Augmenting employee performance with AI and other technologies

Forrester examines the productivity paradox and how technology will impact the workforce.

By Forrester Research and J.P. Gownder | March 6, 2018

One of today’s biggest opportunities for IT to make an impact is by automating business processes, manufacturing, repetitive tasks, and more. We delve into examples and best practices.

I recently researched a problem that’s vexing CIOs and government leaders alike. In aggregate, US companies face a productivity paradox: Despite billions of dollars invested in technology, growth in employee productivity has slowed since 2004. Even though global technology spending will for the first time pass $3 trillion globally in 2018, this productivity paradox should concern CIOs and other decision-makers: For all these investments, shouldn’t we expect a return in the form of more effective employees?

As it turns out, not all those technology investments make their way down to employees — at least not effectively. While employees commonly have devices — six out of 10 information workers have a PC, and the same proportion have a smartphone — too few of them have adequate apps beyond just the basics: Forrester’s Business Technographics data shows that fewer than one in 10 have job-specific applications they use daily. Emerging technology time-savers like voice applications enjoy moribund usage, despite the recent boom in Amazon Alexa applications. Instead, employees use just the basics, like email and calendars, on their devices.

But the problem runs deeper than apps or voice control. Tectonic shifts are underway in our economy and how we work. Forrester forecasts a world in which automation cannibalizes 17 percent of US jobs by 2027, partly offset by the growth of 10 percent new jobs from the automation economy. A transformed workforce, comprised of human-machine teams, is already emerging. More and more human employees find themselves working side-by-side with robotic colleagues — today.

What’s required to succeed in this new world is, in part, a new way of thinking. CIOs and other leaders must think about how a given technology will help an employee solve problems across three possible dimensions:

Decision Context: First, design experiences that provide information to help employees act. Making informed, timely decisions is difficult. Done right, technology can help a great deal. Increasingly, smart software can offer next-best action advice across the employee journey.

Execution Support: A second level of augmentation occurs when machines take on part of the workload. In the 1960s, a budget director spent a lot of time calculating a budget by hand. Today, Microsoft Excel or financial software takes that arduous task off their plate.

Human-managed Machines: Finally, with machines taking over entire workflows, some humans are becoming robot-managers. As robotics process automation (RPA) deployments proliferate, humans who formerly did routine work are being promoted to overseeing exception-handling and process improvement, training the bots to be even more effective.

Once you’ve determined the appropriate context for augmenting an employee’s activities with technology, you face a new challenge: An ever-increasing panoply of emerging technologies that can help solve previously intractable problems. Making decisions about these technologies requires you to ask numerous questions and to perform a proof of concept trial. How mature is this new technology, and have other companies succeeded with it? How will it impact the employee and customer experiences? What’s the total economic impact and business case of deployment?

Augmenting workers — more frequently, with more sophisticated tools — will require an upgrade in employees’ skills. RQ (robotics quotient) will join IQ (intelligence quotient) and EQ (emotional quotient) in the lexicon of human resources, business, and technology leaders everywhere. That’s because RQ measures the skill set that human workers have when working with automated and semiautonomous systems; people who are adaptable, flexible, pick up new smart tools quickly, and who can problem solve have higher RQ. Companies with high RQ among their staffs will create more top-line revenue and profits than those with lower RQ ratings. In many cases, these organizations will need to invest in training and learning and development efforts to raise RQ, even if they don’t know it yet.