Deep Design: Designing with Machine Intelligence

Pioneering tech like machine intelligence is fuelling opportunities for designers to rethink their design solutions, but what’s the impact on users?

By Jordan Dalladay-Simpson, BCG Digital Ventures

By applying new technological capabilities in creative and human-centric ways, designers can solve old problems more effectively as well as find solutions to problems that have yet to be solved. The pioneering tech we see in the field of machine intelligence (AI/ML) today is no exception, and it’s providing a wealth of new possibilities to designers. The challenge now is for designers to work with these technologies to build solutions that harness creativity to solve problems or provide improved solutions — while always putting the user at the center.

The journey so far

Our story starts in 1950, when Alan Turning posited the idea of a learning machine. This was a description of a kind of computer that could learn and become artificially intelligent — along with a famous test to assess any machine’s ‘intelligence.’

A hive of activity followed, with researchers and academics around the world building various learning systems, pushing forwards what was possible with the technology available at the time.

In the mid-sixties, Joseph Weuzenbaum at MIT started working on a new system called ELIZA. He created a virtual therapist program for the system that would apply simple rules to transform users’ answers and repeat them back as questions. Though relatively simplistic and procedural by today’s standards, this program elicited an emotional response and feeling of human-like connection from its users, to the ongoing surprise of its creator.

ELIZA would become one of the first chatbots ever created, and one of the first programs that could attempt to pass the Turing test. It also demonstrated that the more ‘natural’ the interactions we have with technology are, the more human we both perceive and expect them to be.

Source: Wikipedia

Machine intelligence research and application continued over the next few decades, with the technology growing gradually more mature. Then, in 1997, IBM’s Deep Blue made the news by beating the world chess champion Garry Kasparov at a game of chess for the first time, and almost 20 years later, Google’s AlphaGo became the first program to beat the professional player Lee Sedol at Go.

Today, we see tools like OpenAI’s GPT-3 that can ‘write’ entire articles based on sophisticated deep learning and language models, available over an API. Capabilities only dreamt about a few decades ago are not just possible, but freely available for general use.

With every product or service we design, there are now opportunities to harness new machine intelligence capabilities. We can use these capabilities to augment existing experiences, as well as create new opportunities for brand new experiences. Applications range from simply applying a set of rules automatically to a user input to running deep learning algorithms to find patterns in giant datasets. We should be thinking as designers how these capabilities can enhance the solutions we are designing today.

Dimensions of impact

When we are thinking about harnessing the capabilities of machine learning in a product or service we are envisioning or refining, we can use the following questions to help us explore the creative applications of these technologies across several key dimensions. Machine intelligence can impact our solution space in one of four ways:

How could we augment processes to increase productivity and/or reduce costs?

How could we improve the quality of outputs with augmentation to boost effectiveness?

How could experiences be augmented to drive deeper user engagement?

What previously unsolvable problems or latent needs may now be solvable?

Three emerging opportunities

With the capabilities of machine intelligence today, new and interesting opportunities are emerging for us to uncover and explore through design. Here are three opportunities that designers can think about now to improve existing products or work towards building new ones.

Real-time Sentiment

Knowing how users engage with our offerings is critical to their success. It takes a fraction of a second for users to form an impression of a product or service, and we see a majority of users wouldn’t recommend a service based on a badly designed experience.

Today, we have methods to manually qualify the experience through testing, and tools to measure engagement with digital touchpoints, but machine intelligence could open up a third way, qualifying an experience in completely new ways at greater scale. How might we harness machine intelligence capabilities like sentiment analysis and computer vision to understand the sentiment of our users better, and see the emotional impact of our products on them?

If we can close this loop, then we can design our products and services to be emotionally responsive, as well as start to meet needs that were previously unaddressable.

Process Augmentation

Today, 25% of Fortune 500 companies are estimated to be implementing intelligent process automation, with a significant increase in productivity predicted from this investment. However, more than a third of workers are worried about job loss from automation.

Rather than just looking purely at the automation and optimization of tasks, how might we leverage human strengths in a system, and rethink jobs to be more human, creative, and empathetic? By understanding the strengths of both people and computers, we can design hybrid processes that reimagine roles, getting people to do the things they are good at and enjoy like communication, creative exploration, and decision making, while getting the AI to do the heavy lifting on repetitive tasks. How can the machine help us have greater impact and more fulfilling jobs, in companies that seamlessly combine humans and technologies?

In the words of Kai-Fu Lee, “Humans need and want more time to interact with each other, I think AI coming about and replacing routine jobs is pushing us to do what we should be doing anyway; the creation of more humanistic service jobs.”

Just-in-time functionality

The average smartphone owner has 80+ apps on their phone, yet only a quarter of these are ever used more than once. To address this trend, many businesses switched their focus from acquisition to user retention.

But, instead of continuing the proliferation of apps to deliver our offerings, is there another way we could think about this, new capabilities we could harness? If we can better predict when and where a certain piece of functionality would be desired, we could deliver that experience through entirely different mechanisms — we can design connected environments. With tools like computer vision and embedded devices, we can design physical-digital systems, providing on-demand functionality beyond our mobile devices.

How might we deliver intelligent experiences at the point of need, blurring the digital and physical for ambient functionality?

Today’s pioneering tech in the field of machine intelligence has come a long way since its humble beginnings, creating many new opportunities to rethink how we approach designing products and services.

But these new capabilities need to work for people and meet their needs, so as designers we need to be creative and human-centric in the application of these technologies. We need to drive the conversation forwards through combining our understanding of advanced technologies with design methodology.

Deep design: This article is part of a series by BCG Digital Ventures on the crossroads of pioneering technology and design entrepreneurship. We have launched 30+ deep tech ventures with our corporate partners to date. Follow us on Medium to keep up to date with the latest thought leadership.

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