The New Data-Centric Workforce

The value of AI and real time feedback is profound, but so is a workforce that feels a sense of responsibility, individualism, and respect.

The Balancing Act

These days you can’t hide from AI or algorithms, and most of us wouldn’t want to. By using AI to analyze mountains of data, streaming platforms introduce us to our favorite new music, our GPS helps us avoid bumper-to-bumper traffic, and our social media feeds are filled with content we have a hard time looking away from.

However, as companies have embraced the power of algorithms to save money and time, using them for everything from hiring (and firing) to project management to promotions and more, employees have become reduced to data points in an equation, instead of the valuable individuals they are. This troubling trend not only risks sinking employee morale, but could cost companies big in the long term.

We’ve Been Here Before

We may think of this data-centric approach as modern, but “Scientific Management,” as it was once known, was pioneered by Frederick Taylor in the early 1900s. Taylor’s theory was that there was one best way to perform every task, from the production line all the way to the front office, and if you just made your employees perform in the “best” way, you’d save time and money across the board. Sound familiar?

While the practice held sway for decades, by the 1930s employers began to see problems with this approach. By giving them only a rote task, essentially turning their actions into data points, companies realized employees were holding back effort and had no incentive to innovate or contribute to the company as a whole. By emphasizing relationships with other employees, letting them know their work mattered, and by letting them be involved in decision making, workers’ performance skyrocketed, as did the welfare of the companies that abandoned Scientific Management.

Enter “Lean Production Methods.

Put into practice in the late 1970s by Toyota, this new concept’s main component was granting frontline employees the ability to improve quality and productivity as they saw fit. The employees doing the work — not quality inspectors at the end of the production line — were put in charge of finding problems and given the authority to fix them. Line employees were even given the power to stop the production lines if they felt it necessary. This investment and involvement in every employee helped catapult Toyota to global player status and is the reason their cars are so famously reliable today.

Learning from Toyota’s global success, over the next four decades Lean Production Methods spread across every industry and Scientific Management was essentially cast aside as a relic of another time. But then came the computers, and the algorithms we love so well.

Rise of the Machines

Scientific Management is back in a big way, except this time, instead of being created by engineers looking to optimize every task, it’s being created by AI to optimize everything.

The reason for this swing back is simple: numbers don’t lie (except when they do, but we’ll get back to that later). Thanks to advancements in computational power and AI, those algorithms that spice up your playlist can also be used to micro-manage tasks, break down assignments into measurable pieces and track them in real time. That means every second of an employee’s day can be dictated by software that maximizes productivity and eliminates waste.

Think of an Amazon delivery driver. Their package allotment and routes are determined by an algorithm, their driving is monitored to ensure they’re being safe, and their drop-offs are scanned to ensure all their deliveries arrive on-time.

This same minute-by-minute breakdown of the working day has spread across every industry, and clearly, offering drivers a better route and helping them use less fuel to save money isn’t inherently bad. Using AI and algorithms to intelligently analyze tasks and data is obviously beneficial, which is why it’s been so fully embraced, but when taken too far, this approach removes autonomy and accountability from the hands of employees. It turns people into data points and kills the employee driven innovation that often saves businesses money in the long run. This lack of long term thinking is at its most egregious in the gig economy, ground zero for algorithmic management.

Gig ‘Em

One big problem that companies face is that while demand is variable, the workforce has long been fixed. Algorithms seem to have the solution — why pay workers when they aren’t always needed? This seems to be an upfront cost that AI can help immediately cut. Instead of hiring new employees, companies can contract out what they need, and pocket the savings.

Uber, Amazon, and Google are huge proponents of the gig system. In fact, in 2020, Google had 130,000-plus contract and temporary workers compared to 123,000 full-time employees. The upfront savings seem to make this a smart solution, but gig workers create long-term costs that need to be looked at more closely.

First, there is the bureaucratic cost of tracking massive numbers of contracts. Second, there are legal costs, lack of uniformity, minimal employee innovation and issues with variable pay. Beyond that, cutting employees in times of decreased demand doesn’t generally have positive financial benefits in the long-term. Studies show that businesses that hold off on cuts during recession fare better than those who try to quickly trim the fat.

Helping Employees Work Smarter

It is easy to get caught up in the movement toward a data centric workforce that treats employees as widgets and their actions as data points, but it is in every company’s best-interest to view employees as people instead of commodities. The value of AI and real time feedback is profound, but so is a workforce that feels a sense of responsibility, individualism, and respect.

History shows that borrowing principles from both engineering and behavioral ideologies will produce maximum returns in task design, productivity, cost savings, and innovation.

Both ideologies have their advantages, but using either one without regard for the other is a display of poorly informed management. Utilizing AI to intelligently design tasks and analyze data is crucial. Likewise, educating employees on how and why they are performing those tasks is also important. Valuing feedback, employee innovation, and collaboration is critical to engineered management.

The best path is to use the algorithms intelligently and ensure you offer a space where employees feel they are valued and understood. The office is crucial for creating an environment where employees feel happy and comfortable. Going the extra mile by providing a well-appointed office space can encourage collaboration, focus, and a sense of value that drives individuals, and companies, to grow beyond what a computer thinks is possible.