Defining and Measuring Employee Productivity and Efficiency in the AI Age
Productivity and efficiency are often confused. This confusion is justifiable—they’re closely related and important to your business. While the focus has been on measuring productivity in the past five years, efficiency gains are returning to the forefront as AI makes larger strides into the workplace.
Definitions and Examples of Employee Productivity and Efficiency
What is Employee Productivity? An Output-based Measurement
According to the Harvard Business Review, productivity refers to the ratio of output of goods and/or services to labor hours needed to complete that output. So, the more labor hours and time required, the lower the productivity. It is about doing more with the same.
Case Study: Manual to Robotic Production in the Automotive Sector
Replacing human workers with robots improved productivity in the automotive industry by 16% between 2010 and 2016. This relates to productivity because changing the manufacturing process increases the number of goods manufactured using the same labor hours.
What is Employee Efficiency? A Time-based Measurement
On the other hand, efficiency is the set number of hours required to complete a specific task or project or to produce a specific good. Improving efficiency refers to reducing the time that goes into the final product. It is about doing the same with less.
Case Study: Automating With AI
Elsa had to clean up documents manually to produce briefing documents, with repetitive tasks such as adding bullet points and reformatting text. With standard AI tools, she can ask the tool to do these tasks for her, significantly reducing the time it takes to produce her briefing documents.
Types of Efficiency
Due to the number of systems developed to improve efficiency, there are several types of efficiencies and methods for measuring them. They include:
- Technical Efficiency: Measures a firm's success in obtaining maximal output from a given set of inputs.
- Allocative Efficiency: Reflects a firm’s success in using the optimal input mix given input prices or producing the optimal output mix given output prices.
- Economic Efficiency: Encompasses both technical and allocative efficiency.
- Cost Efficiency (CE): Ratio of minimum cost to actual cost.
- Revenue Efficiency (RE): Ratio of maximum revenue to actual revenue.
- Profit Efficiency (πE): Ratio of maximum profit to actual profit.
Why are Productivity and Efficiency Used Interchangeably?
Words matter when it comes to using these two terms. Efficiency can be defined as a component of productivity since it is part of the equation determining the productivity ratio. It is correct to refer to efficiency as a component of productivity but not as a synonym for or a component of efficiency. Think of productivity as an umbrella term with efficiency nested underneath it.
The Historical and Current Significance of Employee Productivity vs. Efficiency
Leading global companies have fine-tuned efficiency through processes like Six Sigma, lean manufacturing, agile systems, and other methods. They refined them so well that efficiency gains peaked in the 2000s.
Between the end of Q2 2015 and 2017, top-line earnings growth fell, signaling that companies had done all they could to tweak efficiency. This was the inflection point when productivity measurement began to take over as a more actionable metric. Solutions like our employee productivity monitoring solution, Prodoscore, began gaining popularity.
By March 2020, solutions, including Prodoscore, were in place to measure productivity, and suddenly, the world required this data to ensure that, for example, remote workers were continuing to produce the same level of output they had in the office. This is when productivity measurement went from a nice-to-have to a must-have in every C-suite.
How AI has Resurrected Interest in Efficiency Measurement
AI has been credited with beginning an Industrial Revolution equivalent to the invention of the computer and the internet. Even those two technologies did not show as much promise in their early days as AI has. AI, or the promise of what it can do, has eliminated many low-level tasks and jobs and has resurrected attention to efficiency gains. As a massive time-saver, it can improve efficiency for nearly every role and lower-level task in business.
How to Effectively Measure Employee Productivity and Efficiency
In this guide, we have extensively covered how to measure productivity, so we’ll focus more here on measuring efficiency. A few topline notes from our guide include:
- Use three sets of productivity metrics: organizational, departmental, and individual
- Build in a “quality” bell curve to your data to account for extra time needed to get the job done well
- Use Prodoscore to get nearly all of the metrics you need for data-driven decision-making
Efficiency formulas are time-based measurements. This is the standard formula: Efficiency = Output Hours ÷ Input Hours. To express it as a percentage, multiply by 100.
Things aren’t quite that simple in the working world. First, you have to develop a fixed number of output hours. Take a sales presentation as an example. To get this number, you could ask your sales reps to time how long it takes them to write a sales presentation; the average of that number will be output hours.
Then, the equation becomes relatively simple as you time input hours for each sales presentation after that to get the efficiency percentage. So, if you determine your output hours for each presentation to be 4, and your team member spends 5 hours on the presentation, they’ll be operating at 80% efficiency.
With Prodoscore, you can easily see time spent in tools like AI and office productivity solutions, to help gauge which tools your most efficient and productive workers use. They don’t need to log anything manually; Prodoscore captures activity data via API for any business tools. Learn more about how it works here.
Final Face-Off - Which Metric is Better: Productivity or Efficiency?
Before 2015, the answer would have been efficiency. During that time, the most gains were realized from refining legacy processes that didn’t work at economies of scale. From 2015 until 2024, productivity would have been the answer since efficiency gains had been optimized and other metrics needed to be examined for top-line growth at most businesses.
Today, the answer is both. AI puts efficiency gains back on the board, but we can’t focus solely on them. Doing so means that other essential productivity metrics, such as employee engagement, may be ignored. While upward-trending numbers and profits are the ultimate end goal for most businesses, the other metrics we’ve been paying attention to are necessary in sustaining that upward trend.
If a company slashes jobs on the promise of what AI can do for it without thoroughly testing out solutions, it will lose top talent. The World Economic Forum’s 2025 Future of Jobs Report stated that 41% of employers planned to downsize their workforce due to AI.
While you can’t blame business leaders for getting excited about the first real leap forward in efficiency gains since 2015, they may need a gentle reminder that priceless human capital and quality could both be sacrificed in the race to cut salaries. A company can still be more efficient and productive with the same amount of staff using AI tools.
Ultimately, the question isn’t whether your business is more productive or efficient—it is how it treats the people who work there and buy or sell its products. Brand perception is everything and will sustain sales and employee goodwill well into the future. Anyone can use a tech tool to be more productive, but not everyone can inspire loyalty. That will be the secret sauce to success as the AI gold rush continues.
Resources:
- Fried, Harold & Lovell, Charles & Schmidt, S.. (1997). Efficiency and Productivity. The Measurement of Productive Efficiency and Productivity Change.
- Mankins, Michael. (2017). Great Companies Obsess Over Productivity, Not Efficiency. The Harvard Business Review.
