Leading While Learning

AI is revolutionizing higher education marketing, and AI tools can help with a multitude of tasks including writing, editing, and brainstorming on a daily basis. Despite the numerous potential benefits of using AI tools, many higher education leaders struggle with implementation.  

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In talking with a colleague this week on another topic, I had a realization about part of the struggle around AI. The challenge goes much deeper than the tools themselves. Generative AI (and agentic AI) breaks the traditional mold of leadership. Historically, leaders were subject matter experts in their field and were then tasked with leading others. AI has broken this model, and higher education leaders are now expected to lead the implementation before mastery. 

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Changes to the Model and its Impact on Leadership 

Before AI, marketing work was grounded in expertise. Leaders had lived experiences about running social media campaigns, brand awareness campaigns, digital marketing, and other daily aspects of the work. Their experience and prior learning made it easy to set expectations and guide teams to execute toward established best practices.  

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The model was straightforward: 

Learn → Set Expectations → Lead → Execute 

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With the rise of generative AI, the model has flipped. Leaders no longer have time to fully learn a tool before asking their team to begin using it. This is for two reasons. The pace of innovation is faster than anything we’ve seen before, and waiting to fully understand AI risks falling behind. At the same time, growing pressure to demonstrate higher education’s value has positioned AI as a lever for doing more with less. 

As a result, leaders feel pressure to act earlier, before they fully understand the tools, their strategic value and potential risks. 

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The new model looks more like this: 

Lead → Set Expectations → Learn → Adjust 

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In the new model, leaders are setting direction and expectations first. Then, they are learning alongside their teams by testing use cases, iterating on their approach, and refining in almost real-time as more information becomes available about the tools and their capabilities. 

With a new model, leadership itself also looks different. Leadership requires less certainty, more adaptability, and a willingness to lead through what I call the “messy middle,” as opposed to leading from a place of expertise. 

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Four Strategies to Lead While Learning 

1. Lead the Conversation; Not the Tech  

As a leader, it is important to set the tone for the team. I try to create a space for the conversation, even without having the answers. While it can be scary to lead a conversation without the answers, that is the reality with AI. I lead the conversation by sharing about my AI successes and failures in our staff meeting each week. Being candid about how AI worked has sparked conversation from others on the team. Additionally, sharing failures helps others see it is okay to fail and also how to avoid the same mistakes. I also share articles on AI to help our team understand how the tool is changing our work and how we need to be thinking differently. Something new I’ve done this year is asked every team member to have a goal that relates to AI. Those look very different but help keep the conversation going.

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For example: Here’s an email ChatGPT helped me write, and here’s the prompt I used to get to this point. 

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2. Set Guardrails 

The use of AI on college campuses is still in flux, so I feel my role as a leader is to put guardrails in place around the use of the tools. Some of the guardrails I’ve talked about involve positioning AI as a thought partner. I think it’s easy to take a shortcut and ask AI to write a press release, develop a plan, or take the lead on a project. From my perspective, that’s the wrong way to use AI. I find I get much better results when I give it my initial thoughts and then partner with AI to build something that fills in the gaps or expands on what I have. The strength of my work is in strategy and institutional context, and it is an important guardrail to ensure I remain the leader. 

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For example: I needed help with a launch event plan for an upcoming dinner. I shared the current draft plan and shared our goals for the dinner. It helped me to identify three ways I could elevate the work without additional resources. 

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3. Reward Outputs and Outcomes 

As data-informed decision-makers, it can be easy to focus on what our teams achieve using AI. That matters, but when it comes to AI, the outputs themselves have value, even when they miss the mark. Every time someone uses AI, those outputs are part of the learning process. If I only reward successful outcomes, I risk discouraging the experimentation required to actually improve. Early AI use is often not great, but that’s where learning and growth happens. As a leader, I try to reinforce that the effort matters. Struggling through a poor result is often what leads to a stronger one the next time. I can hear a mentor of mine saying: honor the struggle. If we want teams to improve, we have to encourage them to keep at it.  

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For example: I am so proud of how everyone tried AI this week across our work. I think we learned some things about what the tools do well and where it isn’t as refined yet. Let’s keep working on trying it out. 

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4. Be Vulnerable 

Too often as leaders, we put on a brave face for our teams. We carry many of the pressures of our industry and shield our teams from it. There are times that is appropriate. I think AI is a moment to let down our guard and share when we are struggling and why. Doing that helps relieve the pressure our teams feel, and it gives us the early-adopters the opportunity to take the lead. One way I have been vulnerable around AI is sharing that I worry about keeping up with the pace of innovation. It regularly improves and new tools come online. It worries me that I’ll be left behind. Additionally, I worry about the content producers on our team. Will there ever be a point where they aren’t viewed as valuable? That said, I also see how they can use the tool to benefit them in their content production, whether it’s writing, video production, or some other aspect of the work. I think it is key to offer both perspectives. 

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For example: I am glad we are trying AI more. I don’t have all the answers about how it will change or work. And if I’m honest, it scares me a bit. However, I do feel that it is here and will be important for us to understand. That’s why I want us working on it now. 

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Where This Leaves Us

AI has disrupted the marketing and communications landscape, and with it, how leaders lead. There is no longer the luxury of expertise before leadership. Instead, leaders must be comfortable navigating the discomfort and ambiguity of not having all the answers. It can be tempting to pause, wait, and allow things to unfold before proceeding. However, this creates the risk of falling behind. Instead, leaders must work to forge ahead by focusing on leading the conversation, setting guardrails, rewarding outputs and outcomes, and being vulnerable. The leaders who move their teams forward likely don’t have AI figured out, but they are willing to lead while learning.