Journey mapping & AI
Why AI journeys are a powerful supplement — not a replacement — for human-led journey mapping. At least not yet.
AI has transformed the way we work — from research and analysis to concept development and strategy. And now, it’s entering another core discipline: Journey mapping.
Katrine Ly
Partner, Service design
Date published:
Length:
5 min. read
Journey mapping with AI
In journey management platforms like TheyDo, you can now upload qualitative data — such as interview transcripts and survey responses — and let AI analyse it. The output in TheyDo is a journey map with AI-synthesised insights that reflect the uploaded data, e.g. how customers describe their experience.
An AI-generated journey in TheyDo provides an immediate, neutral snapshot of how customers experience a given situation — for example, the experience of joining a loyalty club. But because AI’s analysis is based solely on what you feed it with, it lacks internal knowledge, contextual understanding, and the organisation’s strategic lens.
A practical example
In several projects this year, I’ve done parallel journey maps in TheyDo — one mapped manually by teams inside the organisation, and one generated by TheyDo’s AI — to explore how the two journey perspectives differ.
One clear example comes from a recent project where we examined the journey: “I become a member of the loyalty club.” The organisation had focused heavily on the sign-up flow, having measured a high drop-off rate of nearly 70% over five months. The internal assumption was clear: The sign-up flow must be too complicated.
However, the AI-generated journey — built purely from customer interviews — revealed something else. Customers described the sign-up as easy and painless; some barely remembered it at all. What they did remember was why they joined — and the frustration that their expectations and needs weren’t fully met afterwards. The experience as a new member felt generic and impersonal.
Suddenly, we were looking at two conflicting journeys:
A manually mapped journey where the sign-up flow dominated, because that’s what the organisation was measuring.
An AI-generated journey where the sign-up flow didn’t appear at all, because customers either didn’t recall it or experienced it as frictionless.
One effective way to present the AI-mapped journey is to embed it as a nested layer within your human-mapped journey. With a single click, you can open and view the AI-mapped journey, giving you a direct perspective of the experience from the customer’s point of view.
Together, the two journeys clearly reflected and reinforced our key insights:
The high drop-off rate wasn’t necessarily caused by a poor sign-up flow, but by a mismatch in understanding: Customers didn’t fully grasp what the membership offered, and the organisation didn’t fully grasp what customers expected.
Customers joined the loyalty club with a specific goal or need in mind — but that goal or need was often left unmet once they became members. This insight appeared in both journeys, but the AI-mapped journey emphasised it even more by leaving the sign-up flow completely out of the picture.
When we redesigned the experience, we worked along two tracks:
We reframed the sign-up flow in a clearer context — helping potential members of the loyalty club understand the value before signing up, aligning it with their goals and needs.
We designed new ways to engage customers right after sign-up — with relevant content and services that addressed the needs that motivated them to join in the first place.
What AI shows us — and what it doesn’t
AI-mapped journeys offer a sharp outside-in perspective. In the example above, the journeys revealed why customers joined, what they expected, and what they experienced along the way. AI helped us see the full arc — from need to fulfilled need — based solely on the uploaded qualitative data.
But that strength is also its limitation. AI can only interpret the data it has access to. Even if customers didn’t mention the sign-up flow — because it wasn’t memorable — it may still be a critical business challenge.
To uncover that, we still need manual journey mapping — combining internal data, business insight, and contextual understanding.
Two perspectives that strengthen each other
When we combine AI’s objective outside-in view with the inside-out understanding of human and organisational insight, we create a far stronger strategic foundation.
AI gives us an unfiltered mirror of the customer journey — a direct voice of the customer, free from corporate or human bias.
Human-led journey maps bring validation, system knowledge, and awareness of dependencies, processes, and business priorities.
In the loyalty club project, this combination helped us build a strong foundation — reducing the drop-off rate while also increasing engagement, satisfaction, and conversion after sign-up. The journey expanded — moving from a strong focus on how to get customers in the door, to a broader understanding of how to keep them engaged, satisfied, and loyal once they’re inside.
Summary
AI-generated journeys provide a powerful outside-in perspective — a direct reflection of customer experience grounded in qualitative data.
But it’s only when we combine AI’s synthesis with human interpretation and contextual understanding that we see the full picture.
Together, the two journey perspectives — data and judgment, inside-out and outside-in — create a more complete foundation for designing experiences that drive both customer value and business impact.
