Want Your Workers to Be More Productive? You Need a Better Way to Measure Their Contributions

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With performance slipping, Amazon CEO Andy Jassy called all corporate employees back to the office earlier this year. Butts in seats, five days a week.

But will RTO solve productivity? The honest answer: Who knows?

Productivity is a seemingly simple concept that proves notoriously 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 they aren’t doing enough.

That uncertainty is coupled with a “business performance erosion crisis” as companies everywhere see productivity plateau.

The real problem: We’re measuring productivity the wrong way. Actually getting a handle on it requires doing something as obvious as it is elusive: finding a way to truly connect people with business outcomes.

Here’s why productivity is so hard to pin down — and how companies can begin to measure it in a more meaningful way.

Unpacking productivity

For business experts and corporate leaders, productivity has long been an obsession. Back in the late 1700s, economist Adam Smith distinguished between productive and unproductive labor. The early 20th century saw the rise of efficiency experts who claimed to help companies get the most out of their workers.

Around the same time, Henry Ford concluded they were most productive when putting in eight hours a day — setting the stage for the 40-hour workweek. By the 1980s, productivity had become a pseudo-science, courtesy of gurus like Tom Peters and Michael Porter.

Despite all of those advances, the basic notion of productivity has remained stubbornly opaque and unhelpful. In the boardroom, it’s often reduced to outputs divided by inputs (total sales, for instance, divided by hours worked). But using such a broad brush only gets us so far.

At the individual worker level, companies still tend to fixate on measuring effort — tracking employees by hours worked or deliverables logged. For an employee who works in customer support, productivity might correspond to the number of calls they handle each day.

In fact, that tells us very little. What’s really needed is a focus on how each individual impacts actual business outcomes. For our support person, customer retention is a much more useful measure of productivity than calls handled. But, tracing the tenuous connection between a friendly call and a customer renewal is easier said than done.

A better way to measure productivity

So, how do we better handle productivity and alleviate anxiety around it?

Here’s where AI and new technology is proving adept at untangling the subtle links between what employees do and how that impacts company performance.

At its heart, this involves combining disparate data sources in new and revealing ways. Companies have long had access to detailed “people data” on their employees, for example — everything from training and professional certifications to tenure and performance ratings. At the same time, digital sales and marketing tools have given companies access to a rich data set on purchases and customer behavior.

Historically, those data streams were siloed. 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 perform better than others, along with each store manager’s training history. Knowing exactly how productive each manager is enabled the company to determine which sales training worked best — and apply it where necessary to boost performance.

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 confined to analysts and number crunchers can now be accessed by the team leaders who need them the most.

Let’s say a company’s sales in a particular region are plunging. Instead of diving into dense spreadsheets, leaders can now pose questions in plain language: Why is this happening? Why are our sales so disappointing?

The answer — surfaced by AI from wide-ranging company data sources — helps get at the root cause. In the example above, it might turn out that churn is very high. Because the entire sales team turns over every six months, reps don’t stick around long enough to learn how to sell the product. The real problem wasn’t with the reps — it was with their manager.

A cultural shift

Despite the potential of AI, 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. For more than nine out of 10 workers, it’s important to have a job that feels meaningful. They need to be able to answer the fundamental question: Am I working on something that matters?

Here’s where having transparent objectives and key results (OKRs) — which propagate down from leaders to individual teams and members — can be a difference maker. More than 80% of companies believe OKRs have a positive impact on their organization. And when teams have processes to identify top-priority work, they’re almost five times more likely to be effective and productive than peers that don’t.

Ultimately, using the latest tools to measure productivity by connecting people with business outcomes is a win-win for companies and their teams. By setting goals that matter and tracking employees’ impact, businesses gain actionable insights into how people drive results. And because teams know what’s 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.

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