Instead of randomly applying AI, a better approach is to journey map the internal process of how product, design, and development teams collaborate. This analysis reveals the biggest bottlenecks and points of friction, which then become the most valuable and targeted places to apply AI for genuine process improvement.
AI can accelerate research, but outsourcing the entire process of understanding is risky. Human teams must retain deep customer knowledge, as this is the foundation for customer-centric decisions. This principle prevents organizations from becoming dangerously detached from their users in an effort to be more efficient.
Responsible design requires considering societal impact. A "bad headlines" workshop is a practical tool where teams brainstorm the worst possible news headline if their AI feature fails or is misused. This creative exercise effectively surfaces potential harms and helps teams decide whether to proceed, pivot, or pull back on a project.
Instead of focusing on cost-cutting metrics like "hours saved," leaders should measure AI's success by the capacity it frees up. For instance, faster research analysis enables more studies per year, leading to more customer-informed decisions. This reframes efficiency as a strategic advantage that drives growth, not just reduces costs.
Forrester data shows consumer trust in AI interactions like chatbots is already below 20%. When organizations rush AI implementation, they create poor, inaccessible, and unusable experiences that don't meet customer needs. This only serves to further erode consumer trust, which is the biggest risk brands face with premature AI adoption.
To use AI effectively in design, organizations need a mature, machine-readable design system. AI tools rely on this system's well-documented components and rules to assemble experiences consistently. Without it, AI-generated designs risk being off-brand and low-quality. Investing in the design system is a key prerequisite for mitigating AI risks at scale.