What impact will the COVID-19 have on business and our economy, and what will this mean for leaders? Futurist Mike Walsh predicts that the pandemic will hasten the arrival of a radical new future of work, and a decade's worth of change in just 12 months. While for many of us working from home has already been a new and unexpected challenge, Mike believes that this is just the start of a much bigger transformation set to reshape the nature of business itself. Watch his video below to learn more - and subscribe to his new weekly video series, New Rules For A New World.
Automation might not entirely eliminate traditional jobs from your company, but it will absolutely change the nature of those jobs and the skills required to do them.
Imagine that you have a job as a retail merchandiser at Coca- Cola, and that you are responsible for visiting stores and kiosks, advising merchants on how to arrange their products, and checking compliance with the brand guidelines. Now, using a platform like Einstein by Salesforce, a customer can just take a picture of their store’s fridge and the algorithm will tell them what to do and where to place their product. What will the purpose of the retail merchandiser be now that their job has been changed by AI? Which of their skills are still relevant, and what might a career migration path look like?
The rise of mass automation brings with it unavoidable, but not unaddressable, political and social consequences. We have been here before. Economist David Autor argues that near the end of the nineteenth century, agricultural states in America faced the prospect of mass unemployment as more automation was introduced into the farming industry. Rather than waiting to see what might happen, those states drove the high school movement, which required everyone to stay in school until the age of sixteen and became the basis for the K–12 education system that is still in place today.
That education system, however, may not be up to the task that we now face. Andrew Ng, also a pioneer in online education and co-founder of Coursera, believes that our challenge is to find a way to teach people to do non-routine, non-repetitive work. To date, our education system has not been good at doing that either at scale or fast enough to keep pace with rapid industry change.
That leaves a lot of the responsibility for education in the hands of employers. Some have already stepped up to the challenge. United Technologies, for example, pays employees’ tuition bills up to $12,000 a year. Facebook offers free AI classes for all their employees, whether or not they work in IT, while Microsoft’s performance review system includes an appraisal of how employees have learned from others and how they have applied that knowledge. However, training is not enough, unless it helps employees migrate to a new way of working and thinking. A good example of a scaled-up migration initiative is AT&T’s Workforce Reskilling and Pivot Program. AT&T is one of the world’s largest employers. The average tenure at AT&T is twelve years, twenty-two if you don’t count the people working in call centers. Internal research com- pleted in 2013 found that 100,000 of AT&T’s 240,000 workers were in roles that the company probably wouldn’t need in a decade. Worse, when the company’s leadership began to analyze the kinds of roles that they would need, they realized there were serious skill gaps. The company would need a lot more coding skills, for example, and more leaders who could make smart decisions based on data and analytics.
To address this, the company kicked off a major reorganization. They streamlined the thousands of job titles that existed at AT&T into fewer and broader categories that clustered similar skills. This simpler classification allowed employees to start planning a more diverse career path through the company and to focus on the new skills they would need.
As part of this overhaul, AT&T created an online system called Career Intelligence that allowed their employees to identify alternative positions, see what skills were required, find out how many positions are available, investigate whether the segment was projected to grow or shrink, and explore what they might earn. However, there was a catch: while the training was free and some of the learning modules could be done at work, employees would have to do much of the work on their own time.
The challenge for companies building retraining programs is that AI is evolving so rapidly, it will be hard to pin down the skills and capabilities that people will need. Even worse than not training an employee for a future job is training them for a job that no longer exists by the time they are ready. Workers will need to constantly upgrade themselves as machines evolve. Algorithmic leaders will have a responsibility, and an incentive, to ensure that both they and the people around them are able to stay just a little further ahead on the curve of the AI revolution in order to remain relevant and valuable.
While lifelong learning is a standard cliché of large organizations, it takes on an entirely new meaning in an age of machine intelligence.
In the industrial revolution, when they first brought automation to the cotton industry, the weavers got upset. They thought, this is our livelihoods here. But as it turns out, they didn't lose their jobs, their jobs changed. Rather than physical labor, their work now became keeping the machines running. As long as they did that, their productivity went up. In fact, 50 fold which meant the cost of cloth began to fall and people started buying more cloth. The total number of people employed in the cotton industry in America, between 1830 and 1900 didn't go down. It quadrupled.
Something similar as you may know, happened with ATMs. Everyone thought the ATM is going to destroy the job of the bank teller. Right? Yes, you didn't need as many tellers to open up a branch anymore, but this also meant it became cheaper to open up branches in many different formats. The job of the teller has changed. You need people who've got social skills, who can empathize, who can market and sell other products who can build relationships.
That's really the question we now need to face. It's not will technology destroy jobs. The question is, how do jobs need to change and in particular, how does our jobs as leaders of the community need to change? I believe we need to become algorithmic leaders. What I mean by that is that we need to become leaders for an algorithmic age. We need to develop a deep understanding of human complexity. This is how to empathize.
What is a good experience for our members or customers, how do we motivate people on our team? These are very human qualities that machines will never replace. There's not enough. We also need to take on some of the qualities of machines too. So we need to develop a flair for what I call computational thinking. Don't worry, we don't have to learn how to program. What this means is in the future, we need to approach making decisions and solving problems in a structured way that allows us to leverage data and technology to augment our capabilities. So something to be frightened of. This is how we're going to give ourselves cognitive superpowers.
As a futurist, I’m often asked what it takes to takes for a large, traditional organization to embrace AI or make digital transformation work.
If only the challenge was just technology! Disruptive technology changes the hardware of your business; to truly become a successful 21st organization you first have to accept that culture is your operating system.
Take Netflix as an example. I have often wondered how an old-school media mogul like Rupert Murdoch, John Malone, or Ted Turner might have run that business. What made the CEO of Netflix, Reed Hastings, so effective? How was he able to achieve such rapid global growth at Netflix while navigating difficult transitions, such as when the company switched from sending physical DVDs in the mail to embracing broadband streaming? Is Netflix successful because it runs on algorithms, or because it is run by algorithmic leaders?
I had an interesting insight into that question when I met Andy Harries, the CEO and co-founder of Left Bank Pictures. Harries is one of the world’s top drama creators, including Cold Feet, Prime Suspect, Wallander, Outlander, and The Queen, which saw Helen Mirren win, among other awards, an Oscar for Best Performance by an Actress in a Leading Role.
Harries wanted to pitch a TV show about the British royal family, based on themes explored in The Queen. He met with all the major US TV networks, who liked the idea but, after lots of consideration and debate, couldn’t commit to moving forward. Finally, Harries decided to meet with Reed Hastings and Netflix’s chief content officer, Ted Sarandos.
It was the strangest meeting, Harries explained, as he handed me a cup of a coffee at his office in London. As soon as he walked into the conference room with Hastings and Sarandos, and before he had a chance to pitch the show, they told him that they were ready to move ahead. And not just with a pilot, but with a full season.
Unlike the other networks, the team at Netflix had already analyzed their audience data and had used algorithms to predict the show’s likely performance. They knew their audience and precisely the kinds of shows that would work. Furthermore, with an upcoming launch in the UK market, they believed that the proposed show would be a hit. And they were right. The Crown’s third season is now in production, and it has twice been nominated for an Emmy for Outstanding Drama Series.
Algorithmic leaders reveal themselves in the way they make decisions and solve problems. How Reed Hastings and his team think about content, its relationship to their audience and their platform, and even how it should be presented and released is radically different from the way traditional leaders in media companies act and behave.
When you are capable of knowing precisely what any of your millions of global customers are doing or desiring at any point in time, how can you not see the world differently? How can you not seek to leverage machine learning, algorithms, and automation to fulfill those needs in a highly personalized way?
How Algorithmic Leaders Are Made
Of course, leaders like Hastings didn’t always have that kind of perspective. Most of us who are currently in leadership positions started out as analogue leaders. We need to make a conscious decision to adapt and evolve and to recognize that the availability of data and algorithms should change our viewpoint.
Being an algorithmic leader means more than just being able to share a few rehearsed anecdotes about artificial intelligence and big data. It means learning to tamp down your own ego, willingly tearing down the corporate structures that support your status, letting go of the idea that you need to make all the decisions, letting your teams self-organize and self-manage, not worrying about being seen to be right all the time, being open to more open forms of partnerships and work arrangements, and embracing a new, uncertain future.
Catching up in LA, we spoke about the lessons that studios like Marvel can teach other organizations about digital transformation, and how we are just at the beginning of a new renaissance in AI-powered creativity.
Geoffrey Hinton, one of the world’s most renowned computer scientists has argued that ‘we should stop training radiologists right now’, and that as a result of AI, most would be out of a job within 5 years. But is this really true? Dr Hugh Harvey has a unique perspective on this question, having worked both sides of the fence - both as consultant radiologist, and also as leader in the AI space first at Babylon Health, and currently as the Clinical Lead at Kheiron Medical. Catching up with Hugh in London, I was keen to find out about the impact of algorithms on employment in the healthcare, and what it might mean to be a radiologist in the 21st century.