Is AI Changing Service Design?

In fact, the chances are, so are you.  Recommended products on Amazon, recommended shows on Netflix, size assistants in ASOS, spell-checkers, autocompletes, predictive text or advanced analytics dashboards, these are commonplace AI-enhanced products and services. So why the buzz about ChatGPT? There’s something new and exciting about the new breed of Generative AI tools out there – but what is it?

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‍Behind the buzz, it’s a new kind of interface

Generative AI uses transformer neural networks (the ‘T’ in ChatGPT) and ‘large language models’ (LLM). Neural networks sound complex, but at a very basic level they are data tables that predict the likelihood of what will come next in a sequence of numbers (the numbers could represent anything, like words or functions). This is how voice assistants like Alexa determine functions, for example “Alexa, play heavy…” will lead to Alexa’s neural network predicting “metal” or “rock” as the next most likely word, enabling it to identify the context action word ‘play’ as a music request, not a game request, or video display request.

Transformer neural networks take this a step further by using vast databases of language – libraries full of books, magazines, newspapers, transcripts, etc. –  to understand the intent of a whole sentence, not just a word at a time. So ChatGPT can take questions like “what is heavy metal?” and use its LLM neural network to work out the intent of the whole question, which is really asking for multiple things such as a definition of the genre and some examples of classic songs. In that sense, ChatGPT style AI is a simplified interface to access, query and manipulate complex data sets and computer functions – making advanced computing possible for almost anyone. The same is true of AI image generators, which use simple text to control an array of complex imaging software that would normally need a skilled graphic designer to use. A simple interface for complex systems? That’s got huge potential for service design.

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The future of transformer AI in service design practices

Service designers capture a lot of qualitative data from user groups to understand needs, map user pathways and observe behaviours during prototyping workshops. Using natural language to query that data makes it much more accessible to the whole team. From mapping decision pathways within an online commercial offering to capturing major life event influencers that affect finance choices – our job is all about asking questions, making it easier to ask them is a no-brainer.

The idea of helping businesses prioritise and move faster has always been at the core of what we do at Spotless, and deploying these tools to create smarter back-office and operational processes – like automating the production of reference manuals, technical documentation, marketing content – is clearly one application. But why stop there? From project planning to itineraries, meeting agendas, error checking, legal compliance processes and governance, HR processes, the sky’s the limit.

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What about for end users?

The opportunity here is taking personalisation to a new level, sometimes called hyperpersonalisation. The potential for creating robust and responsive services that adapt to changes in user need is compelling.

1. Faster, frictionless customer services. In many ways, this is the most obvious and least exciting application of the technology, but nevertheless, the ability to ask a system to do pretty much anything without the need for clicks is huge. Simply type what you want into a GPT chatbot, and in theory, we no longer have to navigate to the right menu or submenu again.… simply ask a customer support chatbot a simple question and get a simple answer, without it getting confused and handing you off to a human customer service agent. Again, if a human support agent comes online, they can use the same ease of access to find a resolution for most problems without putting you on hold for ages to find the right solution – speeding up resolution times (and boosting your NPR scores too.)
2. Proactive services. What happens if a customer loses their job or gets divorced and their financial situation changes, how effectively can their existing finance products adapt to their new needs? If an existing subscription service customer wants to transfer to a new supplier, or combine accounts with their fiancée, or split services within a house share, share billing with another customer, apply for new services and so on – there are hundreds of current friction points that could be smoothed out by a system that uses chat to access complex functions. Conversely, the system itself, using natural language, can reach out proactively at the first signs of a change in user behaviour and ask if everything is okay.
3. Better user safety: The service design industry is increasingly being tasked with improving accessibility of products and services, to meet different levels of education, different ages, different cultural backgrounds and attitudes, and different levels of cognitive development or neurodiversity. Transformer AIs enable us to build systems that respond proactively to clues in language detection – meaning interfaces and support information can be much more dynamically tailored on the fly for teens, the elderly, languages, vocabulary, visual impairments, ADHD and autistic spectrum needs.