Turn AI Experiments into Real Design Workflows
This customizable workshop helps product and UX design teams integrate AI tools into practical, shared workflows. Talk with our team to learn more and get a custom proposal.
Workshop Summary
AI tools are rapidly entering design workflows—but in most organizations, adoption is still uneven. Designers experiment individually, using different tools and approaches, without a shared understanding of where AI actually improves the work.
This hands-on training helps product and UX design teams move beyond scattered AI experiments and develop practical ways to integrate AI into real design workflows.Through guided exercises, real examples, and collaborative discussion, your team will explore how AI can support research, ideation, prototyping, and iteration—while maintaining strong design judgment and user-centered thinking.
The training typically runs 6–12 hours and can be delivered as a full-day workshop, a series of shorter sessions, or a multi-session program depending on your team’s schedule.
By the end of the workshop, your team will have a clearer framework for when and how to use AI, along with practical workflows they can immediately apply to their day-to-day work.
Build Practical AI Workflows Across Your Design Team
WHAT YOUR TEAM
WILL LEARN
LEARN MORE AND Get a Custom Proposal for your team
Customized for Your Design Team's Context
Every organization approaches AI differently. Some teams are actively experimenting with new tools, while others operate within stricter governance and approved technology stacks.
Because Designlab works with designers and design teams across a wide range of companies, we understand the realities of introducing AI into professional design environments—from evolving tool policies to varying levels of team familiarity.
Before the training, we collaborate with your team to understand your workflows, goals, and tool environment. This allows us to focus the workshop on practical applications that fit within the tools and constraints your organization already works with.
Our goal is not to introduce yet another set of disconnected experiments, but to help your team build practical AI workflows today while preparing for how these tools and practices will continue to evolve.
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