Leading at the Threshold: Abiding Leadership in the Age of AI
Why the most important leadership decision right now is not what we adopt, but how we lead through it
Inside the Unmade Future
In the final essay of the Abiding Leadership series, I wrote that we are standing at a threshold. That threshold has a name at this point in time: artificial intelligence. If the last series was about the idea of abiding, this next one begins with a simple recognition: We are no longer preparing for change. We are living inside the unmade future. So, this next series will focus on the application of the Abiding Leadership model in the age of AI.
Artificial intelligence is not a future possibility, it is our present reality. It is already shaping classrooms, workflows, assessment, advising, and decision-making across our institutions. And with it comes questions: What should we do? What tools should we adopt? What policies should we create? How quickly do we need to move? These are important questions. But they are not the first question. Instead, there is a question that feels more urgent than any that I’ve encountered in my leadership journey thus far: Will this change who we are? I have seen this question surface in faculty conversations, in student work, and in strategy meetings. It is rarely spoken directly, but it’s there under the surface. I think this question is what is behind the concern about what AI might mean for teaching, the resistance from students or faculty that seems like it’s about identity, policy debates, arguments about tool adoptions, and pedagogical experiments.
The Key Leadership Question
When we focus only on tools, we risk missing the deeper work, so let’s go back to that first question: Who are we becoming as we use and integrate AI? We should ask ourselves: Will we design systems that prioritize efficiency over formation? Will we move faster at the cost of depth? Will we solve problems while unintentionally reshaping what it means to learn, to teach, and to lead?
Every decision we make about AI carries an implicit answer to these questions, whether we name it or not. I think this matters deeply. If we get this wrong, if we adopt AI in ways that are not consistent with our institutional mission and values, we will not just miss an opportunity, we will have allowed our institutions to be shaped by a logic that contradicts everything that we say we value.
So, here we stand, at the threshold between the known and the unknown, the familiar practices and emerging possibilities, clarity and uncertainty. I have witnessed this threshold in faculty and student conversations about AI. In a conversation with a group of faculty members, one noted that while they had been teaching for years and felt confident in their abilities, now they often feel like a beginner. A student noted that the future career they had dreamed of seemed destined to be changed dramatically by AI. I think that these concerns capture an important thing that many of us are feeling right now, AI doesn’t just challenge our skills, it challenges our sense of competence, our professional identity, and our place in the work that we love and have given our lives to or were hoping to do!
How AI Tests Each Commitment
I’ve been thinking about how AI tests each of the abiding leadership commitments, and how we can help ourselves gain clarity in the midst of those tests. Below I’ll share what I see as the test for each commitment, some reflections on my own experience, questions to ask ourselves as leaders, and some ideas for what this could look like in practice.
Commitment 1: We focus on the mission before we move forward
The Test: When the pressure is to adopt quickly, to keep pace with peer institutions, respond to student or employer expectations, or to demonstrate innovation, do we pause to ask what our mission requires at this moment?
When we were considering an enterprise system at Lipscomb, I knew it was really important for our faculty to have a voice in the decision. Our core values are centered on respecting all, and our community values collaboration. I found that it was critical that the faculty members had the chance to try different tools and then make a recommendation to me about their preference. They were able to identify a tool that felt right to them, that allowed for experimentation with multiple large language models, and that provided security and stronger environmental protections. All of these things mattered deeply to our community, so they should inform the choice the institution made about an enterprise tool.
Questions to Ask:
Does this tool serve who we are called to be, or just what we want to do faster?
Are we adopting AI because it aligns with our education philosophy, or because we fear being left behind?
What would it look like to let mission, not momentum, guide our AI decisions?
What this Looks LIke in Practice: Before piloting a new AI tool, gather your team to ask: “If we had never heard of this technology, what would our mission call us to prioritize in this area?” Then evaluate the tool against that answer. Before choosing a tool, make sure that a variety of voices are heard and reflected. Give your community a chance to try things and weigh in on big decisions.
Commitment 2: We design for belonging, not just performance
The Test: As we integrate AI tools, are we creating environments where people can engage without fear, or are we widening the gap between early adopters and those who hesitate?
I found this really important both when we were launching our AI implementation with faculty and staff and when we did so with students. People who have concerns or feel overwhelmed need the chance to be heard and acknowledged. They need to know that we care, and they need to be offered a community of support. We also need to provide opportunities for community members to share what they’re learning with one another, demonstrating that this work is something we can do together, not something they will have to navigate alone.
Questions to Ask:
Who is being left out of AI conversations because they intimidated or skeptical?
Are our AI training opportunities designed for belonging, or do they assume a baseline comfort that not everyone has?
How are we honoring the legitimate concerns of those who are cautious, rather than dismissing them?
What this Looks Like in Practice: In faculty development sessions, AI Faculty Design Labs, and pedagogy groups, and even in introducing AI to our students, we learned that we needed to begin not with tools, but with discussing fears and concerns. I even did a recap for faculty of the ways many different technologies had disrupted education, in order to give us a broader context. Reading about the industrial revolution and the associated fears and changes was eye opening for a lot of us. We want to name and honor those realities, creating space for people to admit that they don’t understand something yet. Our goal here is that belonging precedes adoption, and that efforts at connecting come along with educating.
Commitment 3: We hold rigor and care together
The Test: Can we hold expectations for AI literacy while honoring the very real anxiety or concern that people feel? Can we challenge our community to engage deeply with these tools while caring for those who are struggling?
I’ve realized that often when folks feel frustrated, it’s because they are missing something. For example, that student who is worried about their future job may need information about what may come next in their field. That faculty member who is resisting may need support and practice with using a tool. Before you get frustrated, find a way to offer care and support so that your team can move forward together.
Questions to Ask:
Are we expecting people to adopt AI without having provided them with adequate support, training, or time?
Are we so focused on care that we’re avoiding the necessary rigor of learning new skills?
How do we stretch people toward growth while supporting them through the discomfort of challenge?
What this Looks Like in Practice: Set clear expectations for AI engagement while pairing those expectations with robust support structures, like mentoring, cohort learning, permission to experiment and fail. You can’t just provide tools, you have to provide context and training, and provide them continuously. When we were introducing an AI literacy assignment into our first year seminar, we created a a lesson plan for all faculty use that first informed students about what AI is, then provided opportunity to explore the ethical questions around it. We realized through early experience that if we didn’t do this first, many students didn’t have the context to engage with an AI literacy assignment.
Commitment 4: We Model the Transformation We Seek
The Test: Are leaders learning alongside their teams, or simply delegating AI to others while remaining distant? Are we modeling the curiosity, humility, and willingness to change that we are asking of others?
This was a big one for me. I am not teaching right now, so figuring out how to engage with real curricular work and AI was important so that I could understand what the faculty where having to wrestle with. My solution was to attend as many of the AI pedagogy group meetings as I could, spend time listening to faculty, and asking a lot of questions about assignments, assessment, and challenges. I have loved learning from fellow faculty members about what what they were learning and how they were adapting their assignments, assessments, and courses.
Questions to Ask:
Am I personally engaging with AI tools, or have I outsourced that learning to others?
Do my colleagues see me struggling, experimenting, and growing, or do they see me acting like I already have the answers?
What would it look like to lead from a posture of learning rather than one of expertise?
What That Looks Like in Practice: Share your own AI learning journey publicly. Let people hear about what you’ve tried, both what worked and what you struggled with. Ask questions in meetings that reveal that you’re still figuring things out alongside your team. I remind myself that transformation must be embodied by leaders, so if I think my team should be trying out new tools and testing new ideas, I need to be doing it too!
Commitment 5: We Align Systems with What We Say We Value
The Test: Do our AI policies reinforce our stated core values, or do they indicate that we value something else? Are we building systems that reflect our mission or systems that contradict it?
I feel really good about the work that our AI Task Force at Lipscomb did in laying out how we would approach AI from our Core Values before we did anything else. I believe that was key in being able to center our work with AI on our own institutional mission and values, and then move forward. I encourage everyone to approach AI implementation in this way - center it on the institution’s mission and/or core values. This will guide you in policy development, adoption, and implementation.
Questions to Ask:
What do our AI policies actually reward? Is the focus on speed, efficiency, compliance, or thoughtfulness, integrity, and human connection?
If someone looked only at our AI guidelines, what would they conclude we value most highly?
Where is there a gap between what we say about education and what our AI systems reinforce?
What This Looks Like in Practice: I suggest auditing your AI policies against your institutional mission or core values. Ask yourself, “Does this policy reinforce what we say we value? Does it quietly endorse other values? I’ve found that alignment in this area really requires ongoing attention rather than a one time decision. And, it’s something we’ll have to keep coming back to as systems evolve.
Leadership at the Edge of Change
Earlier in my work, I asked: What if leadership looked like an invitation?
That question feels even more urgent now. I say this because in moments of uncertainty, people are not just looking for direction. Instead, they are looking for the space to ask questions, permission to not have immediate answers, and reassurance that they are not navigating change alone.
This is where hospitality becomes not just a value but a leadership practice. Hospitality, in this context, is not about providing a comfortable environment, but about creating space where people can engage the unknown without fear.
I saw this in our AI Faculty Design Labs and pedagogy communities, where faculty came together not as experts, but as learners. They shared early attempts, questions, and concerns and explored together. Our work as a community did not begin with certainty, but with an invitation to explore and learn together. We’ve been thinking together about how we create this same kind of experience for our students. We want to create a community of learners, questioners, explorers, who can move forward together.
Resisting the Rush to Resolution
One of the greatest temptations in this moment is to move too quickly to try to close the gap between the known and the unknown. But as I’ve written before, transformation does not happen when we strive to quickly escape uncertainty, it happens when we remain in it long enough to be formed by it. There is a special kind of leadership required in this moment, one that is discerning and reflective. In this moment, we need leaders who can linger without panicking in the space between what has been and what is becoming.
I remember in our meetings to finalize an AI policy, instead of rushing to resolution, we named what we knew, what we didn’t know, and what we needed to learn before deciding. We identified the front line users who needed to be in the room to give us meaningful feedback and guidance. We set a timeline that honored both urgency and discernment. We could have moved faster, but then our policy wouldn’t have been fully informed by our community, and that engagement has been critical in forming trust and buy in as we move forward together.
Designing for the Future We Cannot Yet See
As institutions, we are not simply adopting tools, we are designing environments. And we must be aware that these environments will shape how students learn, how faculty teach, how communities engage, and how decisions are made. This means the work before us is not only technical but is deeply human. If we want to preserve formation, we must design for it, if we want to sustain belonging, we must structure for it, and if we want to cultivate courage, we must create the conditions where it can emerge.
I have talked with other administrators, faculty, and staff, and I have seen institutions adopt AI in ways that fracture trust. For example, policies may be created without input, or tools adopted in isolation and implemented without training, or expectations raised and then no support provided. I don’t think the technology itself is really the problem, I think the posture of the leadership is the problem. And in those instances, the cost is not just inefficiency, it can be the erosion of trust and the very culture that the institution claims to value.
As you reflect on your own leadership in this moment, consider:
Where am I feeling pressure to move faster than clarity allows?
Where is fear present in my team, my institution, or my own thinking?
How might I create space for others to engage this change with curiosity rather than caution?
How do we help people navigate the identity disruption that AI creates along with the skill disruption?
What Comes Next
This essay begins a new exploration: What does it look like to lead with the abiding leadership model, focused on mission, formation, and courage, in an age shaped by artificial intelligence? In the coming weeks, I’ll explore questions like:
What should never be automated?
How do we design AI-integrated environments with hospitality?
What are our systems teaching as we adopt new tools?
How do we attend to the human skills and needs of those in our community as we implement AI into our systems?
It’s vital to understand that the future will not be shaped by technology alone. It will be shaped by the leaders who decide how it is used, and why.
Over the past two years, I have led dozens of conversations about AI with faculty, staff, and students. I have watched fear show up in predictable ways, and I have seen what happens when leaders respond with presence rather than pressure. What I’ve learned is that technology is not the most important thing to consider, rather, I needed to be thinking about the uncertainty of the community, the human feelings and concerns involved, and the path forward centered on the institution’s mission and identity.
Here’s something that I am certain about: the future before us is unmade, and it is being created by every decision we make about how AI enters our institutions. That can feel unsettling. But I’ve come to understand that it is also an invitation to lead with intention, to remain grounded in our calling and purpose, and to shape what comes next with clarity and care. So, it’s become clear to me that the work of abiding continues. Only now, it meets us at the edge of something new, and I believe that edge between the known and the unknown is exactly where leadership matters most.