The world is always evolving and adapting, but there are times when a one-time event leads to rapid changes and adjustments. The COVID-19 pandemic was one such event. In 2020, the headlines are focused on employees working remotely. That may be a micro trend or a macro trend and may or may not last on a grand scale, but there's another trend that has been taking shape in the work world for an even longer time. That is industrial automation. Industrial automation started accelerating due to 2020 unexpected circumstances. Companies are forced to shut down because they must send home workers who cannot do their jobs from home. And now more than ever bringing in more machines looks like a great idea. For many, the rise of automation in the workplace sounds alarm bells. It is the first chapter of a story we all know well: robots will eventually replace us.

The pandemic is accelerating a shift towards people-less companies that will eventually characterize businesses everywhere. The efforts are representative of a broader shift amid the pandemic towards automation, artificial intelligence and digitization. The economy is undergoing a great robotic leap forward, as it removes human touch-points in its operations. Online businesses, algorithms, and automation save costs, boost efficiency and protect public health. Though the shift predates COVID-19, the crisis has accelerated it. People used to expect to interact with other people to get things done. No longer. Much work can be largely handed over to a combination of software and robotics. That is true for blue-collar jobs in health care, food service, delivery, manufacturing, logistics, transport and education. And it is increasingly happening for back-office white-collar jobs in finance, customer service, sales, human resources, law and accounting. It is no wonder that David Autor, an economist at MIT, calls the covid-19 pandemic and economic crisis “an automation-forcing event”.

From 1970 to 2020, the number of bank tellers in the U.S. increased from a little under 300,000 to around 640,000, according to Bureau of Labor Statistics. ATMs have played a major role in this significant growth. The reason ATMs led to more bank teller jobs in the U.S. is, in large, due to efficiency. ATMs allowed banks to quickly open more branches since each branch could be run with fewer tellers, which also meant banks could hire more tellers overall. Yet, when ATMs entered the market, many thoughts these ATMs would replace bank tellers. Needless to say, they were wrong.

72% of Americans are concerned about a future where machine can perform human tasks, with people being particularly worried about the prospect of mass unemployment. With some predictions suggesting as many as 80% of all retail jobs will be replaced by 2030, this fear perpetuates. While the outbreak of COVID-19 made the immediate future of many industries uncertain, many fear that their jobs are in danger and automation will have a negative impact on the economy. They think the pandemic created uncharted territories and unprecedented times leading to the faster rise of autonomous systems.

Discussions about autonomous systems often present solutions as a simple binary choice between automation and augmentation. Automation of processes replaces human decisions and actions by technology. Augmentation, on the other hand, uses technology to support and enhance human behavior, both in making decisions and taking actions. And, contrary to common belief, automation does not always eliminate jobs. Automation eliminates dull, tedious, repetitive and uncreative tasks. If you remove all the tasks, you remove the job. Jobs are made up of a myriad of tasks, many of which are not easily fully automated. Automation can be leveraged to offer businesses and society as a whole unprecedented new opportunities — amplifying the idea that automation is dangerous is rarely ever our best tactic. However, there is no shortage of data to show that, in fact, the fourth revolution eliminates some of our existing jobs. The fourth automation in the history has the potential to eliminate more jobs than mechanization, mass production, and automated production did combined.

The rapid evolution of AI and robots could eliminate more than 500 million jobs by 2030. However, the same report from the McKinsey Global Institute shows that those losses could be offset by an increase in productivity, economic growth and many other factors. Maintaining full employment is likely to be highly challenging as the economy and labor market would require massive overhauls. Midpoint automation could lead to 39 million job losses in the United States by 2030 while rapid automation could cost 73 million. Despite the potential losses, however, about 20 million displaced people could be shifted into similar jobs where they could tackle slightly different tasks. Still, a significant share would have to be retrained completely in the U.S. and many other developed countries. The jobs most threatened by automation tend to be physical and predictable — including workers in the fast food sector or machinery operators. Some jobs will be less susceptible to automation. Those jobs are generally less predictable, including creative roles, engineers, scientists and many other jobs that require critical thinking or creative skills. Yet, all of us will be impacted by the history's fourth automation revolution in one way or the other. In some cases, this impact occurs during a short period of time; in other cases, it takes place over decades. There also typically exists a corresponding amount of data highlighting all the new jobs and industries created by the very automation that eliminated or changed the previously existing jobs.

These data often show that, over time at least, the new automation tends to be a net creator of jobs, i.e., more jobs are produced by the new automation introduction than are replaced. However, we will only turn automation into a net creator of jobs and opportunities if we harness the power of automation to build human-centric systems that are aligned with a human need. This requires us to leverage our product sense to harness the power of automation to augment human intelligence.

DAOs + AI = Explosive Results

Decentralized Autonomous Organizations (DAOs) can be viewed as a computational processes that run autonomously, on decentralized infrastructure, with resource manipulation. The Bitcoin network and the Ethereum network are DAOs. DAOs can live on other DAOs. It is just a script that that sits there, waiting for some transaction to trigger it, to transfer runs according to logic. But most consider “triggered” DAOs still DAOs, because they do not require any human intervention to operate, and they cannot be turned off because they are running on decentralized infrastructure.

Today, AI and DAOs are accelerating at a blistering pace, but with that comes a new class of problem - one that algorithms (at least so far) cannot solve — the human problem. At the end of the day, many AI- powered technologies will impact people at a personal level, and keeping that in mind when designing solutions will become increasingly critical as we progress. If we are not aligned with a human need, we are just going to build a very powerful system to address a small or nonexistent problem. Like any human-centered design process, the time we spend identifying the right problem to solve is some of the most important time spent in the entire effort. Talking to people, looking through data, and observing behaviors can shift our thinking from technology-first to people-first.

As product and design leaders, we rely on our product sense to make correct decisions even when faced with ambiguity. And this is not an easy task. Many times we are facing some unknowns where we do not have all the data we need to make a decision and extrapolating the data does not get us far. Yet, we can rely on our product sense to make a decision that will likely to be the right decision for the overall product experience. We focus on our users and know everything else will follow. We do these by relying on our product sense. So, what is product sense? Product sense is the ability to consistently build for value. As product and design leaders, whether we realize it or not, we use our product sense every single day to make decisions about our product experience, and the experience we want to provide for our end users. And there is one thing and only one thing we are always optimizing for: creating value. If you do not have a product leader on your team that is relying on their product sense to always build for creating more value for the end users, you will end up with a feature factory where your engineers are working on solutions to imaginary problems. Product sense primarily entails creativity, domain knowledge, intuition, reasoning and most importantly empathy. We focus on our users and we know everything else will follow. But you may wonder what product sense has to do with automation. The reality is that even the best AI models will fail if they do not provide unique value to users. In order to build these models in a human- centered way, the most important decisions we make as product leaders are: who are the users we are building this model for? What are their jobs to be done? Which problem should we solve for them? How will we solve that problem? And how will we know when we have succeeded in creating that ultimate product experience? It all comes down to the user. By focusing on the user, we can shift our thinking from technology-first to people-fist.

In order to shift our thinking from technology-first to people-first, the world needs product and design leaders to turn AI Research into products customers want to use, can get value from and can rely on. Automation, AI, and our fourth industrial revolution do not need more researchers, they need more AI-centric products. It is about time to focus more on productizing our machine learning algorithms and build automated systems that solve real human needs and lead to net creator of jobs and opportunities. The goal has to be commercialization. That is the only way to create jobs, wealth and a virtuous cycle of reinvestment. This requires a cultural change. That means understanding our strengths and weaknesses.

In the sphere of automation, AI and DAOs are poised to have a transformational impact, on the scale of many earlier developed general- purpose technologies. The hype around AI and DAOs has created a jargon-filled environment around these very powerful and useful technologies. This is unfortunate because it can make learning about this already- complicated topic even more difficult.

Let’s forget the buzzwords for a moment: what is AI anyway? A broad definition of AI helps us to focus on defining the problem our team is trying to solve, rather than fixate on specific techniques to use in the solution. In the simplest words, AI can be defined as automated decision-making, where you trust an AI-powered system to make decisions based on how the system has been trained. And based on the complexity of the environment, the system is making decision in, you can expect the system to make the right decision without human intervention. As product and design leaders, we must thoroughly understand our problem space so that we can properly define requirements and allow our team to solve the right problems. This often leads to "how" prematurely; before we know it, we have accidentally biased ourselves towards specific solutions before we have properly defined this problem.

Think of the last time you were sending an email to someone and in your email you wrote "see the attachment". You would get a heads-up message from Gmail asking if you meant to attach something to your email. This feature is solving a very common mistake made by users and it is an important problem that Gmail solves but it is not an AI problem. As much as our team might find it tempting to rely on AI to solve this problem, AI is not the most effective solution to address this problem. All your product needs to do is to have a rule-based model to check if the user has mentioned attached, attachment or a variation of that but using AI to solve this would be an overkill. Heuristics work great here and attempting to solve this problem with AI would be an overkill. Now, think of a tourist taking a great photo in front of CN Tower and posting it on Instagram without a caption or location. How can this great User Generated Content (UGC) be discovered by Tourism Toronto so they can share it on their social channels? More than 95 million photos and videos are uploaded to Instagram every day. So, a simple search might not do the trick. We can use AI here to augment this Marketing team's intelligence and empower them to find this photo with landmark detection. Landmark Detection detects popular natural and monumental structures within an image. In contrast to the previous example, in this case, AI can be used to augment the abilities of people, to enable them to accomplish more and to allow them to spend more time on their creative marketing initiatives.

AI is making it easier for people to do things every day, whether it is searching for photos of loved ones, breaking down language barriers, or getting things done with their Voice Assistant. AI provides new ways of looking at problems, from rethinking health care to advancing scientific discovery. We can build an augmented intelligence system that would enable professionals in any space to augment and extend their decision making and creativity.

In the sphere of visual marketing, AI is poised to have a transformational impact, on scale of earlier general-purpose technologies. AI- augmented systems are like trusted teammates, resource we can rely on to get the job done. AI can provide new ways of approaching problems and meaningfully improve our lives. With AI, we have another tool to explore and address hard, unanswered questions. What if people could predict natural disasters before they happen? Better protect endangered species? Or track disease as it spreads, to eliminate it sooner? AI can help, but it is definitely not a silver bullet: tackling these questions requires a concerted, collaborative effort across all sectors of society.

The rapidly increasing usage of AI raises complicated questions: How can we tell if models are fair? Why do models make the predictions that they do? What are the privacy implications of feeding enormous amount of data into models? Models trained on real-world data can encode real-world bias. Let's pretend you are a college admissions officer trying to predict the GPA students will have in college. If a sexist college culture has historically led to lower grades for female students, the model will pick up on that correlation and predict lower grades for women. Training on historical data bakes in historical biases. The sexist culture might improve over time, but the model learned from the past correlation and still predicts higher grades for men. This is just one of the many examples where AI models can encode previous bias. How do you make sure a model works equally well for different groups of people? It turns out that in many situations, this is harder than we might think. And hiding information about protected classes does not always fix things — sometimes it can even hurt. No matter how we build our model, accuracy across these measures will vary when applied to different groups of people.

This is why AI needs product and design sense. AI needs multidisciplinary teams that explores the human side of AI by doing fundamental research, creating design frameworks, and working with diverse communities. For AI to achieve its positive potential, it needs to be participatory, involving the communities it affects and guided by a diverse set of citizens, policy-makers, product managers, product designers and more. Now, you might be asking, what is the secret sauce? How can we shift our thinking from technology-first to people-first? Design Thinking is the answer to building meaningful solutions and user experiences.

Now, you might be asking, what the secret sauce is. How can we shift our thinking from technology-first to people- first? Design thinking is the answer to building meaningful solutions & customer experiences. Design thinking is the glue that contextualizes the technical capabilities of Artificial Intelligence in order to build meaningful solutions and user experiences.

As product and design leaders, when we are building AI-powered products, it is tempting to make the assumption that the best thing we can do for our users is to automate every single task they have to do manually. There are many situations where people typically prefer for AI to augment their existing capabilities and give them “superpowers” instead of automating a task away entirely. By building AI-powered systems with users in mind from the ground up, we can open up entire new areas of development for building people-centric AI systems that solve real problems and delight our users. An example of a delightful experience that allows our users to take a useful action is a model that predicts the likelihood of a user clicking certain videos. But a model that predicts the probability that someone will click "thumbs down" for a specific video does not solve a real problem and does not create any value for the user. A great example of this delightful experience is what Google’s Smart Reply team did. They recognized that users spend a lot of time replying to emails and messages; a product that can predict likely responses can save user time. Similarly, Google Photos team recognized that AI could augment the user’s experience by finding a specific photo by keyword search without manual tagging. Android team augments their user experience by suggesting common actions to their users. If you book a cab to work on Uber every day at a specific time of day, your phone will start suggesting that action. The next time you design a new feature, think about how AI could or could not augment the experience for your users.

For years, academics and experts had been warning that rapid developments in artificial intelligence and machine-learning were set to destroy hundreds of millions of jobs around the world, leaving many of us out of work. But more recently experts have coalesced around a more nuanced point of view: that AI could help us to work faster and smarter, boosting productivity and creating as many – if not more – jobs than it displaces in the coming decades. Automation is not the future, human augmentation is. Algorithms cannot tell you how you can create value in your business, asking those sorts of questions is an innately human capability. Algorithms rely on humans having done that work first. Rob McCarty, director of AI at PwC in the UK, says he is cautiously optimistic about the job-creating potential of AI, and says that “augmentation” will play a big part in the future workforce – be it by enhancing the skills we already have or freeing us up to do more interesting or important things. Most of us are happy to let AI make decisions for us about trivial things, like movie recommendations, but we are less so when there is any major risk involved. Or, we want to know how decisions were made.

AI should also augment the way we work, allowing us to be more productive. Most experts agree that roles involving more repetitive tasks are at a higher risk of automation – think transportation or manufacturing. However, offloading them onto robots could free us up and allow us to focus on other things.

Anywhere there are stretched resources, or too much information or data to manage, is a great place to start with an AI-led strategy. For example, the Babylon Health application, a subscription-based service in the UK that allows you to book video consultations with doctors and health care professionals. It features AI-powered chatbots and a symptom checker. The symptom checker frees up health care professionals and it also augments their stretched resources. As roles requiring less innately human skills are automated, others that do, such as creative roles or those in social care, will expand. We could see totally new sorts of jobs, enabled by AI. Much further into the future, some have suggested we could even see human workers being physically augmented with AI systems.

Mark Zuckerburg, for example, has invested in a company that is researching ways to implant computer chips in the human brain, albeit to cure neurological diseases. And, Elon Musk has reportedly backed a brain-computer interface venture called Neuralink. While that sort of augmentation may sound like science fiction, experts do expect to see a tangible economic boost from AI in the near term. PwC estimates it could boost global growth by up to $15.7 trillion in 2030.

When it comes to retraining the workforce, the transition to future jobs will be an “incremental” transition rather than a massive step change. AI and DAOs are becoming increasingly sophisticated. But, they can also get things wrong. Systems have been shown to be vulnerable to bias, and specialists will be needed to interpret their decisions and make sure they are fair and transparent. This trend will lead to a new role: DAO Oversight Manager (with a role hierarchy based on the person's seniority and experience level). Now, if a DAO causes a major issue, who is to blame, if all of the decisions made by its algorithms have first been programmed by humans? DAOs are likely to throw up a host of ethical dilemmas and firms will require hordes of DAO Ethicists to address them.

As product and design leaders, we are in a unique position to leverage our product sense to use artificial intelligence to augment human intelligence. How can we make our users a better version of themselves. And how can we build better photographers not better cameras. This world needs product leaders that have a strong product sense (empathy, domain knowledge, and creativity) to harness the power of AI. This world needs product leaders that fall in love with the problem and not the AI model built by their data scientists. Eighty-five percent of product and design leaders think AI will give their company a competitive advantage, but only 20 percent have already incorporated it into their business processes and less than 39 percent have an AI strategy in place. Leaders that implement now will be ahead of the curve.

While Artificial Intelligence is the creation of machines that work and react like humans, Augmented Intelligence is using those same machines with a different approach – to enhance the human worker and augment our intelligence. Augmented Intelligence involves people and machines working together, playing to their own strengths to achieve greater business value. In other words, the primary objective of IA is to empower humans to work better and smarter. We can use artificial intelligence to augment human intelligence. And that is how we can make the world a better place for all of us.