Using AI in the workplace promises significant productivity gains. And using chatbots may make you feel productive, because it they designed to create engagement from users. But, you need to be more explicit about calculating the costs (and opportunity costs) and tangible benefits to your work. That will help you determine whether the AI juice is worth the LLM squeeze.
Here are three key considerations.
1. Calculate your time spent using AI
When people first started analyzing the downside of smart phones, one of the big data points that got trotted out was how long someone would remain off-task once they picked up their phone. Because apps on your phone are so immersive, once you pick up the phone, it may be 20 minutes before you are back to work on what you were doing before. Based on data like that, phone operating systems started providing users with the amount of time they were spending on their phones and the activities they were engaged in, with the hope that information would guide how people engaged with technology.
LLMs need something similar.
When you sit down to engage with a chatbot or system that will help you build a tool, it creates an engaging conversation that provides you with long responses to your queries and can build tools for you on the fly. When the system is building tools, the models often step through the logic they are using, so you feel like you will miss something if you look away.
As a result, engaging with an AI system can put you in a flow state in which you don’t notice the passage of time. That means you need to track the time you’re spending engaging with AI at work explicitly. That time estimate reflects two costs. First, you have to know whether the value of what you get from the engagement is worth that cost. Second, you should look over your To Do list and determine whether there are other priority items you could have dealt with in the time you spent with AI. The things you could have done with a resource (like time) spent elsewhere is called an opportunity cost, and those opportunity costs often go unnoticed.
2. Evaluate the quality of the output
When you finish engaging with an AI model, you often feel pretty good. For one thing, unless you give the model you’re working with explicit instructions, it tends to butter you up—telling you how insightful and nuanced your thinking is. For another, the model often suggests things you haven’t considered before, so it will take your thinking in a new direction. And flow states in general feel good.
