Cognitive Automation is the Immediate Future of Team Management. For all the anticipation of increased automation at work, commentators have spent a lot of energy trying to convince people it can only handle easy, repetitive processes.

It’s time to finally confront the truth: Per the McKinsey Global Institute, today’s robots can handle up to a quarter of the average CEO’s job and 35% of management tasks.

While robotic process automation refers to using robots to speed up concrete processes, cognitive automation takes a more advanced version of the same underlying tool set and applies it to more conceptual, judgment-based tasks — what we now call “knowledge work.”

Using specific AI techniques that approximate the way our brains work, cognitive automation helps us make better decisions, complete tasks faster, and meet goals more easily — and it’s swiftly gaining traction. 

KPMG predicts spending on intelligent automation will hit $232 billion by 2025, up from $12.4 billion in 2018.

Of course, we’re a long way off from managerial jobs being fully automated, but these findings indicate that automation can — and should — play a bigger role in how we lead the 21st-century workforce.

Where Cognitive Automation Fits Into the Workforce

At Exela Technologies, our managers wouldn’t be able to support our global workforce of more than 22,000 employees without the help of cognitive automation. Among other things, this technology enables us to obtain information from scattered sources, conduct deep analysis, and collaborate more easily.

We’re not the only ones, either. Deloitte found that increased reliance on cognitive automation in the insurance industry improved firms’ recruitment and development processes, removing much of the heavy-lifting that human managers once performed.

Business leadership has a lot to gain from cognitive automation. Here are some ways managers can take advantage of it.

1. Capture and dissect data.

Intelligent systems can gather more data than manual processes, then analyze that data more effectively to uncover trends, detect anomalies, and produce predictive models.

One sector where we see this technology emerging rapidly is healthcare. AI technology can now compare a patient’s medical history with established guidelines for common illnesses to help identify gaps in care and specific opportunities for improved treatment. When done by a human, this analysis could take hours. When done by a machine, it takes seconds.

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