Where (and Why) We Really Need AI in the UX Workflow

"Because of my passion for AI tools, I often find myself in conversations with designers, leaders, and ones who are just starting their journey in UX and product design. Almost everyone asks the same question: "How should we integrate AI into the design process?" It's a fair question, but maybe not the right first one. Instead of asking how, perhaps we should start with where and why. Where do we actually need AI? And why are we introducing it in the first place?"

"The question of where AI can add real value isn't one-size-fits-all: it depends on each team's specific challenges and culture. Across the various UX product teams I've worked with, I've seen how dramatically their pain points can differ, even within the same organization. In general, two factors determine where AI is most useful: Which parts of the workflow are time-consuming or ripe for automation. Where you can trust AI's output, and have the expertise and time to review it when needed."

"When I gathered data from different designers about how much time each design stage takes, the diversity of answers was striking: For some, handoff was the biggest bottleneck because of limited communication with engineers. For others, discovery took the most time because every step required stakeholder alignment. Some teams needed help with design craft due to weak or inconsistent design systems. So before jumping into AI adoption, we must clearly define what exactly we're trying to improve ... and why."

Start by identifying where and why AI is needed before deciding how to integrate it into design workflows. AI provides the most value where tasks are time-consuming or suitable for automation and where teams can trust outputs and review them. Pain points vary: handoff delays, lengthy discovery with stakeholders, inconsistent design systems, and frequent undocumented requirement changes. Teams should map specific workflow bottlenecks and trust boundaries to target AI efforts. Clear definitions of desired improvements and the reasons for them enable focused AI adoption that saves time and complements team expertise.

Read at Medium