Companies are investing a lot of money in AI resources right now in the hope that it will enhance people’s performance and make organizations function more efficiently. Because of the size of the investment, there is pressure for people to engage with artificial intelligence in as many tasks as possible.
Broadly speaking, if you have not played around much with AI, there is value in engaging with it. The tools are constantly changing, and the capacities continue to grow. Not only are the models providing more systematic responses, they are recommending and building tools to carry out tasks relevant to the conversation.
That said, there are many dangers in becoming overly reliant on AI at work. Here are three situations where you should avoid or minimize your use of AI.
1. When you need to learn
One phrase that has entered the public discourse since the rise of generative AI is cognitive offloading. The broad idea is that AI is, almost by definition, carrying out tasks that required mental effort in the past.
What many people may not have recognized is that mental effort is a signal to the brain that something needs to be learned. That is because the brain is trying to minimize the amount of time it has to spend on doing any task. The more effort you put into it, the more the brain assumes that the task will require less effort in the future if something is learned. That makes it worth investing the energy to change the brain’s structure, which is what supports learning physiologically.
The more you circumvent effort by engaging with AI, the less likely you are to send the brain signals that something should be learned. If knowledge or skills need to be learned, you should sacrifice short-term efficiency for the long-term benefit of learning. Take the time to work through the complexity of a problem yourself. Read material rather than summarizing it. Ask yourself questions and answer them. In this way, you’re setting yourself up to learn something new.
When you do complete that effort, you may choose to check your work or understanding using AI. Engage in a conversation with a model to ensure you grasp the information well. You can even take a reading and ask AI to quiz you on it to test your understanding.
2. When the details matter
One temptation in the age of AI is to take long readings, email threads, or reports of projects and summarize them to save time. After all, why read a long document when you can get the gist of it quickly.
As the old saying goes, though, God—or sometimes the devil—is in the details. Plenty of research has demonstrated that people suffer from an illusion of explanatory depth in which they believe they understand the world better than they actually do.
If you are going to be responsible for the detailed understanding of something, there is no good way to short-circuit the effort required to internalize that explanation. You’re going to have to work through the explanation in all of its glory and ensure that you have a grasp of the minutiae in addition to the general summary.
3. When you need to engage with your team
AI models play into a work environment that is becoming increasingly individualized and remote. The COVID-19 pandemic ushered in an era that has greatly increased the number of people who work from home. In addition, the rising generation grew up with cell phones and texting (not to mention a pandemic that influenced their social development). Generative AI benefits people working alone by giving them a constantly available partner to think through workplace issues.
Working as a team has many benefits, though, and AI cannot replace them all. On the positive side, AI can help to get you out of your own head by providing an alternative perspective on problems.
Engaging with members of your team has other important consequences. If you want to get widespread acceptance of an idea, it is helpful for many different people from your organization to have input and an opportunity to get their concerns addressed. If you work on those ideas only with AI, you may develop a great concept, but you haven’t done the work to bring the rest of your team along with you.
In addition, after a course of action has been established, it is valuable to have your team synchronized in the way they are thinking about key concepts. When groups work together, that creates convergence in the way they think about things. Group dynamics help a team to settle on a common vocabulary for discussing things and a common understanding.
When there is value in developing unity, bring a group together to work rather than engaging with AI. Those interactions may feel awkward and may even create tensions while the work is being done, but the benefit in giving an entire team ownership of the work and a shared understanding are worth the effort.