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Amid declining productivity, Amazon CEO Andy Jassy called all of the company’s employees back to the office earlier this yr. Butts in seats, five days a week.
But will RTO solve the productivity problem? Honest answer: who knows?
Productivity is a seemingly easy concept that seems to be very shaky in practice. What does it even mean to be productive? Is this a feature of logged hours? Emails sent? Sales accomplished? Are customers satisfied? Every boss seems to have their own definition.
It’s no wonder that “productivity anxiety” is reaching impressive heights eight out of ten employees worries that they are not doing enough.
This uncertainty is coupled with a “business performance erosion crisis” that is affecting corporations around the world. see productivity plateau.
The real problem: we’re measuring productivity unsuitable. Actually, tackling this requires doing something as obvious because it is elusive: finding a way to truly connect people with business outcomes.
Here’s why productivity is so hard to measure and how corporations can start measuring it in a more meaningful way.
Unpacking productivity
For business experts and corporate leaders, productivity has long been an obsession. Already in the late 18th century, economist Adam Smith made the distinction productive and non-productive work. At the starting of the twentieth century it appeared increase in the variety of efficiency experts who claimed to help corporations get the most out of their employees.
Around the same time, Henry Ford concluded that they were best during insertion eight hours a day — preparing the ground for a 40-hour work week. In the Nineteen Eighties, productivity became a pseudoscience, courtesy of gurus like Tom Peters and Michael Porter.
Despite all these advances, the basic concept of productivity stays stubbornly opaque and unhelpful. In the boardroom, it often comes down to dividing results by inputs (for example, total sales divided by hours worked). But using such a broad brush only gets us so far.
At the individual worker level, corporations still focus on measuring effort—tracking employees by hours worked or recorded performance. For a customer support representative, productivity may be equivalent to the variety of calls they handle each day.
This doesn’t really tell us much. What’s really needed is a focus on how each person impacts real business outcomes. For our specialist, customer retention is a much more useful measure of productivity than calls handled. But tracing the tenuous link between a friendly conversation and a customer renewal is easier said than done.
A better way to measure productivity
So how can you better manage your productivity and ease the anxiety around it?
This is where artificial intelligence and recent technology are proving effective in unraveling the subtle connections between what employees do and the way it impacts company performance.
Essentially, it involves combining different data sources in recent and insightful ways. For example, corporations have long had access to detailed “personal data” about their employees – from training and skilled certifications to seniority and performance evaluations. At the same time, digital sales and marketing tools have given corporations access to a wealthy set of knowledge on customer purchasing and behavior.
Historically, these data streams have been siloed. But recent tools are combining them and providing unexpected insights. Take the example of Cartier, a luxury retailer with a whole bunch of stores around the world.
By integrating people data with point-of-sale data, they were able to see which locations were performing better than others, in addition to the training history of each store manager. Accurate knowledge of each manager’s productivity enabled the company to determine which sales training worked best and apply it where mandatory to increase productivity.
Meanwhile, incorporating natural language processing into AI-based workplace tools is also proving to be a productivity breakthrough. Information that was once available only to analysts and number crunchers can now be accessed by team leaders who need it most.
Let’s assume that a company’s sales in a particular region are declining. Instead of delving into dense spreadsheets, leaders can now ask questions in plain language: Why is this happening? Why are our sales so disappointing?
The answer – derived by AI from a number of company data sources – helps get to the root cause. In the above example, you may find that churn is very high. Because the entire sales team changes every six months, reps do not stay with the company long enough to learn the way to sell the product. The real problem wasn’t the reps – it was their manager.
Cultural change
Despite the potential of AI, technology is only a part of the solution to the productivity dilemma. Old-fashioned management still matters, which incorporates setting clear goals up front. For more than nine out of 10 employees, it is vital to have a job that offers you a sense of meaning. They need to be able to answer the basic query: Am I working on something that matters?
This is where having clear goals and key results (OKRs) – that spread from leaders to individual teams and members – can make a difference. Over 80% corporations consider that OKRs have a positive impact on their organization. And if teams have processes in place to discover the highest priority work, then that is exactly what happens almost five times they are more likely to be effective and productive than their peers who do not.
Ultimately, using the latest tools to measure productivity by connecting people with business results advantages corporations and their teams. By setting meaningful goals and tracking worker impact, corporations gain actionable insight into how people produce results. And because teams know what is expected of them and where they stand, they are less anxious about their contributions. When it comes to productivity, it is time (and money) well spent.