, Cristina opens a cross functional discussion on turning AI pressure into shippable value across consumer products, internal tooling and subscription growth.

Outcomes:

• Share concrete ways to turn vague AI mandates into sharp, fundable problem statements
• Identify how early adopter tinkerers can de risk AI change and influence sceptical teams
• Compare approaches for deciding when AI experiments stay as prototypes and when to invest in platform change
• Design new rituals across product, design and engineering so AI work flows into production, not just slide decks

AI is collapsing the traditional boundaries between product, design, and engineering. What used to be a sequence, from idea to design to build, is becoming a continuous, AI-assisted loop.
This conversation will discuss shares how advances in AI systems are changing how ideas are formed, tested, and shipped, and what this means for team structure, roles, and decision-making. Drawing on experience across applied AI and research, we’ll explore how organisations can adapt as ideation and execution converge.