A Practical Path to AI Value in UX: Accessible Areas, Improved Experiences, and Productivity from Tools
Lately, I’m hearing a lot about AI tools in productivity, development, and design—and far less about using AI intelligently to create new value for customers and employees. Don't get me wrong, the tools are amazing, and can deliver unheard of productivity gains, but in my consulting work, many leaders are still asking the same question:
“Where can/could/should we apply AI?”
They know their business, they struggle to imagine the specific “what” across journeys and experiences that would actually move the needle. Historically, my answer was simple: start with the user and the problem space. That's still often the answer. But today, many teams can’t yet picture what AI could do, and some problems are already defined, just poorly served. So we look for opportunities for AI, trace the actual workflows, and identify where AI can upgrade the experience, all while keeping the users in focus.
Here’s the simple, pragmatic path I recommend.
1) Assess: Find accessible opportunity areas
Look for processes and flows that are ready for AI right now. Can you find areas of:
High-volume, repeatable work (document-heavy steps, long queues, monitoring, matching/merging, checklistable compliance).
Data-rich, time-critical decisions (signals and trends, forecasting, anomaly detection, knowledge trapped in tickets/wikis/email).
Strong data foundations (historical + streaming/event data, un/semistructured text, reasonable data quality and access).
2) Blueprint: Map where AI helps the journey
Use an AI-Blueprint to walk the employee and customer journeys, pinpoint friction, and identify AI leverage points. The outcome is a prioritized backlog with ROI hypotheses, data/guardrail needs, and a pilot plan.
Fast-win examples your blueprint could surface areas around:
Employee Experiences: Smart document generation, automated data entry/validation, automated summaries, anomaly alerts.
Customer Engagement: Personalized recommendations, proactive care, dynamic offers, emotion-aware service, voice AI for call centers.
Business Processes: Trend forecasting, intelligent quality control, contract analysis, smart inventory, fraud/risk monitoring, predictive management.
3) Build (Faster): Use AI to speed research, design, and development
Yes—then bring in tools. Use AI to accelerate:
Research & synthesis: rapid insight extraction and pattern-finding
Design & prototyping: variant exploration, fast idea-to-prototype
Engineering: code generation, proof-of-concept
This shortens time-to-value while you prove outcomes via user testing, A/B testing, or working pilots.
The Bottom Line
Start with opportunities where AI could be practical today. Use an AI-Blueprint to identify high-impact moments across journeys. Then apply tools to speed research, design, and development so you realize value—quickly. As with many new technologies, UX needs to lead the way in "how" a new technology can be used, in a human-centered way that solves real customer and employee challenges.