Co-working space | Customer Journey Map (AI-Augmented)

Brief Description

I chose to not select a specific company to create the CJM around, but rather the general idea set forth in the brief. I used Claude's Sonnet 4.5 model to generate a majority of the information used in the Customer Journey Map.

*All assets used in the brief, including, but not limited to, the persona, headshot, and journey steps, are fictional and not the direct reflection of an actual individual.

Persona

To keep in line with the brief's objective, I jotted down my interpretation of what a user of such service would look like, and prompted Claude with the following (after providing task context):

"I need a Gen Z female user persona, working in a high-pace environment remotely, that, in the span of a few concise sentences, explains the context under which she is looking for a co-working space, including motivations, goals, and potential areas of concern (such as safety)."

In response to which, after further refinement, he provided me with the persona and context used in the CJM. To generate the image, I used Leonardo's Lucid Original model.

Journey Steps

I needed to generate a set of journey steps that led to a definitive conclusion of the journey being mapped out, for the purpose of which I used the following prompt:

"The objective of the customer journey map is to identify pain points and opportunities for improvement, to enhance the overall experience, including the online experience, for individuals utilizing co-working facilities.

Generate broad key step of the customer journey that lead to a booking."

Layer Selection

There was no complex rationale used to generate the CJM layers. I considered the most basic layers that would contribute to a comprehensive (yet brief) CJM, and put them on the map.

Journey Information

The information in the journey under each step for each layer was generated by Claude as well. I used the following prompt, which I repeated for every layer of the journey:

"Generate brief few-word points for Discovery, for the following layers:"

Claude already had the context of the task at hand, the persona, and the desired information, so it was pretty straightforward from there.

Conclusion

While CJMs are deeply rooted in rationale and reasoning, I used AI to assist me in crafting this one for the brief, as it's supposed to serve as a general journey map, without going into any specifics (ergo, the lack of KPIs and metrics).

Of course, with more specific data, and for more specific scenarios, it'd be easier to craft a CJM that is a true reflection of the user and their journey, while potentiating the highest potential for valuable insights.