IAK17 / Innovation and Design

This years IAK17 conference focused on UX for Smart Services. For two full days a series of talks discussed current and future challenges for human-centred product development in an industry that tends towards application of artificial intelligence wherever possible.

Talks touched on innovation, conversational interfaces, creativity and artificial intelligence, empathic services, privacy and industry use cases. Some of these topics will be discussed in a couple of blog posts. This post comments on innovation and design.

Innovation and Design

In his keynote Klaas Bollhöfer (The unbelievable Machine Company) discussed some challenges for innovation — and how design can contribute and help facilitate those.

Setting up innovation teams to explore new technology and services, is the first step. A place for exploration of innovative ideas and experimentation is crucial for innovation. However, according to Klaas the actual challenge is about transforming such effort into longer term product strategies.

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While Design often is understood as a process to solve problems — a key role might also be in providing a vision that is bold enough to encourage a product team to take on the hard work to further define and develop all detail of a concept. UX and Service Design bring a rich toolset of artefacts that might help teams to discuss and align on a shared vision for a service within an existing ecosystem.

New mental models

Business and service design often focus on the need for new business models. Concerning technological innovation however, we might need to establish mental models first.

Artificial Intelligence in many ways works slightly different than human intelligence. Things that seem easy for humans, are incredibly difficult for machines, and vice versa. Some examples, while searching and grouping thousands of documents based on similar words is easily done, assigning a meaningful label to such sets is terribly difficult for the machine currently.

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Knowledge workers rely on technology to find the right information. They might be used to advanced keyword searches and boolean operators. Expecting AI to provide a “better search” misses the point though. Intelligent systems support a different search — maybe one that doesn’t behave like a search. We need to establish a new understanding of such behaviour, a new mental model.

According to Jan Korsanke (SinnerSchrader) its Design and Accessibility that will help AI to break through. The process of sketching, testing and iterating concepts, can help facilitate discussions with customers and users. These enable to learn about whatever a user projects into a tool — Adapting a design is easy, while changing culture is extremely hard.

AI is not a panacea

Often clients expect “Data Science”, “AI” or “Machine Learning” to be a general solution to all kinds of problems. However, as with many things, these terms only describe a collection of many different methods.

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Clients often only understand the problem from their perspective — likewise ideas for solutions build on incomplete understanding of what data science actually can do. It is a challenge to match both — the problem and available technology.

Good research is required to understand existing workflows, pain points and opportunities. Beat Walther (Vendbridge) presented their work building on Jobs-to-be-done, in order to better research and evaluate added value for the customer.

Collaboration with AI

Often AI is thought of as a technology that might eventually replace human cognition. However, given the nuanced detail and creativity many jobs require, the idea to enhance human cognition might be more appropriate (for now).

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As stated by Jan Korsanke, it is not “us versus them”. An example, the development of chess computers led to the often cited invention of ‘freestyle chess’, in which humans play alongside chess computer. Such human-machine teams have proven to be more powerful than a single chess computer or a single human player.

Keeping that in mind when creating new technology might lead to a richer picture that also includes human actors in a socio-technical system.

What are your thoughts on human-centred innovation?

Written by

UX Research & Design at #trlabs London, w/ brilliant data science & vis team. curious about UX, IA & design thinking. Posts solely reflect my personal opinion.

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