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How can your humans benefit from digital assistants? In this article we explore whether businesses can live in harmony with (or, indeed, live without) robots.

According to McKinsey Global Institute, ‘While less than 5 percent of all occupations can be automated entirely using demonstrated technologies, about 60 percent of all occupations have at least 30 percent of constituent activities that could be automated.’ That suggests a lot of people are going to be forced out of work, but is that really true?

The Robots Are Coming—But is that Bad News?

Walk along any seafront on a breezy day and you’ll notice that, sometimes, the crashing wave you expect to come crashing down on the rocks just ebbs away and disappears into nothing. That happened on January 1st 2000 when many computing experts expected many mainframe computers to stop working. Or rather, it didn’t happen. It turned out the so-called millennium bug was more like a passing attack of the sneezes.

In 2021, post the inevitable distraction of the global pandemic, attention is going to naturally turn back to the impact of robots on the world of work—and whether they’re actually going to take over the world instead.


Not Everyone Agrees A.I. is a Good Idea

Some are FOR artificial intelligence. They include names like Jeff Bezos, Jay-Z, Bono, and others.

“Autonomous weapons are ‘genuinely scary,’ robots won’t put us all out of work.”—Jeff Bezos

On the other side of the argument, others fear the influence of A.I. (or at least in the ability of humans to manage its role). The notables on this list include Elon Musk, Mark Zuckerberg, Stephen Hawking, and Gary Marcus.

“A.I. is a fundamental existential risk for human civilization, and I don’t think people fully appreciate that.”—Mark Zuckerberg

Others sit comfortably on the fence. They include Bill Gates who says the rise in artificial intelligence will mean society will be able to produce a lot more goods and services with less labor. And overwhelmingly, over the last several hundred years, that has been great for society.’ So, he argues, ‘A.I. is a very very good thing used in an enlightened way.’ But Gates also warns, ‘First the machines will do a lot of jobs for us and not be super intelligent. That should be positive if we manage it well. A few decades after that though the intelligence is strong enough to be a concern.”—Bill Gates

Look beyond the opinions of celebrities and even the man in the street isn’t sure how this is all going to pan out.

The University of Oxford’s Future of Humanity Institute ran a 2019 survey on the perspectives of U.S. citizens and found forty-one percent of respondents said they somewhat or strongly supported the development of A.I., while 22 percent said they somewhat or strongly opposed it. The remaining 28 percent said they had no strong feelings one way or the other.

The perspectives of business people tend to side with A.I. as being ‘useful’ if not a force for good. When MIT Sloan Management Review and BCG Henderson Institute surveyed 3,000 global executives, 85% believed that A.I. will allow their companies to “obtain or sustain a competitive advantage.”

With pundits then on both sides of the argument, and some in between, expecting something miraculous to happen in the next few years in the realm of Artificial Intelligence and an era of machines is ‘reasonable.’

The good news is that early examples of A.I. and machine-learning are proving how useful “computer systems that perform tasks or make decisions that usually require human intelligence” are to the workplace, and workers. They suggest that software robots are finding roles not as ‘bosses,’ but as ‘willing workers’ comfortable working as sub-ordinates to busy administrators, customer service supervisors, and Line-of-Business managers.


Your New Personal Assistant

Machine-learning and A.I. is being used today to enable humans to do more, with less resource.

  • It means IT Data Security professionals don’t need to monitor every curious log-in activity; only the exceptionally curious ones.
  • Analysts can concentrate their minds on what data is telling them, not what data exists and how it should be presented to tell a story.
  • Researchers can focus their minds on a larger gamut of qualitative and quantitative data to develop more accurate predictions.
  • Programmers and coders can entrust simple coding tasks to their computer assistants, so they can concentrate their efforts on ‘value-added coding’ that brings more value to stakeholders.

Using A.I. software robots opens doors to re-designing how organizations are designed with one potential future view suggesting that one human could be looking after a dozen conversational A.I. chatbots and handling exceptions, one scientist could be managing a lab manned by robots performing the tests. Or one researcher could have oversight over hundreds of A.I. driven research robots managing survey design and deployment.

Not everyone agrees. Some believe that A.I. robots are more likely to become our bosses than our subordinates. As the New York Times reported in June 2019, ‘A Machine May Not Take Your Job, but One Could Become Your Boss.’ A.I. technology is good at learning the best outcomes of an activity, to then monitor and coach humans on how best to behave to achieve it.

One example highlighted in the NYTimes article is Cogito, an A.I. program used in call centers and other workplaces. When a customer service representative in the call center of the insurance giant MetLife talks to a customer over the phone, they keep one eye on the bottom-right corner of the screen—because that’s where a little blue box driven by A.I. tells them how they’re doing. Talking too fast? The program flashes an icon of a speedometer, indicating to the representative they should slow down. Sound sleepy? The software displays an “energy cue,” with a picture of a coffee cup. Not empathetic enough? A heart icon pops up.


What it Means for the World of Work

A.I. technologies tune and tweak all sorts of activities in business; from research and experiments, to customer service experience and market grow projections—and everything else in between.

Analysis by McKinsey & Co. of more than 400 use cases across 19 industries and nine business functions found that A.I. improved on traditional analytics techniques in 69 percent of potential use cases (Exhibit 1). In only 16 percent of A.I. use cases did we find a “greenfield” A.I. solution that was applicable where other analytics methods would not be effective.

“Once computers can effectively reprogram themselves, and successively improve themselves, leading to a so-called technological singularity or intelligence explosion the risks of machines outwitting humans in battles for resources and self-preservation cannot simply be dismissed.”—Gary Markus

Whether you think TERMINATORS are just around the corner, or that it doesn’t matter because we’ve got Ironman superpowers thanks to A.I., the fact is that Artificially Intelligent software robots are on the way. Back in 2019, PricewaterhouseCoopers (PwC) said they expected 20% of companies to deploy A.I. enterprise-wide—that’s one in five companies planning to use A.I. in some form to improve their products and services, fine-tune their customer experience and operating cut costs. In 2020, they reported 18% actually did and that just 4% were looking to do the same in 2020. But 2021, is a New Year, and the latest research from analysts including IDC and Forrester points to a significant gearshift to adopt A.I. faster given the rising level of competition.


Looking Beyond the Workplace, A.I. Will Create New Types of Businesses

Don’t imagine a world where A.I. fits snugly into the way companies and markets work today and serves a role in automating mundane tasks for humans. That may be a perspective on the world that some Robotic Process Automation (RPA) vendors project, but a very different kind of future awaits us.

A.I. is a game-changing technology and it allows entrepreneurs to re-imagine how services can be designed and delivered.

Think about:

  • Insurance cover offers being served up AS you step up to a hazardous situation, or when a storm turns up in your district.
  • Smart fridges that recommend your diet and meal plan for the next month.
  • Fine-grained learning and education courses that allow you to learn about whatever it is your looking at, or the subject of the book you’re reading.
  • Shopping applications that inform you in real-time where you can get that nifty pair of shoes that girl is wearing.

A.I. is here, and it’s changing the world.



I can tell you that the speed of light is 299 792 458 m/s but I don’t have near enough knowledge to tell you why. Einstein sA.I.d, “If you can’t explA.I.n it simply, you don’t understand it well enough.” What he didn’t realize is that, by making this point, he was defining the greatest challenge on A.I. adoption.

Humans aren’t great at understanding paradigms. We like to learn things in our own time and compartmentalise our learning into boxes that we can stick neat labels on. Something like A.I. disrupts our understanding of how things work. It creates not one, but many new paradigms we can’t get our heads around.

When Intel invented what was arguably the first mobile device in the 1970’s (a thing that to deliver pretty much everything a smartphone does today, the fact they didn’t call it a phone (and folk couldn’t understand why they needed it) was a fundamental flaw in their ideation process. This is why—some people say—Thomas Edison made the first lightbulb flicker like a Gaslight flame. In the future, we won’t have mobile phones, because we don’t need a hardware device to access the features found on a mobile computing device today, when we’ve got 10g technology that passes data to the invisible ‘cloud computer’ as quickly as we can think. All we need is a way to identify ourselves to the computer (remember the lapel ‘communicators’ on Star Trek?).

The ‘black box’ complexity of deep learning techniques creates the challenge of ‘explA.I.nability,’ or showing which factors led to a decision or prediction, and how. Without simple ways to explA.I.n the role and decisioning of A.I. engines, mere humans are going to struggle to trust it, rely on it, or embrace it.

Ian Tomlin is a management consultant and writer on the subject of enterprise computing and organizational design.  He serves on the USTECH GLOBAL EMEA Management Team.  Ian has written several books on the subject of digital transformation, cloud computing, social operating systems, codeless applications development, business intelligence, data science, office security, customer data platforms, vendor management systems, Managed Service Provisioning (MSP), customer experience, and organizational design.  He can be reached via LinkedIn or Twitter.

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