Humans & AI: Why Collaborating with Machines Could Make Work More Meaningful

Author Avatar Oliver Bridge wrote this on Jan 20, 2020

 

Today, there are a variety of tasks that machines can perform better than humans from gaming to transcribing to data entry and software creation. This new expertise is also coming for finance, law, and medicine. 

There’s Google’s AI-based breast cancer detection, and an algorithmic cervical cancer screening developed by the National Cancer Institute. In the financial sector, AI now plays a role in underwriting and making credit decisions, and in the legal system, AI is interviewing clients and helping them complete their paperwork. And then, of course, there are the non-professional jobs like manual labor and clerical tasks that robots can easily take on. 

So, with all this in mind, it makes sense that workers across all sectors and job titles are getting worried about their futures. But maybe it isn’t all doom and gloom. The reality is, robots don’t represent the “end of work.” 

In many ways, our new “co-workers” stand to help us focus on the more meaningful aspects of work. Here, we’ll explore some of the reasons why working with robots could be changing work for the better.

 

The Promise of AI

AI technology stands to boost business productivity by an estimated 40%, according to Accenture. We’ll spend less time on work-related tasks that are, well, boring. Think of data-entry, follow-up emails, or anything involving a checklist and clipboard. 

New skills like storytelling, solution-based selling, creative problem-solving, and empathy are becoming differentiators, whereas AI can help us fill in our blind spots. For example, some of us are error-prone and, as a result, either work very slowly to get around the problem or submit sloppy reports. 

There’s also the issue of bias—which can play out in the form of poor hiring decisions, bad investments, and impede any real effort to create a more welcoming and diverse workspace. This graphic does a nice job breaking down the different ways that human thinking can distort data based on our emotions, personal values, and general outlook on life. 

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While productivity gains are an exciting prospect for businesses, Pew Research reports that 72% of employees live in fear of AI coming for their jobs. 

While some jobs may go to AI, others are better suited for human skills. McKinsey posted this discussion, with experts weighing in on the future of work. One of the key points made was that the ATM didn’t represent the end of bank tellers but it did change their role. Today, tellers are more involved in selling different types of financial products and adding more value to customers as opposed to only dispensing cash.

The Pew report does bring up some valid concerns from job loss to something perhaps a bit more sinister, data abuse and a loss of human agency. Still, they also bring up potential solutions like using AI to improve human collaboration across borders and developing policies to ensure AI is used to improve human well-being. 

Humans and AI: What Happens When We Team Up with Robots?

According to HBR, humans need to perform three critical roles to effectively collaborate with machines. They must train machines to perform tasks, explain the outcomes of those tasks, and prevent machines from doing harm to humans. 

Explaining Outcomes

Here’s the thing about AI—often it arrives at a conclusion without offering a clear picture of how it got there. Getting around the black box problem means that AI requires human experts to speak on their behalf, particularly in industries like medicine or law, where a practitioner needs to be able to communicate what the machine was able to diagnose and recommended as a treatment plan.

Preventing AI from Doing Harm

According to Accenture research, 92% of AI leaders currently train their teams in AI ethics and 74% say that close oversight over intelligent machines is essential. AI should be used to benefit its users, and as such, should be overseen by humans to ensure that it delivers the desired outcomes.

On the flip-side, machines also stand to help us improve our own capabilities. Here are a few examples of how that might play out in the real world. 

Enhance Our Creativity

While the AI itself isn’t so great at thinking creatively on its own, it can be applied to creative endeavors like art, music, or design. There’s Continuator, an AI “instrument” that learns your musical style and plays with you after memorizing patterns. Or, Microsoft’s Rembrandt project, which well, creates AI-generated images in the style of the old master and Google’s Deep Dream, which allows users to apply different algorithms to visual content.

While these examples are more exploratory or entertaining than practical, they do offer a glimpse into how we might use AI to enhance creative projects or better understand the mathematical side of art or music.  

Decision-Making

It’s important to understand that it’s not about bringing in more data, teams need to be smart about how they invest in AI to ensure that they capture the right insights. By some estimates, humans produce 2.5 quintillion bytes of data every day, though that number is increasing every day. 

Accenture found that AI practitioners, dubbed, Strategic Scalers, were better at managing their data and identifying useful information.

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Personalization

AI is responsible for individualized recommendations, on-demand and at scale. From Amazon's recommendations to streaming services like Spotify and Netflix, this application has long been familiar to the average consumer. However, as AI becomes more widespread, more organizations will have access to marketing automation and analytics tools. 

New Skills for Successful Collaboration with Robots

Soft skills, including critical thinking, problem-solving, creativity, communication, collaboration, and others are rising in demand, as noted in this infographic sourced from the Institute for the Future’s 2020 Skills report. 

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Here, we’ll run through those skills and why they’re becoming so important. 

  • Sense-Making—Sense-making is all about coming up with unique insights based on the information we’ve been given. Where computers can crush humans when it comes to speed, scale, and winning chess games, it’s our unique ability to give meaning to something—particularly new or ambiguous situations.  
  • Social Intelligence—Social intelligence is becoming increasingly important, as the workplace becomes more collaborative and distributed. It’s those workers who can anticipate the needs of others, build relationships, and communicate effectively that will maintain an edge against the robots.
  • New Media Literacy—New media literacy depends on a worker’s ability to produce content in a wide range of formats from podcasts and video, to blogs, social stories, and new communications tools. Here, success hinges not only on using new tools, but also knowing which tools to use in order to engage with audiences and persuade them to take action.
  • Computational Thinking—Computational thinking doesn’t mean you’ll need to add computer chips to your brain or anything. Rather, the term refers to the ability to translate massive data sets into insights and action. While AI will gather and sort through data sets, it’s up to humans to determine what’s important, what isn’t, and make sense out of patterns that emerge.
  • Design Mindset—A design mindset is the ability to develop tasks and workflows in a way that yields the desired outcome. Design thinking is all about continuously improving environments to change the brain or the behavior—and as such, this skill is valuable for developing better customer experience and more productive internal processes. 
  • Virtual Collaboration—With tools like Slack, email, social media, and newer entrants like VR conference calls, employees have more ways to stay connected than ever. That said, it can be difficult to balance productivity with being a present member of a virtual team.
  • Cognitive Load Management—Cognitive load management is all about maximizing cognitive function so that teams don’t become overloaded with notifications, data, and multiple systems. Those workers with techniques for separating which information is relevant or a top priority from information that gets in the way will have an advantage over more distracted peers.
  • Novel and Adaptive Thinking—Ability to come up with solutions and ideas on the fly. Things like writing a persuasive argument, responding to an unexpected crisis, and other abstract tasks will be high in-demand as automation and outsourcing continue to grow. 
  • Cross-Cultural Competency—Cross-cultural competency refers to a worker’s ability to operate in a range of cultural settings. Because the modern workplace is becoming increasingly global, workers need to be able to work with individuals from different backgrounds, and sometimes languages, and identify and communicate shared goals and how to accomplish them.

Wrapping Up

It’s worth pointing out that change, in general, is hard for a lot of us. And when this idea that robots are not only coming for our jobs but that they can do them better, is floating around, it’s hard not to get freaked out. 

Ultimately, it seems that yes, we’ll likely face some growing pains, but those of us that can learn to leverage our human qualities and collaborate with our algorithmic colleagues may well have a more meaningful career—with less time spent invoicing and reporting and more time spent coming up with new ideas.

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