The aesthetic of the future is dead

The iPhone. Seamless, reflective, cool to the touch and enigmatic in its lack of expression. In its otherworldly appeal and promise of infinite possibilities, Apple’s design aesthetic captured the impossible: the digital made physical. This aesthetic of sci-fi minimalism and futuristic elegance – the digital code – has for decades been the visionary fantasy of human progress.

Today, that fantasy is reality.

The digital code does not promise a better future anymore. In fact, it represents a dystopia where we don’t control our devices, but they control us. When our devices are designed as black boxes, it does nothing to alleviate our lack of understanding of the technology we are intimately relying on.

With the current big-tech backlash around privacy issues and the urge to “disconnect” for fear of what technology is doing to us, we are in need of a new visual and material language that would help us feel at home with our devices. Our technologies and tools shape us like we shape them, and thus there can be great power in design actions. In his book Make it New: A History of Silicon Valley Design (2015) Barry M. Katz posits that it is design that has always “provided the bridge between research and development, art and engineering, technical performance and human behaviour”.

We believe that the winners of the future are those who through design language and form re-forge a meaningful, positive relationship between humans and technology. For in the everyday, it is the physical manifestations of digital ideas that shape our relationship to technology – and ultimately, to our future.

For the past centuries, the dream of the Western world has been to transcend our bodies and reach a blissful state of knowledge and purpose. Humanity has worked towards those better futures through technological development. For in developing new technologies, we improve our own capacity to do more, to learn more, to better ourselves.

The iPhone offers high levels of functionality, ease and simplicity, with no time wasted on romantic appeal or tactile qualities. At the time of its release, the fluidity and otherworldliness of the perfected glass and chrome rectangles promised a better world in the digital future. The internet had already opened the door for a possibility to rethink who we are by transcending location, ability, race, gender.  The iPhone put that in our pockets, promising that digitalisation would transcend humanity from their material condition, abstract them from time and place, for good.

The digital code does not promise a better future anymore.

In his online essay Digital Dualism versus Augmented Reality (2011) the sociologist and social media theorist Nathan Jurgenson introduces the concept of “digital dualism”: the belief that the digital world is “virtual” and the physical world “real”. This concept is central to the ontology behind Apple’s design philosophy. The magical iPhone allows us “real” beings to blend into the “virtual”, allowing us to truly master our universe.

The iPhone became the icon of the digital code, and its design aesthetics were replicated across major consumer electronics brands and spread from cosmetics to food packaging and jewellery. Still today, the iPhone sets the design direction of consumer electronics for the year coming in their annual Autumn launches.

Building a design aesthetic that lasts

For a design aesthetic to stick rather than be a fleeting trend, it needs to be situated on the continuum of aesthetics past, present and future. A resonating aesthetic is hardly spontaneously occurring, but a product of previous art and design. Indeed, the iPhone’s success does not stem from miraculous “out-of-the-box” thinking, but a deep understanding of design. Design that builds on the social and cultural aesthetic – whether it rejects, updates or evolves – is intuitive and purposeful, and is easily adapted as part of everyday rituals.

The roots of iPhone’s design aesthetic can be traced back to the beginning of the 20^th Century, particularly to the movements of Futurism and Modernism.

Futurism’s influence on the digital code is undeniable. In the beginning of the 20^th Century, with the rise of industrialization, the belief in technological progress’ ability to emancipate the human race had unprecedented proof. Human capacities rapidly expanded beyond imagination, leading to technological utopianism – the belief that technology can solve any problem humanity faces.

Technological utopianism found an aesthetic in Futurism, introducing the fetishization of technology and progress to art and literature. Traditional art forms like painting and sculpture drove the change towards the “Machine age” that would renew consciousness through the aesthetics of speed, progress and industrial development. The newly popularised mediums of photography and cinema brought to the mainstream fever dreams where machines, steel and fire speeded humans to the future. The birth of popularised science fiction can be traced back to 1927 when Metropolis, the first ever feature length sci-fi film premiered. Today the film is an icon, but when it originally came out, the eroticised Maschinenmensch was greeted with suspicion.

Nevertheless, from there on the aesthetics of the future have been defined by the materials linked to industrial progress – chrome, shiny glass, stainless steel, electronic bright colours – and a hard-boiled belief in the perpetual future. Apple lives by this cultural myth with their constant updates both in hardware and software that force renewal – think of the removal of the headphone jack in recent models that pushed people to wireless listening, or the software updates that eventually clog up the memory, pushing people to larger capacities. The iPhone aesthetic religiously replicates these dreams with its materials; glossy glass backs, metallic hues, inorganic colours and a growing lack of tactility, reminiscent of the un-embodied nature of virtual space.

Industrialisation also gave birth to the second key influencer of the digital code – the Modernist movement in design. What separated Modernism from previous arts and crafts movements was how it prioritised functional requirements instead of traditional aesthetics. As a result, Modernist design was simple and clean, based on rationality and functionality, always aiming for a seamless and purposeful experience.

In Modernism, everything that is not necessary, stands in the way of progress. In his 1908 essay Ornament and Crime, the influential architect and designer Adolf Loos states that “ornamentation represents wasted labour and ruined material”. Loos’ radical aesthetic purism attributes cultural evolution to purity of form, seeing ornamentation as childlike and primitive, a “phenomenon either of backwardness or degeneration”. Purity of form represents the essential, superior in its freedom from distraction.

Modernist ideals carried on through the century: in architecture, most notably in the Bauhaus movement, and in industrial design, in Functionalism. The simplicity and seamless purity of the iPhone is a direct descendant of Modernism. Dieter Rams, a famous Functionalist designer, laid down ten principles of good design still widely recognised today. He determined that the measure of good design is how effective and simple it is: good design should be unobtrusive, always neutral and restrained. Think of the iPhone X as the zenith of the digital code, bezel-less as if nearly blending in to virtual space, becoming so simple and essential that it’s close to disappearing altogether.

The simplicity of the hardware is replicated in the software. The iPhone uses Flat Design Language for its user interface, a design interface language that is a direct descendant of the Bauhaus movement. First adapted by Microsoft in the early 2000’s, flat design uses minimal 3D elements, such as shadows or textures. Later, both Apple’s and Google’s operating systems adopted the same design language, dropping skeuomorphism – a design language that retains ornamental design cues.

Today, all major consumer electronics use Flat Design UI. The popularity of flat lay pictures is a testament to how, when a design aesthetic is intuitive and purposeful, it evokes meaning and is adopted and appropriated by people. This, in turn, guarantees its longevity.

What then? The oversaturation of an aesthetic

There comes a time when an aesthetic becomes so widely adapted that it starts to lose aspirational meaning.

The iPhone X is the logical conclusion of the Western obsession with formal purism – an item so simple and perfect, it seems otherworldly, god-like. The boundless design reflects its miraculous technological qualities – it is the rightful tool for homo universalis, the renaissance man whose knowledge knows no boundaries.

Today, the dream of the digital has been realised, the aesthetic seeping everywhere. The digital code does not signal a future anymore, it has become the status quo. It reflects the desires and needs of a bygone era, where digitisation was only a fantastical dream – not a mundane reality.

Today, we can connect across the globe, experience places and things away from our physical reality, appear as whatever we wish in online communities. Yet we humans, even now that we live in a digital world, cannot escape our analogue selves. The way we interact with the digital, what shapes our relationship to it, will always be somehow embedded in the material world. And that material world together with our embodied selves is a large part of how we create, learn, communicate, and feel.

What lies behind the sleek, glassy surfaces of the iPhone is a whole philosophy about technology – an ontology of what technology means to humans now shared and disseminated globally through consumer electronics and beyond. But that philosophy, the philosophy behind the digital code, does not respond to our cultural needs anymore.

We face altogether different issues today that the digital code is unable to respond to. With growing computation speed and ever improving nanotechnology, we are questioning the reason for our existence. We struggle simultaneously with boredom and meaninglessness as well as information and stimulus overload.

The digital code does quite the opposite of helping. Consumer electronics are fetishized, adorned, treated as sacred objects. We protect them with casing, we wipe their screens so that our greasy fingerprints that reveal our embodied self, won’t show. We hold them at the end of our fingertips, delicately, always taking care that they do not fall.

Our devices have become the object of attention. Ever multiplying and largening screens demand our time with push notifications and pulls-to-refresh. The use-object has been transformed into a kind of phantasm whose existence is a psychological projection of our quest for meaning, estranged from its social and embodied reality. As a consequence, devices start feeling inhuman, cold and scary rather than sublime and otherworldly.

In order to not feel subservient to our technology, we need to find a different aesthetic of our future. The response outside tech has been the return to the “real”. A testament to this is the emergence of DIY-culture in fixing your own bike, scrapbooking, making craft beer and pickling, being an explorative cook and a forager.

However, “authenticity” as a trend is not a long-term strategy for technology. Nostalgia for better times is not progressive like technology inherently needs to be. In the long run, we do not want to just reference the design character of historical non-commodity modes of production (“craft”) and paint them on modern ones (a wooden cover on an iPhone).

Instead of falling to technophobia or neo-Luddism and shunning the technological in pursuit of the mythical “real”, we should keep looking forward. We should break out of the ontology of digital dualism and stop seeing the “virtual” and the “real” as separates.

Spotting signs for a new design philosophy

Rather than valorising the “virtual” over the “real” or vice versa, future aesthetics need to balance and play with different aesthetic cues and find a way to co-exist.

There are some signs of a paradigm shift already in technological innovation. The newer MacBooks take a step into the analogue world. Instead of their trademark keyboard that glorifies the seamless, effortless typing, the latest in the series are characterized by their clunking sound. This evokes the analogue world of a typewriter, asserting the creativity and productivity of the individual.

The fluidity and seamlessness of the digital code is also being challenged in the way technology is operated. Sound and touch are challenging the supremacy of vision as the primary sense of being-in-the-world. All big tech companies are innovating around voice controls, rather than screens and buttons, recognizing the naturalness of speech for humans as well as its inherently social role in comparison to the individualism of screens. Touch, too, is becoming more recognised when innovating with materials and more sensitive haptic cues.

Turning signs into typologies and guidelines

To evolve our relationship to technology, it is crucial we address the paradigm shift on the level of design aesthetics and language. The task is still to make the abstract understandable and relatable, in other words, to make the invisible visible.

Armed with a different philosophy of time, space, and cognition, we can design a different future. In the end, it is designers who give shape to our technological dreams, who shape into being the immaterial by creating physical manifestations of it.

The winners of the future are those who through design aesthetics and language re-forge a meaningful, positive relationship between the “real” and the “virtual”. As Dieter Rams observed in his essay Omit the Unimportant (1989): “Our culture is our home. It would be a great help if we could feel more at home in this everyday culture, if alienation, confusion and sensory overload would lessen”.

By identifying design typologies from the world of technology and design today, we can begin to decode the intuitive form of an aesthetic, and generate guidelines to purposefully build towards an inspiring and future-proofed aesthetic. Here, we have identified and described three typologies with some key examples. 

1. Organic tactility

Organic tactility downplays the other-ness of technology, instead valorising the organic. It uses natural shapes, textures and colours that are responsive to the touch, comforting in their familiarity from the natural world. The designs are not highly finished and polished, but an aesthetic designed to engage the senses beyond the visual. Hand-touched signifiers evoke safety, security and humanity.

• Google Home emulates a stone in its form and uses a textural finish to soften the look and help it blend into surroundings
• Muji x Ladies and Gentlemen studio installation at New York Design Week 2018 explores the design ethos of minimalist Muji, focusing in on its honest materials.
• Native Union uses raw materials like marble finishes and textured layering on devices and appliances to bring a grounded aesthetic and feel to technology.

2. Empathetic functionalism

Empathetic functionalism over-emphasizes technology in an irreverent and playful way. The retro-analogue code emphasizes human agency and creativity. The primary-colour and overtly simple, matte aesthetic reflects the curiosity and empathy of the designs, rather than positing them as tools for superiority or conquest. The bold and cute signifiers cue innocence, optimism and cosiness.

• Fujifilm Instax presents an analogue alternative to Instagram. It re-asserts the joy and expressiveness of the photographic moment by departing from a generic high-tech aesthetic of precision and technical finesse.
• Nokia’s re-introduction of the 3310 has a rounded, empathetic design and bright, optimistic colours.
• The lofree keyboard design recognizes the importance of tactile response to actions, and its matte colour palette brings a cosy, soft feel.

3. Bionic beauty

Bionic beauty emphasizes the likeness of the natural and the scientific, going back to roots of renaissance where art and science were not seen as separate. It replicates ecosystems and models of the natural world in biomimicry. The signifiers mixing the real and the synthetic evoke curiosity, progress, and sustainability.

• The conceptual designer Lola Gielen’s Neo is a tactile musical instrument. “Music is a sensory experience, while playing a traditional instrument sound is directly influenced by touch. This is often lost in digital instruments. Even though Neo is a digital instrument, your touch will still influence the sound”.
• Adidas Futurecraft 4D + Studio Hagel sneakers use a 3D-printed midsole. The cutting edge technology replicates natural processes in mass manufacturing by moulding liquid with light and oxygen. This gives designers unprecedented opportunities in terms of material design, celebrated in the collaboration with Studio Hagel’s re-imagination of the shoe.
• Patch, by designer François Chambard / UM project, is a project featuring interconnected furniture that makes electricity visible and part of natural design, offering an alternative to the current smart home designs.

No technology reveals as much about our relationship with the things we build as robots. In part, this is because robots and robotics are still more idea than reality. The idea of a robot centers on its ability to be the equal of humans: it is supposed to be a machine created by us that can rival us in every way. Because we are not actually able to create machines that get anywhere close to this right now, we are not in immediate risk of a robot apocalypse. But it is the idea that we can try, and may succeed one day, that captures our imaginations. We see frightening potential in a robot, and it is this that makes us uneasy. We see robots as something that can eventually be exactly like us. And as an idea, robots become a technology that forces us to ask what makes us human, and what our relationship with our technological creations should be like.

In a more practical sense, robots are exciting because they have function and purpose. They do work that humans either do not want to do, or cannot do. They tirelessly build things with more accuracy and efficiency than a human worker. They can go to Mars or to the bottom of the ocean and extend the limits of our reach and knowledge. And they can clean our floors while we do other things.

All of this provides us with immediate value and justifies the development of robots. But this makes them little more than highly-capable tools. The relationship we have with our tools is defined by human dominance and control. It is the human that uses the tool. The tool is a passive technology that is only used to accomplish a task. As tools that do things, robots are not a direct challenge to our existence or even our sense of what it means to be human.

So why is there such a gulf between the idea of robots and the practical reality of what robots are now? The idea of a robot was developed long before we developed the technology to fulfil this potential. The concept of a “mechanical man” is now over 500 years old, but the technology needed to make this a reality is still being developed. The idea comes from the past, while the technology lives in an uncertain, unfulfilled future. What this means is that our cultural understanding of what a robot is, or should be, is a lot more mature than the technologies needed to make it a reality.

Remember: this is still an idea. But this idea is the driving force behind the development of robots. This idea is more important than the practical reality of what a robot is now. And this idea is culturally determined, which means it is a collection of assumptions and beliefs that are far from the practical realities of engineering new technologies. Because it is a cultural construct, it can be different in different cultures. Hence, we must understand the assumptions and beliefs that shape our ideas of robotics. Only after we understand these driving forces, can we understand the value of robotic systems.

A Revealing Relationship

In the West, robots are not entirely good or safe things. We see them as useful, but potentially dangerous, and we are suspicious of them as a result. Much of this unease comes from a long history of literature and story-telling. Writers, filmmakers, and thinkers have created ambivalent examples of robots since Karel Čapek coined the term in his play R.U.R. (Rossumovi Univerzální Roboti or Rossum’s Universal Robots) from 1920. Shortly thereafter, Fritz Lang produced the film Metropolis (1927) where the robot created by the scientist Rotwang in a plot to take over the city of Metropolis. Lang’s film can be understood as a statement about the problematic relationship between people and technology. The robot that Rotwang creates can transform itself into an exact copy of the hero’s lost love Hel, and as such is able to be human, but the robot is not benign and proves to be the centrepiece of the scientist’s dangerous plot.

While Metropolis is the first cinematic statement of the problem of human-like robots, its depiction of the problematic relationship between humans and our creations is much older. For that we can look to examples like God and Adam, Chronos and Zeus, God and Satan in Milton’s Paradise Lost, Dr. Frankenstein and his creature in Mary Shelley’s Frankenstein, and many others. These stories show us that there is always a difficult relationship between a creator and their creation. Adam defied God by eating the apple with Eve. Zeus killed his father Chronos in a rebellion. Satan spends much of Paradise Lost thinking about how his relationship with God is restrictive, and how he wants to supplant his father. And Shelley shows us the dangers of creating new life through technological means as the creature struggles to understand himself, eventually becoming vengeful and seeking to destroy his creator.

They also tell us that the relationship between creator and creation is a difficult one and that it may often end in destruction and sorrow. Like all myths and fables, the telling and retelling of this basic story is the way in which we solidify cultural ideas and come to believe them completely. The implication of this is clear for robots: the relationship will be problematic and robots will come to be dangerous, and the robot apocalypse is coming. That we often develop military systems capable of killing remotely or autonomously adds a fatal reality to this possibility. But this is just one cultural construct amongst many. It is not strictly true. We know this because there is another example that tells a different story.

There is a counter narrative that is told just as frequently, and it tells a story of autonomous, intelligent cybernetic systems that are friendly, loyal, and hardworking. Films like Star Wars, Short Circuit, and AI, books like Isaac Asimov’s Robot series, Karl Schoeder’s Lady of Mazes, and television series like Star Trek, Lost in Space, and Astro Boy follow a pattern of robotic systems that work in collaboration with humans. Here, we overcome our fear of our creation. These robots are helpful, even kind. They provide humans with companionship and emotional support. They rarely question their creators. They even work hard to become more human. Theirs is a tale of a well-adjusted system that has found a way to work in harmony with their human colleagues.

These differing stories about technology focus on the relationship between humans as creators of artificial beings and their creation. Their difference lies in how this relationship is articulated. In the first, the creations are struggling to find a place in the world. Most of those stories depend on the fact that the robots do not have a place in society, or become unable to participate in it as healthy members. The second set of stories show us the potential of a robotic being that has found a place in human society and culture. Together, these stories present examples and patterns that help us understand our relationship with our technology. They also present us with an insight into what we expect from our technology, and that we seem to have a choice. We either provide our robots with a role in society, or they will struggle to work with us and become potentially dangerous. 

Defining the Purpose to Unlock their Value

Dr. Jin Oh Kim, professor of robotics at Kwangwoon University, describes a simple model that allows us to see robots for what they are –  tools that we use to accomplish a task.

Dr. Kim’s model is straight-forward:

Human → Task

Human → Tool → Task

Human → Robot → Task

By focusing on how a robot mediates a human’s approach to accomplishing a task, he has provided us with a way of looking at any real-world situation where robots are put to use. The benefit lies in that his model is unburdened by the mythologies described above. Here, the robot is designed and used to fit a particular purpose. That purpose is defined within the relationship between the human and the task.

All of a sudden the threats presented by sentient, malicious robots fall away, because within Kim’s model, a robot is just something designed to fit a purpose. It does not need to be capable of challenging us, and as a result it is less of a problem. In this way, we see that when robots are applied to a task, that they become smaller, simpler, less problematic systems. There is no challenge, because they become a tool that helps the humans in the equation of accomplishing something. The robot is a more complicated version of a knife or a screwdriver. However, it is not something that needs to challenge the capabilities or social status of a human being. If applied only to meet the needs of a task and amplifying, or modifying, our capabilities, there is no reason why it should threaten our innermost identity.

What this illustrates is that in real-world situations the value of a robot depends on the nature of the task and the capacities of a human being. The robot is the mediator of the effort and skills needed to accomplish the task and needs only to be advanced enough to accomplish what jobs we set for it.

The value of a robot lies in how well it is suited to the task that it is designed to accomplish. The principles of MVP (minimum viable product) apply here because it need not be anything more than that. This changes completely the nature of the problem of the robot’s relationship with the human being. It no longer needs to be anything that threatens the human, because in this situation the robot is there to help or enhance the human’s abilities only. This means Dr. Kim’s model provides us with an alternative to both dangerous and friendly robots. It allows us to see robots as mediators, collaborators, and extensions of ourselves. And this means that the robot has a social role. It has a place in society and a purpose for existing.

This place in society is enough to ensure that it never challenges us, simply because we need never design it beyond this human/task relationship. However, it means that we must begin to look at robotic systems in a more holistic way. It must be framed by the limits of the humans and the tasks in the equation. The robot’s value lies in how well it helps us humans accomplish our tasks. But this means that to create value through the application of robotics, we must look beyond the technology and to the task and context of the situation.

Ultimately, finding the value in robotics is still more about the human context than it is about the limits, problems, or capacities of the robotic system. The best way to approach value-driven applications of robotic technology of any kind is to begin by understanding the situation in which it is needed. So, finding value in robotics involves finding what is valuable to human beings and creating a contextually-driven set of solutions that describe what kind of robot is needed and what it will do. In the end, the robot will be a mediator and its only value will lie in its relationship with its user.

Artificial intelligence, or AI, is one of the most popular business buzzwords in circulation today. Many people are talking about how AI will transform how we live, work, and play. Journalists, politicians, futurists, and technology companies are also talking about how people all over the world will lose their jobs after they are replaced by thinking machines that do everything better than their lowly human colleagues. Companies of all kinds are asking themselves how AI can help them compete better, be more innovative, and be more efficient. The idea of AI has captured our imagination so completely because it promises the ultimate technological dream: a machine that can do more than a person ever could.

Much of this is just hype. The truth about AI is relatively boring compared to the utopian fantasies that suggest we will all soon be living a life of leisure while our computerized friends do all of the work. Until recently, AI has been little more than an area of advanced computing. But with new developments—the ones that are generating all of this hype—artificial intelligence is poised to become a truly transformative technology.

Despite this potential, however, the real value of AI has yet to be realized in business. It already is an amazingly useful technology and is full of potential for the future. But its usefulness is being missed among the fantasies and problematic dialogue. This is because few understand how it works, let alone how to unlock its potential. Business leaders are not AI specialists. For many, AI is still largely just a part of the world of big data, or it is just a magical black box that you must “get” in order to compete. In part, this failure to see what AI technologies can do is due to the fact that there is a fundamental problem surrounding AI that needs to be addressed. Simply put, the hype is clouding our ability to see what we should do with this technology. And this is leading to a crisis of missed opportunities because it is not being applied well.

This story of AI has deep roots and great meaning, and it shapes how we understand what AI is and what it should be doing for us. Many of our most sophisticated AI technologies have been around for over forty years in some form or another. And we have been living with many of these technologies for years. This long history confuses the issue because much of what AI is now are technologies and processes we’ve known and used for years. Also, what we mean when we say “AI” today is only one of many definitions, and we’ve lost sight of the fact that our computer-aided world is full of carefully applied variants of AI technologies.

Visions of artificial beings have been around for five hundred years or more in one shape or another. Medieval craftsmen made mechanical monks, the earliest artificial humanoid machines. Inventors and conmen in the 18th and 19th centuries made automatons that could play chess and even speak. The science fiction of the last 100 years has added greater detail to this vision and told stories of the potential positives and negatives of creating artificial beings with thoughts of their own. All of these attempts and stories have whetted our appetites for AI and AI-enabled robots, and contributed to a feeling of the inevitability of AI’s “future” trajectory. They all tell the story that machines can match human capacities and be creative, reasoning, and social beings.

But because these stories are so caught up in the idea of general AI—a thinking intelligence that has similar, or greater, capacities to a human—they are also a burden. They hide the fact that AI is a tool that can provide solutions to many problems. While we are distracted by the idea of an artificial person, AI’s true utility as an applied solution is lost.

Making the most of AI’s potential lies in finding a purpose for it. We need to focus on applied AI, and finding ways to put AI to work now. But applied AI is not as sexy an idea as general AI. Instead of autonomous robots that can do anything, we will have more humble systems managing smaller problems. The future of well-applied AI will include self-driving trucks and trains that move cargo. It will feature computer systems that anticipate problems in traffic flow and make the necessary corrections to keep cities moving. It will also be filled with AI enhanced consumer experiences that allow us to navigate web touchpoints more easily.

To unlock AI’s potential, we need to look beyond the technology. We need to start asking good questions about how we should mobilize our technological forces to accomplish concrete tasks or solve important problems. We must begin to ask ourselves how AI can make a difference in all of our lives. Finally, we should also ask how companies can best apply artificial intelligence to create products and services that will be truly valuable to their customers.

To answer these questions, we must stop looking at AI as something that needs to be fully intelligent, and start looking at it as something smaller that can be applied to improve people’s lives and create new opportunities for creating compelling experiences.

What this means is that providing value right now, or in the next ten years, through the use of AI involves five points to consider:

1. You have to first cut through the hype and see AI as a set of tools that serve a function.
2. Next, you must assess who you are solving for and understand their problems.
3. Then you must design a better outcome for you and your customers.
4. Once this is done, you need to find the right kind of AI to fit the purpose.
5. Finally, you have to apply it with a light touch.

1. Cut through the hype

Cutting through the hype means understanding AI as a tool and avoiding the belief that the future of AI lies in general AI alone. Like other technologies, AI is very good at scaling tasks that humans can do already. It adds speed, a wider global reach, includes more people, and provides a greater level of repeatability. Its primary value is that it can do the work of many humans, faster, without tiring. Because it never has to be turned off, it also provides continuous presence. It is best employed to manage large amounts of information or to find novel or surprising patterns in information-rich contexts that quickly confuse individual humans. But all of these things are somewhat abstract. These capacities only come into focus when you put AI to work on a specific task. And this is what it means to apply AI.

AI can play chess and Go, but these are goal-oriented applications of pattern management and decision engines. IBM’s premier AI system, Watson, can play Jeopardy, but it is not actually a single AI system. It is several that have been connected together to accomplish a task: playing the game. It is a natural language processor, two data retrieval systems, a decision engine, a set of trained models and filters, and a final natural language processor. It reads the clue, deconstructs it, explores unstructured data for possible answers, scores the validity of these answers, synthesizes these results using human and taught filters and then constructs an answer. But these systems were built to serve a purpose, and it took time and human effort to figure out how to best tackle the problem of providing a good answer within the rules of a game. If Watson had been designed to do something else, it would have different components. It took time and effort to generalize it—to convert it from a Jeopardy! playing machine into a sophisticated analytics engine.

This is the most important thing to understand about applied AI: the context, goal, and desired outcome matter more than the technology. They guide how AI is implemented, and this defines how it is built. What this means is that applied AI is really more about design and problem solving than it is about raw technology. Yes, expertise in AI is absolutely necessary, but an engineer cannot work without a problem to solve. When we think of applied AI not as a fabulous technology, but as a resource in a design exercise focused on making life better for customers and users, we find the true value of AI.

2. Assess who you are solving for and understand their problems

The next step in correctly applying AI is to identify a problem that AI can solve or improve. To do this you have to understand the lives of the people who will be using the system or who will benefit from its application.

Today, AI can be found analyzing natural language to serve as an interface between people and relatively dumb systems like Amazon’s retail business. It is also serving to improve searches on the web, or improve the quality of Google’s translation services. In each of these examples, the AI is an enhancement of something that existed before. People were able to order books, socks, or anything else from Amazon, but Alexa provided an application of natural language processing that created a new, effortless interface. The incredible work done by Google’s team in improving their translation services was a modification of an existing product. Newer forms of neural network processing augmented their existing expert-system AI translation and made it faster and more natural.

In both of these cases, the application of AI improved something that people were already using. It made users’ experiences better. In both cases, however, these improvements were made to serve people better, and in more natural ways. Applied AI should be used as a tool to enhance people’s lives. Both of these examples demonstrate a human-centric approach to design. The AI was used to solve problems like lag, or to create new experiences that engaged people more directly and naturally.

Understanding your customers always begins with learning about them directly. What this means is that the process of applying AI well begins with human-centric investigations. An ethnographic exploration of your customer’s lives will reveal pain-points, needs, hopes, and desires that can be addressed through careful design.

3. Design a better outcome

Once you understand your customers better, you can begin to design new experiences, products, and solutions that will meet their needs. This design process should include a reimagining of your relationship with your customer. This will reveal the opportunities where AI can be applied to the greatest effect. But the opportunities are ways to create new products, services, or experiences that are enhanced with some form of AI, not opportunities to use artificial intelligence. Because of this, the design process best suited to this task is one that is based in the iterative design principles of design thinking.

4. Find the right kind of AI

Next, look for opportunities to use AI to enhance the function, scope, and purpose of your newly designed solutions. To do this, it is best to begin with an analysis of what you hope to accomplish. Do you want to remove a pain point? Do you want to enhance the abilities of your users so they can do something with the help of the AI that they could not do before? Do you want to include more people in completing a task? Or do you want to create a novel experience that was not possible without the application of AI?

These questions are essential because they outline the relationship between the user and the technology. These relationships define what the AI must do. Understanding them helps you decide what kind of AI will be needed to provide value for your customers.

It can expand or amplify the capacities of a user:  The user is able to expand their own abilities beyond what they could accomplish alone. The tool can be seen as an adaptation of their body, knowledge, or information processing abilities. The tool and the user work together to accomplish the task and the relationship expands what is possible. Examples of this include centaur systems that help people problem solve or strategize, such as the forecasting platforms used by the financial industry. They help expand the reach of an analyst by providing them with information they would not be able to gather quickly otherwise.

It can shift a user’s relationship with the skills and/or knowledge needed to accomplish a task: The tool displaces some of the capacities or responsibilities of the user and assumes them itself. The user is either relieved of unwanted responsibilities, or must give up some of their own. One example is the centaur systems that are used in Free Chess, a variant of the game where a computer and a human play chess together against another team. The player and computer share the responsibility of choosing the next move, something that has created a team that is better than human or computer alone.

It can change the relationship between a user, or a group of users, and the skills necessary to complete a task: The tool transfers capacities or responsibilities between other users or other tools. The tool allows for the sharing or exchange of roles or responsibilities. It can often be a platform for the redistribution of user’s roles. This relationship is a familiar one. When people talk to Siri, Alexa, or Google Assistant, they are giving up a lot of the responsibility for what is needed to complete a task. The AI takes care of everything once it understands the request. These NLP (natural language processing) interfaces understand what you want and translate the request into something that the computer systems in the background can process. You could do the Google or Amazon search yourself, instead they do it for you, and make some decisions on your behalf.

It can change the number or type of resources needed to complete a task: The tool combines, collapses, or eliminates roles or responsibilities. The tool allows users to accomplish tasks that might require multiple users, or to eliminate entire sub-tasks entirely, thus eliminating roles. Machine learning has transformed how we process information. Technically, humans can do this kind of processing too, it would just take thousands of people and a very long time to complete the task. The AI eliminates the need for all of these resources and can process it quickly. This is the relationship that is at the core of the threat to worker’s jobs, because AI can eliminate many of them by providing a quicker, lower-resource route to completing a task like data processing and analytics.

It can produce novel solutions to problems or tasks: The user is able to do something they otherwise could not without the tool. The tool and user work in conjunction to achieve either a new task or to do it in a novel way. This is the rarest of the alterations. This is what we are really working towards with AI. We are looking for new solutions to problems. One very simple example of this is the fact that Google has put image recognition into its Google Photos application. Now you can use a keyword to search for the content of particular photos. It is an image processing AI that provides the foundation for this service.

Like Watson, no AI system is going to be a single technology. The secret to successful applications of AI lies in using it to create one of these relationships. Once you know what you want to do for your customers, you can then begin to design the system that will carry it out. This is simply about finding the best tool for the job. In most cases, you are not going to be creating this yourself, so finding the right technology partner will be an essential part of the next step.

5. Apply with a light touch

Finally, remember that the goal of any new product, service, or experience is to provide the best solution for your customer. Avoid taking a technology-first approach that prefers the technology over their needs, and use your new technology to provide for the needs that you identified earlier. Technology works best when it is invisible and works in the background. Artificial intelligence is no different. People should never know that they are dealing with AI. They should feel that they are being served well by the company they have chosen to provide them with the product, service, or experience.

Like any technology, applied AI is really nothing more than a tool that we use to accomplish a task. Its value lies in how well it does this. Ultimately, this means its value is measured in how well companies use it to serve their customers well. Once the novelty of artificial intelligence wears off, this is really all that will be left. So, to unlock the value of AI, it is best to move past the technology completely and serve your customers well.