And Carrie On

As a MarComm leader, I’ve been encouraging my team to use AI in our work regularly. To that end, we have a team ChatGPT account that helps us work more intentionally. Additionally, every team member has an AI goal this year to help us bake that into the work and increase comfort with using the tools. Finally, our team shares ways we’re using AI in our meetings each week.

I tend to consider myself well-versed in AI. That said, AI still surprises me on occasion. Here are six AI lessons that I didn’t fully expect when I began using AI tools nearly two years ago.

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Lessons I Didn’t Expect

The biggest barrier is curiosity – AI is much more about change management than new technology. AI is the latest tool, and it’s a big one. Because of that, some people feel intimidated or scared to try it. As leaders, our job is less about getting people technically proficient and more about encouraging people to type in that first prompt and test it out. Once people begin regularly testing it and see some of the ways they can help, they are generally more receptive to trying it in other use cases. The people who are getting the most benefit from AI are those who are willing to test it out and experiment to see how it can work best.

AI exposes vague thinking – One of the biggest surprises for me is how AI can highlight bad strategy. It’s really good at inadvertently sussing out root causes. When I have trouble with generating the right prompt or explaining all the needed context for the output I desire, it usually is a problem with the input itself. I think sometimes we are too quick to judge the AI tool and avoid the root cause, which is unclear strategy. On several occasions, that process has helped me go back to the project and realize the strategy wasn’t clear, which was why I was having issues articulating to AI what I needed. AI is trained to look at strategy and then operationalize the tactics, so when the strategy isn’t firm, the output won’t be either.

Iteration beats perfection – When I first started using AI, I was wowed that it could get me something pretty quickly that was 80% of where I wanted to go. However, I wanted to get to 100%. Something needed to change. I adjusted how I was prompting and started working in an iterative model where I’d look at the draft and work through it with the tool. That iterative approach ensured my critical thinking skills remained intact but also helped me to work with the tool and give it feedback, just like I would an employee. As AI learned my style, it got better at getting me closer. I also learned how to provide information at the beginning to help it. A mentor used to tell me “honor the struggle,” and the struggle helped me learn how to best use the tool so I get much closer faster.

AI is only as good as your perspective – Another surprise for me is realizing the tool can generate ideas and give you feedback again and again. However, combining the idea generation with your own perspective is where the real value lies. The tools are great, but the experience as a leader or a sector expert helps you know which ideas will work in a particular context and which will fail miserably. AI doesn’t have that critical thinking ability of human judgment. Many times I provide AI feedback about an idea and explain why it won’t work (or sometimes why I think its idea is terrible.) It appreciates the insight and helps it improve. However, the true success comes when you combine AI’s speed with your professional judgment.

AI works best as a thought partner – One of the biggest surprises I’ve experienced is how AI works best as a thought partner. As I’ve become more familiar with AI, I’ve changed my approach to have AI be a thought partner on technical execution, while I drive the strategy. For example, I may use it to analyze data, crunch numbers, or write a quote. However, I’m not using it to develop the strategy or vision. Keeping it in the thought partner lane is where it works best. It can usually do that work faster and better than I can, and it keeps me in the strategic lane of driving the vision. It also means I’m regularly giving the tool feedback and providing context and judgment that I have. I’ve been surprised how well this approach has worked and helped me to get stronger results when using AI.

AI pushes you to explain your reasoning – AI has helped me improve in explaining my logic and reasoning for decisions. When working with a tool, I regularly have to give it context, or it asks questions that I have to explain my reasoning. This process has helped me realize that sometimes I rely on gut instincts more than I should. Having to articulate those gut moments to AI has helped me to get better at thinking through the “why” of decisions and providing that context to others. Because AI has a way of asking lots of questions and needing clarifying information, it helps ensure I’m making decisions that are less on a gut check and have sound reasons that I can articulate.

How has AI surprised you?

If you’re using AI more, what have you learned about the tool (or yourself) that make you better at leading and managing this work.

Feel free to drop your thoughts in the comments!