What Writing This Book Revealed- The Knowledge Was Already There

A note about this series: I'm finishing a book on AI leadership for higher education—not a technical guide or a futurist manifesto, but practical guidance for leaders making real decisions under real pressure. As I prepare to send the manuscript off, I'm reflecting on what the writing process taught me. This blog series shared over the next few weeks shares those reflections.

by Claire L. Brady, EdD

One of the biggest surprises of writing this book wasn't discovering what I didn't know—it was realizing how much I already did.

Over the past several years, I've collected an enormous amount of practical, tactical knowledge through my consulting, training, and coaching work. Client stories documenting what happened when institutions got AI governance right—and what happened when they didn't. Decision points where leaders chose between competing values and their choices revealed institutional character. Successes that looked small at the time but proved foundational. Missteps that seemed catastrophic but became valuable learning. Language that helps leaders name what they're seeing on their campuses when technical jargon fails them. Frameworks that bring clarity to complexity without oversimplifying the real messiness of organizational change.

I hadn't fully appreciated just how much was there until I started writing this book.

The knowledge wasn't scattered or fragmentary—it was coherent. It had patterns. Themes kept emerging across different institutions, different challenges, different leadership contexts. The provost at a small liberal arts college facing faculty resistance was asking fundamentally the same questions as the CIO at a large research university trying to scale pilots. The issues that derailed implementations at community colleges were structurally similar to the ones causing problems at flagship institutions. The success factors were surprisingly consistent across institutional types and sizes.

What I needed to do wasn't invent insight from scratch. I needed to notice what was already there, name it clearly, and organize it so others could use it.

This realization was liberating. It meant I wasn't trying to be an oracle predicting AI's future or a technologist explaining how algorithms work. I was doing what I do best: seeing patterns across institutions, translating complexity into clarity, and helping leaders make better decisions with the messy, incomplete information they actually have.

What didn't surprise me was how much I enjoyed the act of writing itself. I loved writing my dissertation—truly loved it—and that joy came back quickly. The satisfaction of finding exactly the right word. The challenge of explaining a complex concept clearly enough that a busy president could grasp it immediately. The craft of structuring an argument so it builds momentum and carries readers toward insight rather than just information. The occasional perfect sentence that captures something important with unexpected precision.

I missed this. And I was grateful to be back in it.

But what was different this time was the rhythm. My dissertation was written with daily intensity and a tightly structured timeline. Three years of focus, then it was done. This book came in waves—periods of deep focus followed by stretches of low momentum, pauses dictated by client work, travel, and life's inevitable interruptions. 6 months in the making and it is done.

There were stops and starts. Quiet months where the manuscript sat untouched while I traveled to campuses or facilitated workshops. High-intensity sprints where I'd write three chapters in two weeks- usually on planes or in airports. Periods of doubt where I wondered if the book would ever feel finished. Moments of clarity where everything suddenly made sense and the structure I'd been struggling with revealed itself.

The writing didn't follow a neat linear path. It looped back on itself. I'd write a chapter, then realize three chapters later that I'd missed something essential and need to go back and rebuild the foundation. I'd outline a section one way, write it, hate it, and restructure it completely. I'd think I was nearly done, then realize I needed an entirely new chapter to bridge a gap I hadn't seen before (purchasing- who knew?)

And that felt fitting. AI work in higher education doesn't move in a straight line either. It accelerates, stalls, loops back, and demands reflection as much as action. Institutions make plans, encounter unexpected resistance, adjust course, learn from mistakes, try again differently. The messy non-linearity of the writing process mirrored the messy non-linearity of the work itself.

What sustained me through those uneven periods was the growing clarity that this book wasn't abstract. It wasn't theoretical frameworks imported from other contexts or speculative predictions about technology's future. It was anchored in real institutions, real leaders, and real decisions. The president who chose to delay an AI implementation to fix accessibility issues despite board pressure to move fast. The faculty senate that transformed from skeptical resistance to thoughtful governance. The CIO who discontinued a tool that was working efficiently but undermining equity. The advisor who learned to use AI to amplify rather than replace their expertise.

These weren't hypothetical scenarios designed to illustrate points. They were lived experiences shared with me in confidence, offered generously so others could learn from them. And writing became a way of honoring those experiences—of making sense of the collective learning that was already happening across campuses, often invisibly, and making it visible to a broader community.

The book asked me to be a curator as much as a creator. To gather the wisdom that already existed in the field—in the leaders navigating this work with integrity, in the institutions getting it right, in the students whose experiences were teaching us what AI in service of teaching and learning actually looks like—and organize it so others could access it.

I'm deeply grateful for that role. Not because it was easy—it wasn't—but because it felt true to the work. I don't have all the answers about AI in higher education. No one does. But I've been privileged to witness a lot of institutions grappling with hard questions, and I've learned to see patterns in how the good answers emerge.

This book is my attempt to share what I've seen. To say: here's what seems to matter, here's what tends to work, here's what usually fails, here's how you might think about the choices you're facing. Not as prescription but as pattern recognition. Not as certainty but as hard-won clarity.

And the surprise wasn't that I had to generate that clarity from nothing. The surprise was that it was already there, waiting to be noticed and named. I just had to pay attention and write it down.

Stay tuned later this spring for the release of my first book. I am so proud and ready to introduce this incredible resource into the world!

Once I press 'send' on this manuscript, the next milestone is release—coming later this spring.

I'm ready to see this work move from my hands into yours.