Four Ways AI Can Augment Human Capabilities

By 2021, organizations worldwide will create nearly $3 trillion of business value via AI, Gartner says.

Jai Vijayan, Contributing Writer

September 5, 2019

5 Min Read
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Artificial Intelligence technologies might well replace humans in the workplace entirely someday. But at least for the foreseeable future, businesses will derive far more value using AI to augment and enhance existing capabilities than to automate away human jobs.

Analyst firm Gartner predicted in a recent report that by 2021 organizations worldwide will create $2.9 trillion of business value and some 6.2 billion hours of worker productivity by harnessing AI to support decision-making, improve efficiencies and to enable new applications.

Far from replacing jobs, companies will use AI in conjunction with humans to create more business value, says Svetlana Sicular, research vice president at Gartner. "There are many themes about AI taking away jobs," Sicular says. "In reality, the greatest benefits for enterprises will come from combining people and machines rather than delegating everything to the machine."

Sicular expects that as AI technology evolves, organizations will benefit the most from using it in one or more of the following four ways.

To pass the baton

One of the primary use cases for AI over the next few years will be to automate tasks, within broader processes, that are currently handled exclusively by humans. Organizations will divide many of their critical processes into a series of smaller tasks and see where they can benefit the most from automation and which tasks need to remain with humans, Sicular says.

The goal here won't be to displace people but to use AI to augment existing processes. One example is the use of AI in radiology within areas such as image recognition. Contrary to the broad misgivings about AI replacing radiologists, such use can actually help improve their workflows, integrate information better and ultimately lead to better patient care.

The pass the baton model works especially well in data-heavy situations, Sicular says. "AI reduces uncertainty where you have a lot of data and people reduce uncertainty when you don't have data," she says.

To take things up a level

AI can enable new opportunities for businesses by providing, for example, precise insights on whom to sell to, what to do or where to go next. "AI can find patterns that help you do new things," Sicular says. It can sift through millions and millions of data points and enable insights that would have been near impossible for organizations to get by doing things manually, she notes. "Take me to the next level is about 'tell me what I don't know'."

As one example Sicular points to a company that is using AI to help customers diagnose vehicle problems more quickly and accurately. The company has a database containing hundreds of millions of documents pertaining to vehicle telemetry, service records, manuals and other repair related data.

When it gets information about a new breakdown the firm is able to quickly pinpoint the five or six most likely reasons for the issue so service technicians can focus on them instead of spending time looking at other potential causes. "They can tell the technician the specific places to look at," she notes. "So, in essence AI is making everybody perform at the top level in areas where they are not that strong," Sicular notes.

Going the last mile

Getting to a very high degree of accuracy with data analysis can often be prohibitively expensive after a certain threshold. So some companies are using AI to do the initial groundwork and then letting human experts finish the rest of the work.

Online personal styling company Stitch Fix has taken this approach in delivering services to its customers, Sicular says. Stitch Fix offers a service where its stylists hand-select clothing for customers based on the customer's description and their unique size and style.

To do this, Stitch Fix uses an AI tool to go through its entire inventory and create a relatively narrow list of items matching the customer's choices. The company then uses its human stylists and designers to go through that list and handpick five items to send to the customer, sometimes with manual notes for an additional touch of personalization.

Theoretically, AI can make the final selection as well, but Stitch Fix is relying on humans to do a better job. "Stitch Fix has democratized capabilities for giving personal fashion advice," via its use of AI, Sicular says. The company is using AI to improve the accuracy of its initial choices and humans to finish the job.


Over the next few years expect to see organizations use AI to enable capabilities and applications that cannot exist without AI. Symbiosis is about using AI algorithms for a specific purpose such as to develop and test the form of a physical object like a building or a bridge, Sicular says.

The City of Hamburg, Germany's Elbphilharmonie concert hall is an example of the symbiotic use of AI. Algorithms were specifically developed to help design and test over 10,000 unique acoustic tiles before they were actually built and deployed inside the main concert hall. The algorithms were developed for a specific unique purpose and the tiles themselves would not exist without the algorithms reflecting the symbiotic nature of the use of AI in this case, Sicular says.

The pass-the-baton and go-the-last-mile scenario is where most activity is happening. But overall, AI adoption remains at a fairly early stage across organizations. As more companies begun examining use cases for AI their best options for deriving value from it is to combine it with human intelligence, she says.

For more on the roles of humans and AI check out these recent articles:

AI: Focus Turns to Ethical Standards for Intelligent Systems

It’s up to Smart Humans to Stop Being Stupid About AI

Why Affective Computing Systems Need Synthetic Emotion

Humans' Fascination with Artificial General Intelligence



About the Author(s)

Jai Vijayan

Contributing Writer

Jai Vijayan is a seasoned technology reporter with over 20 years of experience in IT trade journalism. He was most recently a Senior Editor at Computerworld, where he covered information security and data privacy issues for the publication. Over the course of his 20-year career at Computerworld, Jai also covered a variety of other technology topics including Big Data, Hadoop, Internet of Things, E-voting and data analytics. Prior to Computerworld, Jai covered technology issues for The Economic Times in Bangalore, India. Jai has a Master's degree in Statistics and lives in Naperville, IL.

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