How AI can (and can’t) help with customer journey mapping

Where AI genuinely helps

Synthesising large volumes of data

If you’ve run a research programme and you’re sitting on transcripts from a series of in-depth interviews, AI can help you identify patterns, themes, and recurring language much faster than a manual read-through. It won’t replace the human interpretation, but it accelerates the process significantly, and gives you another pair of “eyes” through which to sense make.

It is also very useful to help you combine and coalesce a variety of information sources you likely already have in house — sales data, CX data, reviews and other social media monitoring.

Where you shouldn’t skimp — getting real data from real people.

Building the assumptive map framework

AI tools can help develop a first-draft assumptive journey map by drawing on publicly available information about how customers in a given category typically behave.

However, they are generic and lacking in helpful nuance.

We recently did this as an experiment on a client project. The difference between the generic AI generated customer journey map and what the team, with their deep, in-person experiences of the business developed over a day is chalk and cheese.

However, these broad, generic AI maps are useful as a starting point for the internal assumptive mapping workshops. They can provide the broad framework for the journey — building possible key stages. This saves time in the early stages of a workshop, freeing up space to get into the weeds (which is where the interesting nuggets lie) with the team.

What’s more, doing an AI generated CJM also helps external facilitators like us familiarise ourselves with the category, so we are more knowledgeable from the get-go. It’s another source of information, rather than the final output.