Shipping Hype: PMs on What it Takes to Bring AI Features to Market | Figma Blog

AI’s rapid rise has pressured companies to launch features quickly, but hype alone does not produce useful products. Product leaders at Figma, Asana, Duolingo, and LinkedIn argue that successful AI development starts with real user problems, clear definitions, and realistic expectations about current models. AI should be treated as a tool for improving valuable workflows—not as a solution looking for a problem.

Start with User Problems

• Teams should identify user needs before deciding whether AI belongs in the feature.
• Figma PM Conor Woods recommends asking:
  • Can the problem benefit from a large existing data set?
  • Is some margin of error acceptable?
  • Is AI genuinely improving the experience, or merely hiding poor UX?
• LLMs are well suited to tasks such as organizing information and generating summaries, but they are unreliable when perfect accuracy is required or when they must invent entirely new experiences.
• AI-generated inaccuracies and hallucinations are unavoidable with current models, making AI inappropriate for high-stakes, precision-critical tasks.
• Asana uses a simple test: does the feature save users meaningful time?
• Its Smart Status feature drafts project updates, reducing a task from roughly 20 minutes per week to two minutes and making the return on investment immediately clear.

Specify the Problem Precisely

• Generative AI can serve many different underlying needs, which makes vague feature descriptions dangerous.
• Saying “we’ll summarize text” leaves open important questions about the user’s actual goal.
• A user might want a summary to:
  • Understand a document’s subject
  • Identify action items
  • Extract decisions or other specific information
• Product teams need to define the desired outcome and detailed use case rather than relying on broad descriptions of AI capabilities.
• Greater specificity helps designers, engineers, and stakeholders develop a shared understanding of what the feature should do.

AI features are most effective when they address a concrete, measurable user problem and acknowledge the limits of current models. Teams should define the user outcome first, then determine whether AI is the appropriate and trustworthy way to achieve it.