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. This panel explores 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.

Building AI products requires more than adding a model to an existing workflow. AI introduces uncertainty, probabilistic outcomes, new failure modes, and entirely new user expectations. Product, design, and engineering teams must rethink how they collaborate to define requirements, evaluate quality, test behavior, and deliver trustworthy experiences.

• Defining requirements for systems that generate, predict, and make decisions rather than follow deterministic rules.
• Designing evaluation, testing, and quality assurance processes for AI-powered experiences.
• Creating effective collaboration models between product, design, data science, and engineering teams.
• Launching AI features safely while balancing innovation, customer value, and risk.

As AI takes on more of the work traditionally performed by product managers, designers, researchers, and engineers, organizations are rethinking how teams operate. The challenge is not simply adopting AI tools. It is determining how people and AI systems collaborate to make better decisions and deliver better outcomes.
This session explores how organizations are redesigning team structures, workflows, and governance models to take advantage of AI while maintaining accountability, quality, and customer focus.

• Redesigning workflows when AI can generate requirements, designs, code, analysis, and recommendations.
• Defining clear ownership and accountability in AI-assisted product development.
• Balancing human judgment with AI-generated outputs across product, design, and engineering teams.
• Scaling AI adoption while maintaining alignment, quality, and organizational trust.

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.

"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."

"AI agents are rapidly becoming active users of products, making decisions, completing tasks, and interacting with systems on behalf of humans. As organizations move from AI assistants to autonomous agents, product teams must rethink how products are designed, governed, and optimized when the user is no longer exclusively human.

In this workshop, you'll learn how to:
Design products that can be effectively used by both humans and AI agents, including workflows, interfaces, APIs, and approval mechanisms.
Identify which customer journeys are best suited for agent-driven experiences and where human interaction remains critical.
Create trust, transparency, and intervention points that allow users to understand, monitor, and override AI decisions.
Evaluate emerging AI-agent business models and prepare your product strategy for a future where AI increasingly becomes the customer."close

The next generation of digital products won't simply respond to users. They will continuously learn, adapt, and improve. From recommendation engines and advertising platforms to personalization systems and autonomous workflows, product teams are increasingly responsible for building products that optimize themselves over time.

In this workshop, you'll learn how to:
Design feedback loops that enable products to learn from customer behavior and outcomes.
Build AI-powered optimization systems that improve recommendations, workflows, and decision-making without manual intervention.
Define guardrails that balance continuous learning with safety, compliance, and user trust.
Measure and monitor self-improving systems to ensure they deliver sustainable business and customer value.