Can AI Enable Us To Focus More on the Work We Love to Do?

Source:  Doyenhub Software Solution

Most of the attention on AI these days seems to focus on how it can make us more productive, as well as how it may even displace some workers whose tasks can now be automated.   However, a new study examines a slightly different question.  It explores whether AI can change the nature of work.  Might we engage in different activities as a result of the impact of AI, and might we even have more time to do what we truly love to do? 

Manuel Hoffmann and his colleagues have published a new working paper titled "Generative AI and the Nature of Work."  They examined the results of a natural experiment associated with the use of GitHub Copilot, a generative AI tool for software developers.  The findings illustrate an interesting shift in the work that developers were doing.  First, the generative AI tool enabled software developers to spend more time coding and less time on administrative tasks related to project management.  Second, they found that developers engaged in more exploratory work with the introduction of this AI tool.  In other words, developers conducted more experiments and spent less time on established projects.  The scholars summarize the key results as follows:

Copliot eligible developers engage with an additional 15 new repositories on average relative to ineligible peers. Beyond simply interacting with a new set of repositories, we also find evidence that generative AI enables developers to gain exposure to a wider range of technologies. In Panel B, we can see that eligible developers increase their cumulative exposure to new programming languages by 21.79% relative to the baseline. We also estimate a version of this cumulative programming language exposure measure weighted by the median salary reported by software developers who use that language.21 Access to Copilot induces developers to experiment with programming languages that command a 1.41% higher salary relative to a baseline of $119,371 (an increase of $1,683).

I found this paper to be quite thought provoking.  It makes perfect sense to me.  If AI is making us more productive, then what are doing with that new time have on our hands? Are we just doing more work in a given period of time, or are we sometimes using that "new time on our hands" to engage in innovation work?  Are we learning new skills, trying out new ideas, creating new products and services, and inventing better work processes?   These outcomes may not only be beneficial for the organization, but rewarding and fulfilling for us personally.  

Does Competing Outside of Work Harm Collaboration at Work?

Source: ESPN

What can we learn about collaboration at work from professional soccer players? A new study offers some fascinating insights.

First, why did scholars Thorsten Grohsjean, Henning Piezunka, and Maren Mickeler become interested in studying soccer players? They noted that people sometimes find themselves competing with their colleagues in settings outside of the workplace. For example, senior executives may find themselves sitting on the boards of directors of competing firms. They may be raising funds for non-profit organizations that are competing for the same grants from local foundations. Or, they may be donating to competing political candidates. Does this competition outside the workplace impact the interpersonal dynamic at work?   To answer this question, they adopted a novel research approach with the setting being professional soccer.   

Grohsjean, Piezunka, and Mickeler focused on the fact that many professional soccer teammates compete against each other during international tournaments such as the World Cup. Each teammate will play for his or her home country, and then they return to being teammates on their professional club the next season. The scholars decided to look at how World Cup competition affected collaboration among teammates the following season.  Specifically, they examined whether these teammates, who had competed against one another in the World Cup, would pass the ball more or less frequently when returning to their professional club the next season.  According to these scholars, "The average number of passes between treated players in the post-Cup season drops by about 11%." 

This startling finding suggests that we should be keenly aware of the potentially deleterious effects of competition among colleagues outside the workplace.  Spillover effects are real.  

Can Data Analytics Make Your Product Boring?

We live in an age in which companies are investing heavily in data analytics and algorithms.  Clearly, these analyses are often very helpful in making better decisions.  However, I'd like to pose a critical question as leaders ponder the impact of analytics in their organizations.  Can analytics make your product or service boring?  Could it lead to strategy convergence in your industry?  In other words, might an intense focus on analytics by all competitors lead to less much product differentiation?

Consider the case of the National Basketball Association (NBA).   In the 1986 NBA season, teams averaged 3.3 three-point attempts per game.  League MVP and champion Larry Bird attempted 194 attempts for the entire season.   That number led the entire league.  Last year, teams averaged 35.1 three-point attempts per game.  26 players attempted more than 500 three-point shots during the season.   In 1986, games involved a wide array of shooting.  Teams had powerful low-post players such as Hakeem Olajuwon and Kevin McHale.  They had players who could slash to the basket and others who had perfected the mid-range jumper.  Today, the game involves an overwhelming number of three-point shots and very little low-post play.  In the 1980s, some teams played a bruising, slower, physical game.  Others played a run-and-gun, fast-break game.  Today, nearly all teams rely heavily on long-distance shooting. 

Fans can disagree over whether today's game is better than the 1980s version of NBA basketball. However, it is difficult to argue that today's game is more differentiated than the 1980s version.  Most assuredly, teams all play a much more similar style of play today.  Strategy convergence has taken place at an incredible rate.   What's driven this change?  Analytics.  The data clearly say that teams should take a high number of three-point shots.  It simply makes a great deal of sense if you want to win. 

What's the lesson for business leaders?  Analytics may drive what seem like clearly better decisions.  Yet, it might just lead to strategy convergence, which may be harmful in the long run.  Your product may become much more similar to those of your competitors.  Distinctiveness falls, and in the long run, that may actually harm profitability for all rivals.