The idea that diversity and inclusion should be core drivers of the new economy and the emerging global society are mostly understood at a human level. The more people who are part of one system, being offered the same opportunities regardless of their gender, race, ethnic origin, and many other diverse variables, the higher the tide rises for everybody. But even with that intrinsic understanding of the idea that diversity and inclusion will generate a different and better world, significant barriers still exist to making this human truth a practical reality in our daily lives.
The power of all genders, all races, and all languages can change the world. Even each of these pieces, though, has its blind spots if taken as a standalone viewpoint — in effect, by seeing the world through a single lens, that lens can act as a deep barrier. Imagine how much is lost with only a single way of seeing, thinking, learning, and maybe even applying those learnings.
Digital companies talk about the power of the individual or the customer to be the center of the service. Yet how can we build around individuals without recognizing and servicing the unique combinations of needs or opinions that diverse thinking and actions entail? McKinsey has, since 2014, leaned into the idea of measuring diversity and inclusivity as a driver of business value creation. The intent is to show every year that companies that live and deliver diverse and inclusive strategies outperform their industry peers. The gap (between diverse and inclusive leaders and the poorest performers) has gotten bigger year by year, growing from 33% percent in 2018 to 36% in 2019. Even with clear and longitudinal data, we still struggle against many inherent biases to accept and act on the fact that diversity and inclusion widen the lens for viewing ideas, thinking, processes, and customers in an increasingly global market.
The world will get more diverse over time. By 2044 it is projected that over half of Americans will belong to a minority group. We will in effect be a collection of diversities, with one in five of us not being born in the USA but living here. Multiply this American future by the nuances of each of the 195 countries in the world and together we will be the largest collection of diversities the planet has ever seen.
Now imagine a world of not just 7 billion people, but 40 billion devices computing, connecting, sensing, predicting, and running autonomously in an intelligent systems world. PwC estimated that 70% of all global GDP growth between 2020 and 2030 will come from this machine economy (AI, robotics, IoT devices). U.S. GDP is expected to grow $10T between now and 2030. If 70% of that is from these machines sensing, predicting, computing, and connecting on the intelligent edge, then that is a $7T economy. Will these machines be more capable than humankind has been to think about diversity and inclusion in the way they work with data, humans, and other machines?
These devices don’t have a McKinsey to explain to them where and how inclusion and diversity will drive a better result. They make decisions in milliseconds based on the programming instructions they receive, and they learn as they execute their many, often complex and intelligent, tasks. How these machines learn to think (constantly) are driven by rules set by humans and by other machines that were in part or wholly programmed by humans. How can the right behaviors be instilled in these intelligent systems? Think of two basic dynamics we must pay attention to in an increasingly intelligent systems world:
Human experiences drive diversity and inclusive design
Learning — and applying — how to be aware of the needs of diverse groups has more value than ever before. This acquired knowledge will act as the codex for how we program the devices that live and work with us globally by 2030 and beyond. There is a narrow time window in which to take our own personal experiences and the experience of others around us into account in the design and programming process for intelligent systems that will manage autonomous vehicles, medical devices, and manufacturing environments where cobots will be working alongside humans. All machines might look and behave in the same way, but the humans around them do not, so what machine biases will exist in the intelligent systems world? Understanding how to design and program for inclusive and diverse thinking without bias means intelligent systems need to have a progressive learning ability (e.g., machine learning and digital feedback loops), as well as mission-critical capacities that mean they can safely and securely function around humans who may look, sound, move, or think differently from those whom the machines have been designed or operated around.
Machines will be diverse too and will need to be inclusive of each other
Once we live in an intelligent systems world, we will need intelligent systems to recognize each other in near instant time. These systems might be doing completely different tasks, but they might need to share data, space, or compute capacity in milliseconds. Knowing when, where, and how to have that network effect in an intelligent systems world (for example, consider autonomous vehicles) requires a capacity for inclusiveness and maybe even a clear comprehension about the power of diverse data sets from different devices to create value far greater than the sum of all parts. Nurturing that capacity to create systems for a diversity of design and operations, as well as for an inclusiveness to allow constant learning, is a challenge that will be essential in an intelligent systems world.
We will not be able to make the right world for these intelligent systems and all that they can bring to humanity if we do not design, operate, and build them to be inclusive, diverse, and without bias in how they operate. While not suggesting that there should be a soul to an intelligent system, we should recognize that the moment in our own human world to encourage as much diversity and inclusion in our thinking is right now, and how well we do it will have major consequences for how we teach our intelligent systems to thrive in a world dominated with a diversity of machines and humans.
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