Explained: How evolved human skills will lead us into the age of automation

Data changes and quickly proliferates. To stay ahead of the curve, businesses should set a target for the next 12 months to match their decision-making speed with the anticipated growth in data volumes. For instance, if companies expect a 30 percent annual growth in data over the next 12 months, their speed of deriving insights and applying data intelligence needs to accelerate by 30 percent in the same period. Anything less will impact the speed of doing business in this fast-changing world. “Winning with data-driven decision-making” has become the number one competitive game in nearly every industry. Increasingly, the human role will become more focused on what gets done with data-driven insights, which requires a renewed focus on decision-making and strategic thinking.

There is a significant jump in executives’ concern about trust and ethics (63 percent in 2016 versus 86 percent in 2020). Businesses need to focus on self-regulation by building ethics into AI applications and systems both at the design stage and after they launch and evolve. While there are no turnkey solutions for machine-risk management, an excellent first step is to appoint a role such as a machine risk officer or an algorithm bias auditor or amend an existing role to recognise and manage new risks and responsibilities related to machines.

As AI and automation take over more repetitive tasks, work demand will change team structures. Rather than larger hierarchical team structures, smaller teams will emerge that allow individuals and teams to become more fluid and flexible across roles and functions. Agile organisations emulate the speed, dynamism and customer-centricity that distinguish digitally native competitors, which can pivot as quickly as customer needs do.

For example, instead of concentrating technology professionals in a centralised IT department, leaders will embed software designers and engineers in independent teams, where they can be quickly deployed on high-priority goals. We must expect to see multidisciplinary teams emerge across functional departments.

Workforce skills will increasingly be tilted towards very human capabilities that validate the need for human-machine collaboration: decision-making, strategic thinking and learning. These skills are not only hard to find but also not easy to develop. While it’s relatively easy to train someone on how to follow a particular process, how does one teach empathy? One way to address this problem is by focusing more on fundamental attributes and behaviours than on skills. This can be achieved through role modelling, leveraging psychologists to conduct skills assessments, mentor and create a work culture in which human skills are prioritised and celebrated. What makes us human will make us employable in the future.

This new way of working, doing business and generating value will come at a cost. Cyber fraud, digital terrorism and winner-takes-all economy are the top three concerns raised by respondents around the impact of digital technologies on their personal and work lives. Cyber fraud is already on the rise with increased online behaviour; according to the United Nations, there has been a 350% rise in phishing websites in the first quarter of 2020. While it’s incumbent on businesses to prepare for the possibly darker side of the future of work, it’s also vital to recognise the positive impact of AI on the workforce and society.

The work ahead is all about striking a balance between machine-driven and human-centric activity. Even when machines can do everything, people will still be the ultimate X factor.

—* Click to read Cognizant study