AI-Enabled Isn’t the Same as AI-Busy
By Claire L. Brady, EdD
This post is part of a series exploring Achieving the Dream’s Creating the AI-Enabled Community College framework through a leadership lens. While ATD is focused on community colleges, the report’s core questions about strategy, governance, culture, workforce readiness, professional learning, and student success apply across institution types. Across the series, I’ll examine what ATD’s eight action areas look like in practice and what it takes to move from scattered AI activity toward intentional, sustainable institutional change.
There is no shortage of AI activity across higher ed right now. Colleges are forming task forces, revising policies, purchasing licenses, offering workshops, testing chatbots, and encouraging faculty and staff to experiment. On many campuses, it can feel as though something new is launching every week.
That activity matters. It reflects curiosity, urgency, and a growing recognition that AI will affect nearly every part of institutional life. But activity alone does not make a college AI-enabled.
That distinction is one of the most valuable contributions of Achieving the Dream’s Creating the AI-Enabled Community College. Rather than treating AI as a stand-alone technology initiative, the report places it within the broader work of whole-college transformation. It asks institutions to consider how AI can strengthen student success, workforce readiness, operational effectiveness, equity, and community impact—not simply how many tools they can deploy.
The difference may sound subtle, but it is significant. An AI-busy institution can point to a growing list of projects. An AI-enabled institution can explain how those projects fit together, what institutional priorities they advance, who is responsible for sustaining them, and how the college will know whether they are making a meaningful difference.
Activity Can Look a Lot Like Progress
I see this gap often in my work with colleges and universities. A task force has been formed. Guidelines have been drafted. A few enthusiastic faculty members are experimenting. IT is reviewing enterprise tools. A workshop drew a large crowd. Several pilots are underway. By almost any measure, the institution is active.
Then the questions become harder. What specific problems is the institution trying to solve? How were those problems prioritized? How do the pilots connect to the strategic plan? Who decides whether a successful experiment should be scaled? What happens when the task force completes its charge? How will the institution determine whether an initiative improved student learning, reduced employee burden, strengthened access, or simply added another tool to an already crowded environment?
This is where activity can begin to reveal its limits.
ATD’s framework makes clear that becoming AI-enabled requires much more than a portfolio of disconnected projects. Its core principles include institutional culture, strategic alignment, operational integration, talent development, ethical governance, continuous experimentation, and ongoing assessment. Those are not simply technology functions. They are institutional capacities that must work together over time.
That idea closely aligns with one of the central arguments in AI with Intention: meaningful AI adoption is not primarily a technical challenge. It is organizational and deeply human work. It requires leaders to build trust, create shared understanding, clarify decision-making authority, invest in people, and confront difficult trade-offs before technology moves from experimentation into daily institutional practice.
A policy can be thoughtful and still sit disconnected from procurement. A pilot can be successful and still have no path to scale. Professional development can attract hundreds of participants without changing how work is done. An enterprise license can be widely available while employees remain unsure when, why, or whether they should use it.
Each of those efforts has value, but they become institutional progress only when the connective tissue is present. Strategy informs priorities. Priorities guide investment. Governance clarifies authority and boundaries. Procurement reflects institutional values. Professional learning builds the capabilities people need. Assessment determines whether the work is advancing the mission. Without those connections, colleges can remain extremely busy while making surprisingly little durable progress.
Build for the Institution You Actually Have
There is another challenge hidden inside the rush to become AI-enabled: institutions often design AI strategies for versions of themselves that do not actually exist. The plans assume abundant capacity, smooth collaboration, easy data sharing, high trust, and enthusiastic adoption across departments. When implementation runs into shared governance, exhausted staff, competing priorities, institutional silos, or uneven readiness, leaders may conclude that people are resistant or that the technology has failed.
Often, the real problem is strategic mismatch.
In my book “AI with Intention”, I argue that institutions have to design for the college they actually have—not the one they wish they had or the one described in a board presentation. If staff are already overwhelmed, launching five simultaneous pilots is unlikely to build confidence. If trust is low, a mandate will probably deepen skepticism. If departments have struggled to collaborate for years, an AI initiative that depends on immediate cross-functional cooperation is not likely to repair those relationships on its own.
An AI-enabled college does not need to eliminate all of those challenges before moving forward. It does need to see them clearly and design accordingly. That may mean beginning with one unit where there is genuine capacity, selecting a problem people actually want solved, involving stakeholders early, and building a credible proof point before expanding. It may look less impressive than a sweeping institution-wide announcement, but it is far more likely to produce sustainable change.
The Goal Is Not More AI
Most importantly, becoming AI-enabled does not mean maximizing the amount of AI used across the college. It means building the institutional judgment to determine where AI creates meaningful value, where it introduces risk, where it should augment human work, and where it does not belong.
ATD emphasizes that an AI-enabled college should be more efficient and productive, but its larger purpose is to become more student- and community-centered. That distinction should shape how leaders define success.
The most important question is not, “How much AI are we using?” It is, “What are we now able to do better because we have built the capacity to use AI wisely?”
Perhaps advisors have more time for complex conversations because routine documentation has been reduced. Perhaps faculty can devote more attention to meaningful feedback because administrative tasks are easier to manage. Perhaps students can get answers to routine questions outside business hours while still reaching a person quickly when their circumstances require judgment or care. Perhaps institutional researchers can spend less time fulfilling isolated data requests and more time surfacing insights that shape institutional decisions—an opportunity ATD specifically identifies.
These are not simply technology outcomes. They are mission outcomes.
As I write in AI with Intention, the opportunities worth pursuing are those that remove friction so people can focus more fully on what humans do best: teaching, learning, mentoring, exercising judgment, building relationships, and supporting students. Efficiency matters, but its value comes from what it makes possible.
For leaders trying to determine whether their institutions are becoming AI-enabled or simply AI-busy, I would begin with a few practical questions:
Can you explain which institutional priorities your AI efforts advance?
Can you trace a clear connection among strategy, governance, procurement, professional learning, experimentation, and assessment?
Do people know who owns the work once the current task force ends?
Are you measuring meaningful outcomes, or primarily counting licenses, workshops, pilots, and logins?
Can your institution articulate where it would intentionally choose not to use AI?
That last question may be one of the most revealing. AI wisdom is not demonstrated by finding a use for the technology everywhere. It is demonstrated by knowing when a technically possible solution does not serve the mission, the people, or the relationships that matter most.
From AI Activity to Institutional Capability
ATD’s eight action areas make clear that becoming AI-enabled is whole-institution work. Strategic leadership, ethical governance, assessment, staff capability, faculty engagement, curriculum redesign, workforce alignment, and student success cannot remain separate conversations indefinitely. They have to become parts of a coordinated institutional approach.
The goal is not to build the college with the most AI. It is to build a college capable of making thoughtful, mission-aligned decisions about AI as the technology, the workforce, and student needs continue to change.
AI activity is easy to generate. Institutional capacity is much harder to build—and ultimately, that is what will matter.
A Question for Leaders
If someone asked your leadership team what is different and better for students, faculty, or staff because of your institution’s AI work, what would they say?
If the answer is primarily a list of committees, tools, workshops, policies, and pilots, your institution may be doing a great deal with AI. The next step is ensuring that all of that activity is building something durable.
This image was created using ChatGPT
