Learning From The Learners: AI Edition

I know I’ve mentioned this before, but of all the programs we’ve built for general audiences, our Thrive with AI program is the one I’m most proud of. This program was born out of a DEEP frustration with the frankly crappy prompt engineering courses that played on hopes and fears about this new technology but did almost nothing to actually help people do great work alongside AI.

We spent a long time (probably too long) building our program, but we sort of had to. After all, if you want to capture best practices of working alongside AI, you need to observe the people doing that and then figure out how to turn that into experiential learning that genuinely sticks. And, you need to build the course to adapt as new advances arrive rapidly.

I believe we’ve achieved all that, but what we have learned in the building stage of the program is only the beginning. We continue to learn and adjust the program based on feedback from people going through it – as individuals, in small businesses, and in enterprises. That’s always been part of our philosophy. After all, isn’t it weird that many learning and development programs don’t develop based on new knowledge? I’ll be sharing more on that in a future newsletter.

So, if you are currently in a BillionMinds program, please know that we love to hear that you are enjoying it, but we REALLY appreciate understanding what you disagree with and why. No matter how smart we might think we are, we can never match the collective wisdom of the people in our programs. Fortunately, many of you are willing to share, for which we will always be grateful. Every time you do, you make our programs better.

Today, I want to focus on a specific example we could never have learned from reading published research alone – how mindset changes how people work with AI.

The AI Mindset

As we began designing the program, we asked participants a simple but open-ended question: ” What is your primary motivation for using AI in the workplace?” We expected many different answers, but the vast majority (over 90%) were variants of just two answers:

1) Saving me time, and 2) Improving the quality of my work.

Now, not only were there only really two answers here, but there was also a huge delta between the popularity of each of those answers, with the time savers massively outnumbering the quality improvers.

This raised a very interesting question—if we tracked the “Time Savers,” perhaps their experience with using AI would be different from that of the “Quality Improvers.” And today, almost a year later, we’ve found that to be the case. Here’s how.

About The Time Savers

When Generative AI was launched, much of the initial commentary focused on how it could save employees huge amounts of time. You might have seen cool examples on TikTok of people taking 3-hour tasks and performing them in 5 seconds. Some influencers even claimed that they were now able to use it as a vehicle to enable their quiet quitting.

Many of the Time Savers were looking for “hacks” – using AI to perform tasks they would have previously performed themselves. They would often scour YouTube for these hacks and were very quick to implement anything they thought could work for them.

In a world where there is always more to do than time to do it, and where many of us are overwhelmed by our day-to-day work experience, the prospect of saving vast amounts of time is very appealing, so in that sense, it’s no surprise that this was our number one answer.

But as many of our Time Savers found out, the high associated with leveraging AI’s time-saving potential can be short-lived. These people often did get more done, but not enough to help them finish work earlier. Many were falling victim to what at BillionMinds we call the To-Do List Paradox— the more work a knowledge worker gets done, the more work it creates.

For some, there was an additional frustration. In general, the tasks with the potential to save the most time were the most complex and the most difficult to describe. Consequently, generative AI was most likely to struggle with those tasks. Ironically, our Time Savers were the most prone to spend hours tweaking their prompts and would become increasingly frustrated, even angry, that AI misunderstood their requests.

And finally, our Time Savers were more likely to miss mistakes that AI made – perhaps exacerbated by using AI from a starting point of being overwhelmed.

To sum up, Generative AI often increased the productivity of the Time Savers, which benefitted their employer—but that generally did not translate to an improved work experience for the employee, and the Time Savers felt let down by the technology.

About the Quality Improvers

This (much smaller) group approached AI in a significantly different way. In most cases, their starting point for engaging with AI was to use it as a “second brain.”

For example, if a Quality Improver was using AI to help them write a document, they might start with a set of ideas and ask AI to provide suggestions for what they had missed. Then, they might write a draft based on that and ask AI for suggestions on how it could be improved. And so on, through repeated cycles from ideation to delivery of their work product.

Interestingly, the Quality Improvers did not always save time through this process, but they produced better-quality output in the time available.

This led to greater satisfaction in their AI experience and increased motivation overall. That’s not surprising. As Dan Pink points out in “Drive,” – motivation is closely linked to mastery, and AI is improving their mastery.

We also found another interesting link. Our Quality Improvers typically displayed more of a growth mindset than our Time Savers. It’s possible that a growth mindset is a prerequisite for using AI in this way. After all, asking AI to improve your work starts from an understanding that your work can be improved.

What to do about all this

It’s one thing to observe all this and entirely another to understand what to do about it. After all, productivity IS valuable to organizations, and getting more done each day might be the way you provide value and stay relevant. And, sometimes, the additional layer of quality that AI may help you provide may not be needed.

However, I think the important thing to emphasize here is that our observations are about primary drivers for engaging with AI. If your primary driver is quality, it doesn’t prohibit you from using AI to save time. In fact, you might use your focus on quality to engage AI in building an optimal structure for your day, and that may save you huge amounts of time.

One thing is clear, though. It’s difficult to have a quality improvement mindset if you are overwhelmed. Many of the Time Savers we spoke to did feel overwhelmed, which is why they reached for AI to solve their problems. If you are a manager and want to make the best use of AI in your team, giving people the time and space to do that well is an important part of the process. Of course, we’d recommend our Thrive With AI program as part of that.