It seems that every week, AI technology has learned to do something humans do, but faster and better. From detecting cancers and eye conditions to predicting floods; or analyzing the language, tone, and facial expressions of candidates during recruitment processes, AI is now at the stage where it not only supports human judgment, but also makes increasingly more complex and accurate decisions.

As technology further improves and we learn how to better work and collaborate with AI, interactions between humans and computers will significantly enhance creativity—of both humans and bots.

Imagine that your boss Ethan calls you into his office. He expresses disappointment in your recent performance and lack of commitment. How would you react? Accept the feedback and put in more effort? Would you pout in your office and start looking for a new job?

Now, would your reaction be different if your boss was not named Ethan but Emily?

I’m a professor of economics, and my research investigates this very question. We hired 2,700 workers online to transcribe receipts, randomly assigning a male or female name to a manager, and randomly assigning which workers would receive performance feedback.

Results show that both women and men react more negatively to criticism if it comes from a woman. Our subjects reported that criticism by a woman led to a larger reduction in job satisfaction than criticism by a man. Employees were also doubly disinterested in working for the firm in the future if they had been criticized by a female boss.

One of the key ideas in lean manufacturing is that defects should be detected as early as possible. Efforts to control manufacturing processes, so that issues can be detected before defects occur, actually predate lean. Statistical process control (SPC) is a set of methods first created by Walter A. Shewhart at Bell Laboratories during the early 1920s. W. Edwards Deming standardized SPC for U.S. industry during WWII and introduced it to Japan during the American occupation after the war. SPC became a key part of Six Sigma, the Toyota Production System (TPS), and by extension, lean manufacturing.

SPC measures the outputs of processes, looking for small but statistically significant changes, so that corrections can be made before defects occur. SPC was first used within manufacturing, where it can greatly reduce waste due to rework and scrap. It can be used for any process that has a measurable output, and SPC is now widely used in service industries and healthcare.

When my kids, ages 11 and 8, bang through the back door after school, often the first thing out of their mouths is: “Mom! Can we play Prodigy?”

After a quick mental calculation of how much screen time they've already had for the week and how much peace and quiet I need to finish my work, I acquiesce. After all, Prodigy is a role-playing video game that encourages kids to practice math facts. It’s educational.

Right?

Though video games are increasingly making their way into classrooms, scientists who study them say the data are lacking on whether they can actually improve learning—and most agree that teachers still outperform games in all but a few circumstances.

But there is growing evidence that some types of video games may improve brain performance on a narrow set of tasks. This is potentially good news for students, as well as for the millions of people who love to play, or at least can’t seem to stop playing (see infographic).

“There is a lot of evidence that people—and not just young people—spend a lot of time playing games on their screens,” says Richard Mayer, an education psychology researcher at the University of California, Santa Barbara. “If we could turn that into something more productive, that would be a worthwhile thing to do.”

How can industrial and manufacturing enterprises achieve better new product introduction (NPI), a critical element of operational excellence? Corporate goals of improving market share and revenue, maintaining competitive differentiators, and improving customer experiences are especially challenging when developing and launching new products—making it vitally important that NPI is seamless and high quality. Despite significant investment in NPI, a startling 44 percent of new products fail to meet most NPI success criteria.

Manufacturers and industrials face three key challenges:
• Organizational and data siloes, with little collaboration among increasingly complex supplier networks
• Core process deficiencies, as shown by key performance metrics, even as solutions are within reach
• Outdated or poorly integrated operations and quality systems, and data sources