Agenda

An overview of UXDX, the purpose of the conference and what to expect over the coming days.

AI is changing more than products. It is changing how products are conceived, designed, built, tested, shipped, and improved. Teams now have access to capabilities that can generate code, analyze customer feedback, create content, conduct research, and make decisions at a scale that was previously impossible.
This session explores how leading organizations are adapting their product development lifecycle for an AI-driven world, and what product, design, and engineering leaders need to rethink as AI becomes embedded across the entire product stack.

• Identifying where AI is creating the biggest shifts across discovery, design, engineering, and delivery.
• Evaluating which product development activities should be accelerated, augmented, or automated by AI.
• Recognizing how AI is changing competitive advantage and the speed of product innovation.
• Preparing teams, processes, and operating models for the next generation of AI-powered product development.

As AI becomes embedded in product experiences, teams need to think beyond prompts, features, and automation. The real challenge is helping AI-driven systems understand the messy reality of human behaviour: intent, emotion, ambiguity, context, and trust. In this session, Richard Dalton explores how design can help teams build AI experiences that are not just technically impressive, but useful, understandable, and aligned with real human needs.

Attendees will learn how to:

• Spot where AI experiences can lose human context, and how design can help teams catch those gaps earlier.
• Translate user needs, behaviours, and intent into clearer inputs for AI-powered products and services.
• Build more effective collaboration between design, product, and technology teams when shaping AI experiences.

As AI systems become more capable, products are increasingly able to act on behalf of users, recommending, planning, personalizing, purchasing, and completing tasks with minimal intervention. While these experiences can dramatically reduce effort, they also introduce new challenges around trust, transparency, consent, and control.

The most successful AI-powered products are not those that automate the most. They are the ones that help users feel confident, informed, and in control as automation increases. Moderated by Alex Burke, CEO of Optimal, this session will explore how leading consumer product organizations are designing agentic experiences that deliver convenience without sacrificing user agency.

• Determining which actions should be automated and which require explicit user approval based on risk, context, and customer expectations.
• Designing transparency, consent, and intervention mechanisms that help users understand, monitor, and influence AI-driven decisions.
• Building trust in products that interpret intent, handle personal data, and take action on behalf of users in real-world scenarios.
• Creating agentic experiences that reduce friction while maintaining user confidence, accountability, and control.

As AI agents increasingly create campaigns, generate creative assets, allocate budgets, optimize performance, and analyze results, advertising platforms are undergoing a fundamental shift. The primary user is no longer just the marketer. Increasingly, it is the AI acting on their behalf. Drawing on her experience leading Ads Platform at Snap, Shobha Diwakar explores how product teams can design platforms that effectively serve both human and AI users while maintaining trust, transparency, and control. In this session, Shobha will explore:

• Designing advertising platforms for AI-driven decision making, including how campaign creation, targeting, bidding, optimization, and measurement evolve when AI becomes a primary user of the system.
• Defining the boundaries between human judgment and AI autonomy, balancing automation with the oversight, control, and accountability marketers expect.
• Building trust in AI-powered products, using transparency, explainability, and feedback mechanisms that increase adoption while maintaining confidence in automated outcomes.
  Through practical examples and lessons from building advertising technology at scale, this session will provide product, design, and engineering leaders with a framework for creating products where AI is increasingly both the user and the decision-maker.

• What had to change inside Sephora when customer journeys became fluid, with customers entering through search, social, AI assistants and other unpredictable entry points.
• How Sephora's Intent Steward model helps teams design systems that understand customer intent, apply context and determine the next best action in real time.
• The operating model, guardrails and cross-functional collaboration required to build and govern AI-powered decisioning systems.
• A detailed case study showing how Sephora applied these principles to a real customer or partner experience, and how the team's role shifted from shipping features to orchestrating outcomes.

AI tools are reshaping how product, design and engineering work together, where responsibilities sit, and what it means to contribute to product development.
In this session, Ashley Nutter shares how CNN is thinking about AI adoption across product teams, from encouraging experimentation to avoiding tool proliferation, spreading useful practices, and deciding when workflows should be standardised. This talk looks at the operating model questions leaders now need to answer: how teams collaborate, how roles evolve, and how organizations keep momentum without creating chaos.
Ashley will share practical lessons from a large media organization navigating AI transformation, with a focus on the people, process and leadership decisions that make adoption sustainable.

Most organizations have launched AI experiments. Far fewer have successfully scaled them into products, workflows, and operating models that deliver measurable customer and business value. Moving from isolated pilots to organization-wide adoption requires new approaches to governance, measurement, risk management, and prioritization.
This session explores what separates successful AI transformations from costly experimentation.

• Prioritizing AI initiatives based on customer value, business outcomes, and implementation effort.
• Establishing governance frameworks that balance innovation, compliance, and risk.
• Measuring the impact of AI initiatives beyond usage metrics and experimentation.
• Scaling successful AI capabilities across teams, products, and customer experiences.

Real examples of AI deployed across the stack including multimodal models, internal developer copilots and agentic analytics

AI tooling enables very small teams to build and ship sophisticated products while maintaining security and reliability

As AI generates interfaces and code, teams must rethink design systems, APIs and engineering workflows

As we wrap up the event, we will summarise the key takeaways and insights from the sessions, ensuring you leave with a clear understanding of the actionable strategies discussed.

AI can now generate discussion guides, summarize interviews, identify patterns, and produce research reports in minutes. But not every part of the research process should be automated. The most successful teams understand where AI adds value, where human judgment remains essential, and how to combine both effectively.

This workshop explores how AI is reshaping user research and provides practical frameworks for deciding what to automate, what to augment, and what should remain human-led.

In this workshop, you'll learn how to:
Evaluate which research activities can be effectively automated and where human researchers continue to provide unique value.
Use AI to accelerate analysis, synthesis, and insight generation without compromising research quality or rigor.
Design research workflows that combine AI efficiency with human judgment to improve speed and decision-making.
Avoid common risks and biases in AI-assisted research while maintaining confidence in customer insights and recommendations.
Ideal for: UX Researchers, Designers, Research & Design Leads, Research Operations, and UX Managers within enterprise organisations who are already using AI in parts of their workflow and want practical guidance on applying it more effectively.

"AI is transforming how teams discover customer needs, identify opportunities, and validate ideas. The challenge is no longer accessing customer feedback. It's knowing how to combine AI-powered analysis with customer conversations, research, and human judgment to make better product decisions faster.

In this hands-on workshop, you'll explore how leading product teams are integrating AI into their discovery process while avoiding the common pitfalls of over-relying on automated insights.

In this workshop, you'll learn how to:
Identify customer problems and emerging opportunities faster by combining AI-powered analysis with continuous customer discovery.
Accelerate opportunity validation using AI-assisted research, synthesis, and feedback analysis techniques.
Prioritize customer needs with greater confidence by balancing AI-generated insights with qualitative research and business context.
Build scalable discovery workflows that help product teams stay connected to customers without slowing down delivery."

Teams are rapidly introducing AI features but there is still no established way to measure whether those experiences are accurate, understandable and worthy of users’ trust. Traditional UX metrics alone may not capture what happens when outputs are generated by AI and can change from one interaction to the next. In this hands-on workshop you’ll learn how to build a practical measurement framework for AI-powered experiences. Using DocuSign’s evolving UX Scorecard approach you’ll explore how to define and apply metrics across trust, transparency and the accuracy of AI outputs. You’ll work with others to evaluate a realistic AI use case before applying the approach to your own product or experience. You’ll leave with a practical starting point that your team can adapt as its AI capabilities and measurement maturity develop. By the end of the workshop you’ll be able to:

• Identify where traditional UX metrics fall short when evaluating AI-powered experiences
• Define meaningful measures of trust, transparency and output accuracy
• Build a practical scorecard for evaluating an AI feature or user journey
• Adapt the approach to your own product, users and organizational context
• Use measurement findings to guide research, product decisions and improvements to the experience

AI can generate polished interfaces in minutes. But an interface can look right while quietly departing from your design system, using the wrong components, ignoring tokens, inventing new patterns or introducing accessibility issues. As AI-generated work spreads across teams, these small inconsistencies can quickly create significant rework and risk.
In this hands-on workshop you’ll learn how to evaluate whether AI-generated products are actually using your design system. Using a public design system and a set of realistic product tasks, you’ll generate interfaces, examine the decisions made by the AI and measure how consistently it follows the system.
You’ll then use those findings to identify gaps in documentation, uncover missing components and distinguish acceptable exceptions from problems that need to be addressed. Rather than relying on whether an interface looks right, you’ll leave with a practical way to measure quality and continuously improve how AI works with your design system. By the end of the workshop, you’ll be able to:

• Create realistic tests for evaluating AI-generated interfaces against a design system
• Measure whether AI is correctly using your components, tokens and established patterns
• Identify hidden inconsistencies that could create rework, accessibility issues or compliance risks
• Use the results to improve design-system documentation and uncover missing components
• Establish quality thresholds and a repeatable feedback loop for AI-generated product work