Design in an age of automation — the human machine interlace

The terms “Ai” and “automation” are cropping up more and more — not only in digital circles but in wider society too, which is making a lot of people anxious as they question their role in this age of automation.

There are many thousands of articles on the subject that talk about the end of work, the dissolution of millions of jobs — including concepts like dark factories. And even Silicon Valley leaders have spoken about the impact of automation on the workforce, with Elon Musk recently saying that “what’s going to happen is robots will be able to do everything better than us…I mean all of us”.

And, of course, dystopian robotic futures are everywhere in popular culture, from Terminator to Westworld.

Fear of the machine isn’t a new phenomenon — the Luddites were a famous example, and even back in the 16th Century, Elizabeth I refused a patent for a knitting machine fearing it would deprive her subjects of employment.

As part of our Leaders in Change series, we invited Kevin McCullagh, founder of Plan, to talk through the reality of the age of automation. Rather than fretting over how easily and soon humans will be replaced, he gave the case for thinking about the future of automation in a positive way. While work will need to be redesigned, interlacing machine strengths with those of humans have the potential to unlock economic growth and fuel innovation.

Increased prosperity

Kevin explained that automation tends to raise employment and prosperity. One example of this is the role of the paralegal. In the 1990s, electronic discovery software went mainstream. However, instead of seeing an end to the role, there has actually been an increase — faster than the labour force as a whole, as the role shifted to focus less on reading reports, and more on summarizing and sensemaking, and their roles became more meaningful.

Something similar happened when ATMs automated the tasks of bank tellers and when barcode scanners automated the work of cashiers: Rather than contributing to unemployment, the number of workers in these occupations grew over time, and those roles became more varied.

In fact, in a study of job titles on the 1950 US Census, it was determined that just one out of 270 listed was eliminated due to automation — lift operator.

And it’s important to remember that the job market is not, and has never been consistent, and ebbs and flows due to all kinds of factors — with automation being just one of these.

Human-machine collaboration

Humans and machines have different strengths. Broadly, humans are suited to lower data situations, where other skills such as judgement, empathy, leadership, creativity and improvisation are required, while machines are good at data-driven and repetitive tasks.

Redesigning work

While human and machine strengths can seem almost oppositional, there’s a big middle ground that us humans need to become comfortable with:

• Training — teaching machines how to perform tasks or act more human. This might include improving machine language learning or developing a brand or personality.
• Explaining — untangling machine decisions and translating this to stakeholders.
• Sustaining — ensuring the proper use of Ai, overcoming setbacks, managing stakeholders and maintaining momentum.

There are also some important ways that Ai can enhance the effectiveness of human activity and decision making. For example:

• Amplifying — smart glasses can help engineers assess a complex environment more accurately.
• Interacting — AI agents with voice interfaces can help facilitate interactions at scale.
• Embodying — Ai combined with sensors to safely and effectively physically augment human workers.

Redesigning design

Ai can (and is already) playing a role in design. Kevin gave some great examples:

Yossarian — a discovery tool that uses Ai to identify new data patterns and connections.

Crimson Hexagon — an Ai-powered platform to uncover insights from existing consumer or user insight data.

Project Dreamcatcher — Ai creates designs based on specified constraints.

Aimybox — an Ai platform to accelerate and democratise prototyping.

Adobe Sensei — helps accelerate iteration and unlocks new creative possibilities.

Descript — automates transcription.

Nike — using computational design to reimagine high-performance running shoe design

Stitch Fix — uses a combination of Ai and human stylists to customise options for their customers.

Marcel — Publicis Groupe hit headlines for pulling its marketing and awards budget for a year to invest in Marcel, an Ai-powered communications platform.

We’d love to hear your thoughts on these products — and any others you’d add to this list.

Automation has the power to drive economic growth, fuel innovation and help make work more human, but it’s important that we focus on working with machines, not against them. We need to champion human strengths, and invest in supporting and upskilling the workforce to be ready for the redesign of work.

If you’d like to talk more about how to redesign work, or find out more about future events we’re running, please get in touch at team@wearefutureheads.co.uk, we’d love to hear from you.

About Kevin McCullagh

Kevin is the founder of Plan, the product strategy consultancy. Plan helps mobility and consumer tech companies explore the early stages of the product and service development. Their clients include Ford, Toyota, Yamaha, Deutsche Telecom, Carl Zeiss, Microsoft, and Samsung.

Kevin writes, speaks, and chairs conferences on design, innovation and society; and has been published in: The Wall Street Journal, The Telegraph, FastCompany, Unherd, Icon, Blueprint and The Design Management Review.

You can connect with Kevin on LinkedIn and Twitter. Kevin also sat down with us to tell us more about his career story as part of our FFS series.