How much of your workweek do you believe is spent on ‘work about work’ instead of doing the work? Work about work could be status checks, updates, emails, or other things that are required to stay aligned. This is precisely the question that was asked in a recent survey of 1,000 technology workers, team leaders, and managers across the nation, and the results are eye opening.
The survey was powered by AlphaROC, which is the data science company behind occam, a machine-learning powered market research platform, and was presented at a summit organized by the University of Maryland’s Project Management Center for Excellence in April. The data from the survey unlocks interesting insights.
The results show more than a third of tech and knowledge workers spend 25-50% of their workday on work about work—and another third of respondents admit they spend 10-25% of their time this way. Breaking the time even further is very revealing. We see:
- 13.8% report spending 10% of time on work about work.
- 14.6% say they spend more than half of their workweek this way
- 35.6% report spending 25–50% of their time
- 36% of respondents say they spend 10-25% of their time this way
Now, let’s consider this in terms of real money or how it breaks down into making the real money here, the ultimate impact of inefficiency on the bottomline. Studies estimate such inefficiency represents an estimated $1 trillion problem in the U.S. economy, but if you dig into specific industries, such as manufacturing, waste up can go up to $8 trillion.
The Solution to Business Inefficiencies
This is certainly a challenge that many are hoping technology can help play a role. And specifically, many are looking to AI (artificial intelligence), to step up and solve sooner rather than later. There are many examples already of where AI is proving to be invaluable. For this column, let’s look at a recent example out of MIT (Massachusetts Institute of Technology).
MIT academics Mok Oh, Ph.D, Professor Wojciech Matusik, and Michael Foshey, have assembled a team and cofounded Foundation EGI (engineering general intelligence) to help solve some of the inefficiency challenges that exist specifically in manufacturing and engineering industries.
The objective here is technology can help design and manufacturing engineers build better products faster, driving healthier revenues. The researchers suggest LLMs (large language models) and an agentic AI platform can help transform natural language inputs, including vague and messy instructions, into codified programming that is accurate and structured.
Ultimately, technology such as this could help optimize automation, accuracy, and efficiency at every stage of the design to production lifecycle. And, of course, this is only one example of how AI can help weed out disorganized and inefficient processes.
If we are honest, we all experience inefficiencies at work. The real question now is how are we all considering how to apply AI to reduce those inefficiencies? So, let’s take the time doing the work about work!
How can we weed these out? Can AI help minimize the work about work? What do you see in your own industry?
Want to tweet about this article? Use hashtags #IoT #sustainability #AI #5G #cloud #edge #futureofwork #digitaltransformation #manufacturing