What Will The Future Look Like?

Machines can help us become better at adapting to change

We don’t have to look to a distant horizon or do a whole lot of guessing to begin to answer that question. Physicians have already begun to partner with AIs to improve the accuracy of their diagnoses and reduce unwanted treatment outcomes. Busy executives already leverage digital assistants and specialized bots to automate time-consuming administrative tasks. High-level decision-makers already employ smart analytics products to organize and contextualize data, then transform them into insights. Predictive modelling tools help organizations plan projects and minimize investment risks. 

The power of these sophisticated new tools doesn’t lie in replacing humans, but in helping them save time, make fewer mistakes, become more effective at their jobs – in short, in enhancing them. The world is still a world of humans, for humans, and we should only ever replace humans with machines, not whenever we can, but rather only when we must. 

Let’s play devil’s advocate for a moment and ask why. Why shouldn’t we replace humans with machines whenever possible? If a machine is better, faster, more reliable when it comes to delivering accurate diagnoses than a human doctor, why not replace human doctors with artificial ones? And if artificial executives are capable of analyzing more data than their human counterparts, and at a higher speed, and also deliver better outcomes for their organization and investors, then why not replace human executives with artificial ones? Why would we not replace humans with machines when machines can be shown to outperform humans? 

The short version of the answer is this: when the only objective is increased operational efficiency, machines can indeed often be more efficient than humans, especially when the tasks to be performed are repetitive and predictable. But most tasks – or rather most jobs – are not all that repetitive or predictable. Whether a job has social and collaborative elements, or creative problem-solving elements, or demands human qualities like leadership, inspiration, instincts and improvisation, machines can assist humans but not replace them. Additionally, progress isn’t solely a game of improved operational efficiency. Not by a long shot. Progress is also – and perhaps mostly – a game of imagination, innovation, disruption and creative adaptation, at none of which machines, no matter how sophisticated, are better than humans.

Even sophisticated AIs, which can help humans drive towards revolutionary breakthroughs in medicine, material science, micro-chip design, energy research and a thousand more disciplines, cannot, on their own, do the sort of work that their human partners rely on them to help them with. There is no artificial Steve Jobs, Elon Musk, Jeff Bezos, Satya Nadella or Sundar Pinchai. There is no artificial Stephen Hawking, Steven Weinberg, Peter Higgs or Freeman Dyson. And for that matter, there is also no artificial Margaret Atwood, Doris Lessing, Toni Morrison or Virginia Woolf. In other words, there is no artificial genius, no artificial visionary, no artificial serial innovator, and no artificial leader, and there may not be any of those for quite some time, if ever. 

That is the crux of the problem with machines, no matter how sophisticated they become, and how close they can ultimately mimic human thought. What changes the world, the forces that propel the world and humanity forward, are genius, vision, innovation and leadership, not improvements in repetitive task efficiency, faster computing power or real-time language processing. Moreover, machines are designed to naturally complement human limitations, just as we are naturally equipped to complement theirs: we are, humans and machines, entwined in a symbiotic dance as old as civilization itself. The equation that has driven disruption and progress throughout human history has never been man vs machine but rather man + machine. That enduring partnership has turned the wheel of progress throughout human history, and that is why we should continue to pursue the tried-and-true model of human–machine partnerships that has put humans on the moon, mapped the human genome and put all of humanity’s knowledge in the palm of our hand. 

If disruption is change, and change is inevitable, then disruption is inevitable. No matter what the industry, no matter what the function, change will happen. Maybe not today, maybe not tomorrow, but disruption is coming. 

For instance, how does a mid-level project manager at a Fortune 500 company protect herself from being replaced by a project management AI that her company’s biggest software vendor keeps pitching to senior executives? One option might be to work longer hours, take on more projects, get more face time with the boss, and develop a reputation as an ambitious hard-worker that gets things done. A second option might be to start employing the same type of smart automation product that threatens to replace her in 6-18 months, but make it assist her, on her terms, thereby increasing her output, shortening product schedules and significantly improving her project teams’ outcomes. 

At best, the first option will get our industrious project manager noticed and moved to another function when her job is taken over by smart automation. At worst, while her superiors may acknowledge her sudden and conspicuous zeal, sooner or later the outcome of her desperate effort will be burn-out, which will only accelerate her demise. 

The second option, however, allows her to get ahead of the impending disruption that would otherwise cost her her job, and perhaps even turn the tables on the company’s understanding of how smart automation products should be used. By adopting the disruptive technology that would have otherwise displaced her, instead of competing against it, she became its beneficiary rather than its victim. The new model she is developing for herself in plain sight of her superiors is a proof of concept (and a proof of value) for a human + machine partnership equation that serves as the antidote to an alternative human vs machine equation – a vs equation that would have not only cost her her job but probably delivered less than optimal results for the company. 

In a worst case scenario, in which the company manages to somehow learn nothing from her initiative and still replaces her with an automated project management solution, she can now enter the job market with sophisticated smart productivity and human–machine partnership skills that she can apply to her next job. Ideally, though, the company she works for realizes that the ideal model is to enhance competent, experienced workers with smart automation solutions, not replace them, and management pursues an employee augmentation strategy rather than an employee replacement strategy.

This is just one example of how workers proactively adopting a human–machine partnership mindset can help turn a threat into an opportunity: by becoming an early adopter of disruptive technologies, and incorporating these technologies into their daily tasks, at-risk workers may not only be able to protect themselves from the threat of losing their jobs to machines, but see their own careers advanced by new technologies. 

This guest post is adapted from HUMAN/MACHINE: The Future Of Our Partnership With Machines by Daniel Newman & Olivier Blanchard. 

Book HUMAN/MACHINE: The Future Of Our Partnership With Machines