Approaching AI as a design leader: rethinking the customer journey with a layer of AI-first

This is part 3 of a series around approaching AI as a design leader, part 1 focuses on culture and part 2 focuses on unlocking small miracles all throughout the experience. It’s also worth it to read my article on emerging patterns in designing for AI-first experiences.

In parts one and two of this three-part series, I focused on the ways you can start driving a culture of experimentation that then translates experimentation into delivering small miracles to customers. Miracles that may seem easy to do with AI but deliver outsized impact to customers who either demand them or didn’t know they needed them.

In this third part, I am focused on how you use the wins you’ve scored already to help transform the team’s culture in order to help transform the user experience of your product or company by bringing a customer-centered point of view with an AI-first layer.

These three parts may sound sequential but they aren’t. It’s all about the level of focus you can drive in each, the size and appetite of the organization, and your ability to drive impact throughout multiple parallel paths.

It all starts with the customer journey.

It really depends on where you are in the maturity of Design in your organization but overall, you’d want to pick an area of the customer journey and focus on using service design to map it end to end. The goal is to better understand how your customer service organization, sales, or one or more areas of your product work in details. The view should include five important parts:

1. The end to end journey from a customer‘s perspective. This is obviously the most important piece and likely the one that the Design team has spent the most on.
2. The end to end journey from the perspective of the internal teams involved. In my conversations with many Design teams, this is the one likely the hardest partially because it’s the layer we often miss in drafting journeys. In an AI-first approach, this is critical. You cannot transform the experience of your customers without helping transform the way things are done internally to drive that customer experience. Customer experience is often a function of many internal teams, not just product or engineering.
3. A layer of time-consuming and low-impact tasks your customers do. This may be obvious or seem like a part of #1, but we often mix those within the journey without a deep focus on them. If you’re planning to transform your organization with an AI-first approach, you want to start focusing on the areas of the customer journey that are most likely to be transformed by the AI. These are usually the tasks customers do repeatedly but get little value from. They’re often administrative tasks like analyzing a specific piece of data to make a specific decision or going through multiple screens to get a specific piece of information. Note, this shouldn’t be just focused on your product, it should be focused on the customer’s journey. In other words, these screens may not be limited to your product screens.
4. A layer of communication between different personas within your customer’s ecosystem. Same goes within your own internal teams. AI is really good at drafting comms. Whether it’s writing emails, sending texts, or understanding context and responding to it. Soon that‘ll be as good for voice (it already kind of is but not quite). This layer is critical because customers may think of it as important, not administrative, but for the most part comms are driving actions and analyzing data. A small minority of communication happening in your customer’s ecosystem is truly focused on the human to human connection. You don’t have to replace all comms with AI, to be clear, but you do have to understand all comms to know what to replace.
5. A layer of re-thinking the journey. This one is a tough one and can likely wait in most product domains. It is, however, important. If you were to re-build this journey as a startup in an AI-first world, how would you do it?

The next steps is likely the most important. You have to decide where to pull the thread on those experiences. Simply knowing the end to end journey does not give you a starting point. In fact, it may overwhelm you and the teams involved in this work.

Knowing what areas to specifically address first is almost as important as drafting the end to end journey.

Layers 3 and 4 give you a perspective on where assisted AI workflows can be transformative for your customers. Layer 5 gives you a perspective on where you may be better off transforming the end to end experience.

The most important things to keep in mind are:

1. Although you’re thinking customer-first, your approach to delivering a better customer experience may be to start internal-first. It depends on your domain, product, and company culture but taking internal risks is often easier than taking external ones with customers. It’ll teach you a lot too.
2. This journey will evolve as AI capabilities and your thinking evolves. This is a general rule of thumb but if you’re printing a customer journey and hanging it up or you’re calling it the customer journey then you’re likely behind by the time the work is ready to be shared. Any customer journey is a living view of the world that needs to have room to be updated with new information.
3. You don’t have to approach every piece of the customer journey with the same level of transformation. Some areas can benefit from simple work, others may need to be transformed. You don’t even need to approach it with the same tools. Some internal teams may be able to run 10x better with external tools designed for them (think support or sales) while your own product may require internal investments in technology on top of the frontier LLMs.
4. Any design worker is harder to translate into impact if done in a silo. This isn’t work that can be done without the collaboration of these internal teams, their leaders, and the product organization, Your job isn’t to surprise anybody with amazing work, it’s to deliver amazing work with everyone else.
5. Any AI-first experiences need to deliver better results with a multiplayer effect than the existing experiences. Simply adding a layer of AI doesn’t justify the investment unless that layer helps make customers and internal teams either be better or feel better about their work and the experiences they’re working with.

This way of transforming isn’t simply a recipe for all so the flavors you add that help make this yours matter almost as much. Good luck!