The Autonomous Workplace

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The Autonomous Workplace

Automation and AI are big topics of discussion as we move out of the pandemic and back to offices. What does it mean for the future of working?

The idea that autonomous systems will replace livelihoods has become an almost inescapable part of the discussion around artificial intelligence (AI) applications. Since at least the 1970s, people have worried that technology will replace humans, leaving untold numbers without livelihoods.

This is, in part, because when the inner workings of a technology are obscured. When the inputs and outputs that go into a decision are visible, but not what happens in between, the average user has difficulty understanding if the action carried out by the agent was right. The result has been people fretting over the implications of algorithms, machine learning (ML), and AI.

Modern AI and ML applications are often given the label “black box”, because the reasons for the algorithm’s decisions are not easily accessed or understood. The technical details of the applications are usually described using dense jargon that only small groups of experts can decipher, and this makes the acts carried out by them difficult to question or to probe.

It leaves many folks questioning both the short- and long-term implications of the technology: What does it mean to rely on seemingly unknowable algorithms? What will the effects of trusting these technologies be on the future? Will it lead to societal changes we can’t comprehend or control?

Though these fears have been around for decades, the pace of change in recent years has turned somewhat latent concerns into full-blown anxiety for many. Visions of a hypothetical future with widespread unemployment resulting from mass automation can be particularly menacing, especially when you work for a large organization where efficiencies seem most likely to wreak havoc on jobs. But while there are serious implications to consider, fear too often overrides practical realities.

Human-centric AI

Automation can, should, and ultimately will be human-centric. A successful AI-powered future will require a focus on humans and machines operating in collaboration — rather than the human versus machine state so many fear — because it is in this additive partnership that we’ll see the real value of automation. When mundane tasks and repetitive busy work are handed off to machines, humans will be able to focus on the higher-level, creative, and socially responsible tasks they’re best suited for; they can give customers better experiences with faster service; and there will be an opening up of opportunities for meaningful work in the day-to-day lives of employees.

Right now, AI’s real-world potential is clouded by ambiguity. In part, that’s because of its relative newness (at least in a state where it can be applied to consumer-facing applications). There’s a lot of hype surrounding it, but that hype isn’t always accompanied by clearly stated facts.

Another reason is that, as technologies advance, there is a natural tendency to test the limits of where and how to apply them to practical applications. AI is no different, and this means it will be applied in situations where it doesn’t perform well. Things like replacing humans in critical decision-making or in the complex and nuanced tasks surrounding human administrative operations, for example. These are all current testing grounds, but none of these applications have proven to be the best or the most natural applications of machine learning— and many of them will likely be abandoned long-term in favour of more successful, practical applications like task automation.

Machine Learning is the most active area of AI and automation at present. It’s a process in which statistical methods allow a system to “learn” from data, and make decisions, without being explicitly programmed. Such systems pair an algorithm or series of steps for solving a problem with a knowledge base or stream of data, and this becomes the makeup of information that the algorithm uses to construct a model of the world and those it impacts.

We spoke with Matt Vasey, Senior Director of Artificial Intelligence at Microsoft, who describes augmentation through Machine Learning as capable of bringing about a powerful shift in workplace attitudes. “Algorithms and their implementations are growing into collaborative guides, expert references, and advisors. Machine Learning has enabled these assistants to improve every day, gleaning information from behavioural patterns and decision-making exhibited by employees in the workplace.”

Already the assistance these algorithms offer is proving an asset to knowledge workers. As they continue to improve and as more instances of ML and AI enter workplaces, we’ll begin to see even more meaningful insights surfaced that have the effect of supercharging an employee’s abilities to perform their job.

AI in the Workplace

The nature of work has changed tremendously over the last decade: It involves more creativity, more resilience, a greater reliance on soft skills, and a higher degree of perseverance in order to excel in many roles than ever before. And now, as we see the adoption of AI increase, the enterprise markets are shifting, too.

AI continues to raise the bar for talent, opening up new ways to generate revenue, and creating new job opportunities while introducing cost savings. Gartner projects that this shift will create up to $2.9 trillion of business value and 6.2 billion hours of worker productivity globally by 2021— numbers that predate the pandemic’s push for more automation in service sectors to help minimize physical contact. Those are not small numbers, and they tell us something important about the role of AI in shaping the global work landscape.

As noted earlier, there are arenas where automation should not take precedence over human action. Barbara Grosz, AI researcher and professor at Harvard’s John A. Paulson School of Engineering and Natural Science, identifies “domains that involve interpersonal connection, respect, affection and understanding” as the markers of those areas where computers should not be involved, “regardless of whether it appears they can.” Our own research at Myplanet on consumer comfort with new and emerging technologies has supported this. A robotic surgeon, which would be performing a precision task, ranked in the top half of all the technologies we surveyed; a robotic nurse, whose medical skills are deeply entwined with their interpersonal skills, ranked third to last.

But the inverse is true as well. There are areas and sets of skills where machines are simply better suited to the task. Things like the ability to analyze large amounts of data or as noted earlier, to undertake repetitive tasks. These tasks are robotic in nature and often compose the majority of workplace agonies for enterprise employees. Wouldn’t it be nice if we could automate some of the biggest pain points and make room for more esoteric, creative, and strategic thought?

Humans possess strong judgement, a capacity for improvisation based in intuition, and can detect nuance in far greater capacities than the average AI agent. And it’s this which informs a new model of partnership between humans and machines focused on the value of what machine automation and humans working together can bring, what we call augmented intelligence. We believe the enterprise adoption of AI will free humans from their repetitive or “robotic” duties, and allow them to truly be more human.

Automating and streamlining tasks shifts the course of employee journeys by allowing them to focus and invest more time in building solutions to the world’s most challenging problems. As it is, roles no longer possess a single focus and there is a greater reliance on hiring individuals with a multi-skill approach to their craft. This shift in how we understand what it means to perform a job will continue as enterprises incorporate more automation into their operations. New jobs will crop up where machines augment humans, as we’re already seeing, and another set of jobs will emerge where humans complement machines. There will be a new way of working enabled by AI.

Bringing AI and ML to Work

Businesses who don’t take advantage of AI — or do so without keeping employee satisfaction front-of-mind — are likely to fall far behind their competitors in creating lasting business value.

We believe that collaboration between humans and machines will lead to the reinvention of many traditional processes in the workplace. In office settings we’ll see the impact of things like automated voice assistants reducing the tedium of paperwork, data recall, note-taking, calendar and meeting management, and so much more, freeing up time for the more meaningful, more impactful work only a human can perform.

The promise of this kind of collaborative overlap is not just in the immediate streamlining of the work humans do in the workplace, but also in the additional efficiencies gained as the automated assistants learn the systems and habits of a workplace and its employees. This kind of assistive tech could act almost as an active participant in company activities, allowing individuals to spend less time searching for relevant information and more time discussing and strategizing around their contents.

This is the kind of workplace augmentation that will take hold and have real, lasting impacts on the future of work. There is no replacement of humans in this scenario, but a removal of barriers, blocks, and drag on a human worker’s ability to do the more meaningful parts of their job.

The Future of Work

The view that machines will replace humans is too simplistic. AI will undoubtedly play a core role in the ways most of us do our work in the future, but far from taking over human work, it will serve as an amplifier. AI will end up complementing labor in fields like medicine and law, extending human skills where real enhancements powered by data are possible, and will even create new roles as demand increases and new opportunities for additive functionality within those spheres come up.

As we see an influx of jobs created for humans complementing machines, there will be a set of jobs created to train, explain, and sustain AI. Individuals will be trained to oversee the quality of automated decisions, supervise the efficacy and safety of AI applications, and much more.

With the help of AI we’ve already seen examples of physical collaboration between people and robots in factories, and we’re about to see how human-machine collaboration will revolutionize experiences in offices and beyond.

AI/ML

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