Redesigning journey map maintenance for a customer experience platform, with an agentic AI layer that keeps content fresh without taking control away from the people who own it.
Client and proprietary details have been withheld under NDA. This case study describes process, decisions, and outcomes at a high level only.
Journey maps are living documents that need to evolve as customer experiences change, products update, and new data emerges. But on this platform, there was no system to alert users when their content became outdated, personas aged out of date, insights no longer matched current metrics, touchpoints drifted from reality.
Users relied on manual review and indirect signals to catch stale content, which led to inefficiency, disconnected data, and eroding trust in the maps themselves.
How might we help journey map owners identify and address outdated content, so their maps stay accurate, trustworthy, and actionable over time?
The client is a customer journey management platform that helps organizations understand their customers by building, analyzing, and maintaining journey maps. The platform already supported creation, analytics, and AI-assisted content generation, but maintaining those journeys over time was a gap.
Conducted desk research, competitive analysis, and a heuristic evaluation against Nielsen's 10 usability heuristics on a competing platform, grounding our direction in real gaps rather than assumptions.
Led 3 of 6 semi-structured interviews with real platform users, surfacing unmet needs, most notably that users had no way to know when their content had gone out of date.
Translated research into a high-fidelity interactive prototype across multiple rounds of iteration, from early concept sketches to a working agentic AI experience.
Helped synthesize findings across 6 usability tests, then presented design decisions and rationale directly to the client's CEO and VP of Product.
The final design introduces two complementary AI capabilities. Together they reduce manual maintenance, improve visibility into outdated content, and keep users in control at every step.
Users told us they wanted to ask questions about their journey and get direct answers without digging through data. This agent is fully reactive and user-initiated, letting people engage with AI on their own terms through natural conversation linked to specific map elements.
Designed both the proactive monitoring agent and the reactive chat interface in Figma, then wrote the prompt structures and evidence-based suggestion logic that made the AI's reasoning visible instead of opaque. The goal throughout was an agent that assists without ever quietly taking control away from the user.
"Leading the sponsor meetings taught me more about translating design into business value than any studio project could. When you're speaking to a CEO, 'it looks better' isn't a reason, 'here's the trust we're rebuilding' is."