What happens to service design when AI reshapes our ways of working?

AI is often discussed in terms of efficiency, automation and outsourcing routine tasks. In service design, however, the starting point is different. The work is inherently varied, context-dependent and deeply human-centred. For that reason, AI cannot replace entire design processes, but it can help explore and refine ideas and handle specific, well-defined tasks.

At Nitor, service designer Satu Heikuksela has been actively using AI in client projects, especially to support ideation and analysis. She uses it to challenge her own thinking by exploring alternatives and questioning assumptions. Heikuksela emphasises that AI delivers the most value when the designer is not starting from scratch.

”Traditionally, you would spar with a colleague, and now you can also do it with AI. The principle is the same, but the dynamic is different,” she says.

Few routine tasks to outsource

Service design rarely begins with solutions. Instead, its core lies in building customer understanding. A service designer seeks to understand who the service is for, which problems are truly worth solving and where friction occurs along the customer journey.

This requires extensive research, hypothesis building and testing. AI can surface potential problem areas based on existing case examples, but most of a service designer’s work is still done hands-on, through interviews with product owners and end users.

”AI is largely based on the logic of programming, where repeatability, automation and pattern recognition play a central role,” Heikuksela notes.

”In service design, the primary objective is to create customer-centric services or products. There are few tasks that can be easily outsourced, but the work can be significantly accelerated in areas such as finding information and processing large volumes of data.”

Visualisation remains essential

Service designers often use whiteboard tools such as Mural to map customer journeys, visualising events horizontally or chronologically using digital sticky notes. Visualisation is therefore an essential part of the discipline, and the usefulness of AI depends heavily on the tools, which vary significantly.

Recently, Heikuksela worked on designing the end-to-end customer experience for a large financial services organisation. The client used Copilot, which currently has limited capabilities in handling visual content, as the interaction mainly takes place through text-based chat. This makes it less suitable for working with digital whiteboards, not to mention photos of real-life sticky notes, although tools are evolving rapidly.

”For working with visual content, tools like Claude perform better. Similar challenges can be seen in software development, for example with sequence diagrams,” she explains.

Faster prototyping with AI

AI is particularly useful in structuring the research phase. It helps define what should be studied, who should be involved and which questions to ask. Given sufficient context, it can generate an initial version of an interview outline, but the design team remains responsible for refining it.

Heikuksela has also seen clear benefits in rapid prototyping. Service designers collaborate closely with UI designers, and with AI-assisted vibe coding, a first version can be created quickly.

”Creating an HTML prototype is almost effortless, making experimentation accessible to everyone. It makes work easier when you can visualise ideas quickly,” she says. Previously, prototyping would have required more time-consuming work in tools such as Figma.

The third key use case is processing and analysing customer interviews. These are conducted regularly with 6–10 participants at a time, and AI is particularly well-suited to supporting this work. What once took several weeks can now be completed in a fraction of that time. Overall, Heikuksela estimates that interview analysis now requires only about one-fifth of the time previously spent.

However, AI is not yet capable of interpreting facial expressions or understanding non-verbal communication. This means responses must always be analysed alongside the interviewer’s notes and judgement.

The designer’s responsibility becomes more pronounced

AI is shifting the emphasis of the service designer’s role in a positive direction by reducing the amount of manual work. It provides broader access to data than before, enabling more independent thinking and interpretation.

As AI can quickly produce summaries and generalisations, the designer’s responsibility is to critically assess them also from an ethical perspective. Biases must be recognised.

”I see AI as an opportunity. It supports me in the areas where I am at my best: strategic thinking, understanding people and ensuring that services solve real problems,” Heikuksela concludes.

Interested in improving your service? Did you forget about the customer? At Nitor, we can help – get in touch!