Intentional AI Use: A work in progress
With the recent popularization of AI tools, I’ve seen a lot of debate over the role they can, and should, play in the workplace–a concept I have found myself wrestling with lately. In certain spaces, AI tools are regarded as intimidating, impersonal, and even threatening. Although I don’t holistically agree with that attitude, I see the validity of it in a world where familiarity is so fleeting. Not only is the technical complexity of AI too large for our minds to visualize, it also feels nearly impossible to keep up with the newest iterations of it, constantly discerning the useful from the unnecessary. But my curiosity on the topic has resulted in a fully formed belief that, in its most intentional form, AI is a tool that insightful and unique human beings can learn to lean on in order to produce their most meaningful work.
I certainly can’t speak on every form of AI, but from my perspective I see many popular AI tools as just that. Tools. They have to be learned, mastered, and implemented just like any other. I can’t help but draw a parallel to the early 20th century when a resume-padding skill known as typing emerged after the keyboard was popularized in American workplaces. There were entire secretarial schools devoted to helping women perfect the skill in order to boost productivity in office settings. Today, typing has become an intuitive skill that no longer requires special training and is embedded in everyday life as a means of efficiency and convenience. Yet, we as a society are still able to recognize the irreplaceable value of hand writing. It could never be totally replaced, and now both methods of communication and documentation work in tandem to allow humans to reach a greater potential.
I must add that the many differences between ChatGPT and a thirty-pound steel typewriter are not lost on me; however, I draw on this example to demonstrate that despite the learning curve and potential bumps along the way, the path toward understanding an unfamiliar tool and the ways in which it can support human innovation is an important one to follow in the interest of creating a progressive and high performing workforce.
So, what’s missing? Why does using AI at work still feel wrong?
We have been tasked now, as the curious and critical beings that we are, with figuring out how to incorporate AI tools into our work while preserving the irreplaceable human element that makes our work special, and more importantly, valuable. AI may be able to generate insights for us, but the motivation, creativity, and criticisms that we bring to projects as people are what give our ideas the power to make real impact. The key is getting to know a tool well enough to be able to adapt it to your specific needs, and free up some mental bandwidth in the process.
Our team has been experimenting with Google’s NotebookLM as a tool to synthesize qualitative information, and we have found it decently helpful. The tool was first introduced to me as a way for TSM staff to learn more about a relevant topic in our community: unionization. Several sources were dumped into the tool, and throughout the week our team was able to ask it questions and quickly learn about the subject in our free time. It dawned on us after a couple of weeks that this tool could be useful in other ways because of its source-based design that strictly draws on the information you feed it.
When it was first utilized for qualitative data analysis at TSM, NotebookLM actually seemingly decreased unconscious human bias in certain aspects. The team initially did the labor of pulling out themes and synthesizing the data on their own just like they normally would, and when it came time to combine perspectives from multiple team members, NotebookLM proved to be useful in aiding that process, effectively organizing the complex human-centered data and confirming the team’s findings from an objective standpoint. We are still in a trial-and-error phase with it, however there is an attitude in the office of embracing this part of the process. I see the time spent collaboratively discussing our experience with the tool and trying to identify any weaknesses or errors to be well worth it. Though undoubtedly challenging at times, this experimentation stage does not deter or discourage us, but rather presents a valuable opportunity to reflect on our own work and test ways it could be enhanced.
AI tools are not one size fits all, and we definitely don’t have it all figured out. That said, I wanted to share our experience and challenge those who feel hesitant about AI to reflect on where you and your team invest the most time and energy.