Leading AI Adoption: 6 Ways to Build Buy-In

In the last three years, there has been lots of conversation around AI and its use throughout higher education. As MarComm professionals, during that time we’ve moved from focusing on how to use the tools to exploring how to best integrate AI into our teams and workflows. Giving our teams access to AI tools is easy. Helping our teams get comfortable using them and encouraging people to change their daily habits and workflows in support of AI is something more difficult.

There are different comfort levels with AI. Some people are early AI adopters, and others are AI critics. Often those perspectives exist on the same team. Such varied perspectives about AI make adoption and implementation a leadership and change management issue.

During my nearly twenty years in higher education marketing, here are a few strategies I’ve found useful in managing culture work and adopting new technology. Over the last few years, I’ve noticed these tactics appear to work well in AI implementation as well.

1. Start with the problem at hand: With AI, it can be easy to focus all the different ways the tools can be used. Because AI can do such much, it can be overwhelming and stall progress if there is not a clear direction. As leaders, it is important that we give people parameters to work within to help narrow the scope. For example, I encourage my team members to focus on an aspect of their job they don’t enjoy and see if AI can help. Freeing up time to focus on tasks people enjoy is a motivator. Additionally, when people come to me stuck on a problem, I ask how they’ve used AI to try and address it. I find that narrowing to a particular situation helps people use AI and be less overwhelmed by all it can do.

2. Intentionally bring the team along: When integrating AI into a process or approach, it is important to let others be part of the process. It is tempting to build and test alone because issues and hiccups are far less visible. However, when adopting a new process or technology, finding ways to engage others in the process is important for buy-in. One way to do this is regularly invite team members into meetings to talk through a particular use case or process being worked on. This helps the team feel valued, but it also helps them begin to understand how the technology will be used when implemented. This is good context because they’ll be better prepared for how their workflows may change when the technology is rolled out.

3. Make it easier: When adopting a new AI technology, people are far less likely to adopt it if it doesn’t make life easier. To many, AI feels scary and complex. That means it’s critical that new processes make thing less complex, not more. Even if the outcomes are better using AI, teams are less likely to adopt if it requires more cumbersome processes. Our job is leaders is to look across the entire process and work to make it better for our teams. If the new AI portion of the process now requires more steps, how can other parts be easier? This system-level view is often overlooked, but it can be critical for adoption.

4. Create space to experiment: Giving teams a sandbox environment to explore AI helps make AI feel less intimidating. Having access and the time allotted to explore new approaches can help team members be less overwhelmed by AI tools. For leaders, this is about coaching and project management. It involves giving the team access, time on their calendars and regular encouragement. Since team members are in different places with AI, it involves understanding what is helpful for each team member and providing that. For some it’s ensuring they have time. For others, it’s hearing that it is okay to use AI. One way I try to do this broadly is to provide time in staff meetings each week to talk about AI wins and failures. This helps signal that AI use is okay and that I hope team members are devoting time to play with the tools each week.

5. Model the behavior: Teams look to leaders when deciding how to proceed, and it’s no different with AI. For leaders looking to create AI-friendly culture, the leader must actively be involved in using the tools. For leaders, AI is the opposite of the traditional leadership journey. Usually, a leader becomes the leader because of their expertise. To lead in AI means learning at the same time. Being in a leadership position and not having all the answers is a vulnerable pace to be. However, the reality is that in creating a culture of experimentation, the team needs to see the leader struggle and fumble. As part of our staff meeting each week, I try to share how I used AI. However, it seems to register the most when I talk about a way I used it and it didn’t work well. Hearing about the failure helps the team feel more comfortable.

The focus of AI is no longer should we use these tools, but it is how do we change culture to support adoption. Changing culture takes intentional leadership and effort. Focusing on the problem helps teams to avoid being overwhelmed with the breadth of AI. Intentionally bringing others along and ensuring AI makes life easier will help with teams being open to try new approaches. Giving teams a place to test, while seeing leaders experimenting, helps teams get comfortable using the tools. Ultimately, successful AI adoption involves creating a campus culture where people willing to try and feel supported throughout the process. The five strategies discussed above are a great way to get started.