Ahead Of The Curve: Emerging Technology’s Impact On Employee Productivity

Scott Pezza

We live in a fascinating time when it comes to advances in technology. The amount of data we are producing has always seemed to outpace our ability to process and analyze it. As technologies like artificial intelligence, machine learning, and Big Data analysis are making their way into the mainstream of business applications, we’re beginning to make some headway in accessing, analyzing, and deriving valuable insights from this ever-growing collection of information. Still, questions remain as to what we do with those insights and how can we ensure that these new investments create value for our enterprises.

In Intelligent Procurement from SAP Ariba: Making Procurement Solutions Smarter, there’s a lot of really interesting information about cutting-edge business technologies, example use cases, and highlights on specific areas of development focus. Some of the specific technologies mentioned, like cognitive computing and neural networks, can seem a bit intimidating when you first encounter them.

In this blog, we’ll take it easy on the technical jargon and just focus on the impact these solutions can have in one specific area: employee productivity.

A new world: Keyboards optional

One big area where new technology can fundamentally change our work environment is in the way we interact with our business systems. A great example of that is voice-based (or “conversational”) interfaces, where we can say what we’d like to do and the system responds to those instructions. For traditional office jobs with easy access to mouses and keyboards, this may be more of a novelty than a true game-changer. But what about in field services in places that are physically constrained or otherwise inhospitable to computers due to heat, moisture, or similar factors? Beyond that, how about in businesses looking to improve accessibility for their workforce to make it easier to interact with systems for employees who cannot use traditional methods?

Making these systems easier to access goes hand-in-hand with making them generally easier to use. For years, there has been a push to make business systems look and feel more like those we use in our personal lives with simplicity and intuitiveness as the goals. Where this really takes off is when these systems truly understand what you are trying to do and can present relevant information to help in your decision-making. In the past, developers would have to predict what you’d need to ensure that what you need is included on a specific screen. Now, technologies can learn from your past behavior and bring that information to you without any human telling the system to do so. In this respect, the system is applying what it has learned from observing how you work and what you need to do your job.

The benefits of machine learning extend beyond the individual employee, as well. We won’t stay in the same roles forever, so it is inevitable that someone else will take over the tasks that we’re doing today. Because the system has learned how the job works and what information is helpful, it can help the next person get up to speed more quickly than if left to their own devices. The key here is that the system has learned on its own and is not dependent on (and limited by) specific rules that were defined manually, as was the approach for older “expert systems.” It is always observing, always learning, and always focused on improving the productivity of its users.

Moving beyond hype and towards value

So far, we know that new technologies can make our day-to-day jobs easier and help a new person get up and running in a new role. But what does that actually mean, and how does that translate to business value? The best way I know to illustrate the impact is through a simplified comparison of an employee’s learning curve to their wages over time. In other words, it’s a look at how much the employee produces relative to their cost over the course of their career. Here it is in graphical form:

Employee Value Delivered vs. Wages Over TimeEmployee value delivered vs. wages over time

In this chart, the dotted curve is the value that an employee contributes to the business. In the beginning, this is low as they learn about the job and gain experience. The curve peaks and then begins to decline later in their career as their skills degrade or become less relevant. The solid line is the amount of wages the employee is paid, with the simple assumption that those wages increase over time.

This leads to three distinct periods of time, each of which is labeled above. During Time A, the employee is ramping up and does not yet produce more value than they are paid – this is the company’s investment in the employee. In Time B, the employee moves from being comfortable with their position to mastering it, producing more value than they take out via wages. This is where the company’s investment is paid back. Finally, in Time C, the worker’s productivity goes down during their highest-earning years, leading to a period where some of the peak-year profitability is repaid.

Why is this important? The main reason is that it provides a useful model to gauge how new technologies’ impact on employee productivity translates into real value. There are three main ways this happens:

  • In the beginning (Time A), the dotted line starts a bit higher. This means that on Day 1, the employee can produce more value than in the past. The key here is the learning system that understands how the job was previously done and can guide the new employee accordingly.
  • As onboarding continues (moving from Time A to B), the dotted line gets much steeper. This means they are getting up to speed much faster than was possible in the past. It also means that the time when they get paid more than they produce is shortened, improving business profitability. This is due to new systems’ ease of use, with “in context” information presented when and where it is needed.
  • Finally, during the remainder of their career (Time B through C), the dotted line moves to more of a plateau at its peak, rather than beginning to decline. This means that the employee consistently produces higher value without feeling the decline of skills over time. It also means that their individual profitability stays constant, minimizing or even eliminating the expected late-career deficit when wages again outpace value production.

In a nutshell

Technical details are incredibly important and are absolutely necessary to evaluating whether solutions and their underlying technologies are a good fit for an organization’s technology ecosystem. That said, if we start the process by focusing solely on those specifics, we can get lost in a sea of jargon and lose sight of the true goal: producing value for our business. This blog provides just one example of a way to look at value, with an emphasis on how, when systems are easy to learn and use – and capable of retaining key learning on their own – we have an opportunity to fundamentally shift some of the economics of employee labor.

Check out our recent paper, Intelligent Procurement from SAP Ariba: Making Procurement Solutions Smarter, to look at a few example use cases and step back to think about not just how each specific process can change, but how that change may influence the existing dynamics of your organization.


Scott Pezza

About Scott Pezza

As part of SAP Ariba's Digital Transformation Organization's Center of Excellence, Scott researches, compiles, and shares best-practice information to help SAP Ariba's customers get the most out of their investments. He has a dual focus on the emerging technologies (AI/ML, IoT, Blockchain, etc.) across the source-to-settle cycle, as well as a specific interest in the financial supply chain (invoice management, payments, discounting, and supply chain finance). His research helps inform strategic planning, performance measurement, and program execution. He has spent the past 17 years in the B2B technology space, in roles ranging from software development and support to research and consulting. Scott earned his BA in English and Philosophy from Clark University, his MBA from Boston University Graduate School of Management, and his JD from Boston University School of Law, where he served on the Executive Board of the Annual Review of Banking and Financial Law.