Frequently Asked Questions

1. How can AI actually help my product or design team?
AI can speed up prototyping, automate repetitive tasks, and surface new insights from your data. More importantly, it helps teams test ideas faster, work more efficiently across functions, and focus their energy on solving customer problems instead of admin work.

2. We’re not AI experts — do we need technical knowledge to get started?
No. You don’t need to know how to code or build models to benefit from AI. We provide step-by-step guidance, playbooks, and training tailored to designers, product managers, engineers, and researchers — so AI feels like an everyday tool, not a technical hurdle.

3. How do we know AI won’t just add more work to our already full plates?
Our approach is about saving time, not adding extra tasks. By showing you how to use AI inside the tools you already rely on (like Figma, Miro, Slack, or Copilot), your team learns how to get results without changing everything about the way you work.

4. What’s the best way to start using AI in our product development process?
Start small and practical. We recommend beginning with use cases like generating early prototypes, writing user stories, or automating meeting notes. From there, we expand into more strategic applications — always aligned with your goals and KPIs.

5. How can we build trust in AI with our leadership and stakeholders?
We help you create tangible prototypes and roadmap ideas that make it easier to show value and gain buy-in. We also work with you to set ethical guardrails so leaders and customers feel confident that AI is being used responsibly.

6. What is an AI Center of Excellence, and why would we need one?
An AI Center of Excellence is a shared company resource where people across disciplines — design, product, engineering, business — can exchange tips, training, and success stories. It prevents silos, reduces duplicated effort, and helps your organization stay ahead without overwhelming any one team.

7. How do you measure success with AI adoption?
Success looks different for every team, but we often track improvements in speed to prototype, reduction of repetitive work, faster decision-making, and increased adoption of features. Ultimately, it’s about aligning AI use with your KPIs — not chasing hype.