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With performance slides, Amazon CEO Andy Jassy called all the employees of the corporation back in the office earlier this year. Back in the seat, five days a week.
But you will RTO solve productivity? The honest answer: Who knows?
Productivity is a seemingly simple concept that turns out to be very slippery in practice. What does it mean to be productive, anyway? Is it a function of hours logged? Emails sent? Sales made? Are customers satisfied? Every boss seems to have their own definition.
No wonder “productivity anxiety” is reaching epic proportions, with eight out of 10 workers worried that they are not doing enough.
This uncertainty is associated with a “The business performance erosion crisis“as a company everywhere see productivity plateau.
The real problem: we're measuring productivity the wrong way. In fact, getting a handle on it requires doing something as obvious as it is elusive: finding a way to truly connect people to business results.
Here's why productivity is so hard to define—and how companies can start measuring it in a more meaningful way.
Unpacking productivity
For business experts and corporate executives, productivity has long been an obsession. In the late 1700s, the economist Adam Smith distinguished between them productive and unproductive work. The beginning of the 20th century saw increasing efficiency experts who claimed to help companies get the most out of their workers.
At the same time, Henry Ford concluded that they were more productive when they decided eight hours a day — setting the stage for the 40-hour work week. By the 1980s, productivity had become a pseudo-science, courtesy of gurus like Tom Peters and Michael Porter.
Despite all these advances, the basic notion of productivity has remained stubbornly obscure and useless. In the boardroom, it often boils down to outputs divided by inputs (total sales, for example, divided by hours worked). But using such a broad brush only gets us so far.
At the individual employee level, companies still tend to fixate on measuring effort—tracking employees by hours worked or results recorded. For an employee working in customer support, productivity may correspond to the number of calls they handle each day.
In fact, it tells us very little. What is really needed is a focus on how each individual affects actual business results. For our support person, customer retention is a much more useful measure of productivity than calls made. But tracking the tenuous link between a friendly phone call and a customer renewal is easier said than done.
A better way to measure productivity
So how do we better address productivity and relieves anxiety around him?
This is where artificial intelligence and new technology are proving capable of unraveling the subtle connections between what employees do and how it affects them. company performance.
Essentially, this involves combining different data sources in new and revealing ways. Companies have long had access to detailed “people data” about their employees, for example—everything from training and professional certifications to attitude and performance evaluations. At the same time, digital sales and marketing tools have given companies access to a rich set of data on customer purchases and behavior.
Historically, those data streams were isolated. But new tools are bringing them together and yielding unexpected insights. Take an example from Cartier, the luxury retailer with hundreds of stores around the globe.
By integrating people data with point-of-sale data, they were able to see which locations performed better than others, along with the training history of each store manager. Knowing exactly how productive each manager enables the company to determine what sales training worked best – and apply it where necessary to increase performance.
Related: 7 Traits of Extremely Productive Employees
Meanwhile, the incorporation of natural language processing into AI-powered workplace tools is also proving a game-changer for productivity. The kinds of insights that were once limited to analysts and number crunchers can now be accessed by the team leaders who need them most.
Let's say that a company's sales in a certain region are falling. Instead of diving into dense spreadsheets, leaders can now ask questions in plain language: Why is this happening? Why are our sales so disappointing?
The answer – revealed by AI from company-wide data sources – helps to find the root cause. In the example above, it may turn out that the duplication is too high. Because the entire sales team turns over every six months, reps don't stay long enough to learn how to sell the product. The real problem wasn't with the reps – it was with their manager.
Related: I'm a CEO, founder and father of 2 – Here are 3 practices that help me maintain my health.
A cultural shift
Despite AI's potential, the technology is only part of the solution to the productivity dilemma. Old-fashioned management still matters, and that includes setting clear goals from the top. ABOUT more than nine out of 10 workers, it is important to have a job that feels meaningful. They must be able to answer the basic question: Am I working on something that matters?
This is where having transparent objectives and key results (OKRs) – which are disseminated from leaders to teams and individual members – can make a difference. more than 80% of companies believe that OKRs have a positive impact on their organization. And when teams have processes to identify high-priority work, they are almost five times more likely to be effective and productive than colleagues who do not.
Ultimately, using the latest tools to measure productivity by connecting people to business results is a win-win for companies and their teams. By setting goals that matter and tracking employee impact, businesses gain actionable insights into how people drive results. And because teams know what is expected of them and where they stand, they feel less anxious about their contribution. When it comes to productivity, that's time (and money) well spent.