Tech Explained: Time for AI: The ‘too busy’ problem is a software-age hangover in Simple Terms

As on-campus colleagues share the technology dilemmas keeping their educational institutions from advancing into the future, one point frequently comes up: “How do we do AI — enterprise AI — when we already have full‑time jobs running campuses? Where do we find the time to implement cutting‑edge practices?”

To understand why we’re so busy today, it helps to revisit the 1990s and early 2000s, when higher education faced a decline in government support and an increasing dependence on tuition. Colleges and universities were constantly told to “trim the fat” and to run like a business — to increase revenue, control expenses and somehow do more with less.

Technology vendors were delighted to frame this as a pain point and promised wonders. They frequently promised that moving online would generate huge numbers of paying students with no increase in cost.

As a result, higher education began a massive technology spend that increased year over year. Campuses tended to follow the vendors’ lead and what they offered. Departments bought tools to solve immediate local problems. Central IT was pulled into the roles of security guard and maintenance crew — necessary work, but it was rarely empowered to do coordinated enterprise design.

On most campuses, the majority of time and resources are consumed not by innovation but by the maintenance of a sprawling, uncoordinated technology ecosystem — systems that were acquired in response to local needs, funded through local budgets, justified in local language and then quietly stitched together with human labor.

In many ways, we’re still paying the bill for the software age in the form of meetings, workarounds, integrations, shadow processes, manual reconciliations, data silos, conflicting definitions and the ever‑popular mindset of “we’ve always done it this way.”

The core of this software‑age strategy wasn’t to design an enterprise. It was to buy a tool. We treated technology as products rather than as services embedded in an institutional operating model.

We created stacks of bricks.

A stack of bricks looks impressive until the weather hits. A brick wall is resilient because it has mortar — design, standards, ownership, governance, maintenance and, most importantly, shared intent.

In higher ed, our mortar was often missing. We bought good bricks. We even bought expensive bricks. But without enterprise mortar, every additional brick creates more surface area to maintain and more seams where problems show up.

That’s the hidden answer to the time question: every unmortared brick becomes someone’s meeting, spreadsheet, workaround and weekend.

Another defining characteristic of the software age is we often used technology to replicate existing practices — digitally — rather than reimagine them. The goal was to do the same thing with a new interface.

That approach will fail us in the AI age.

AI is not simply “software, but smarter.” It’s an accelerant. It amplifies whatever system you feed it: your processes, culture, governance, data quality and coordination — or lack of it. If your institutional pattern is fragmentation, AI will enthusiastically scale fragmentation. If your pattern is aligned practice, AI will scale that.

Look at many current AI conversations. We spend our oxygen trying to bend the tool to fit last decade’s workflows, instead of asking, “How do we use this to advance student learning and institutional performance?” We drown substantive questions in side quests. Even legitimate concerns, such as integrity, can become a way of avoiding the deeper work: redesigning practice so the institution serves students better.

If the AI age becomes the “software age, but with chat,” we will simply add another stack of bricks and call it transformation.

Unfortunately, there is no magic time locker. This is a matter of priorities and operating model.

Strangely, when a crisis occurs, we find time. We cancel meetings. We reprioritize. We move money. We make decisions. We do the work. So, when we say, “I’m too busy,” we are often saying something more specific: “This is not a priority high enough to displace other priorities.”

If you want to infuse AI into your campus, you must make it a priority — at all levels across the institution, not just in one office or one committee. And the priority must be realistic.

AI technology is changing rapidly, but an enterprise strategy does not arrive in a single year. Think in terms of a five‑year strategy. If you try to force an enterprise transformation into a single budget cycle, you’ll get what higher education is famous for: a pilot that becomes permanent, a tool that becomes policy and a committee that becomes a substitute for a decision.

The goal is not speed. The goal is direction, alignment and sustained momentum.

Here’s my recommended list for how to bring about an enterprise AI deployment in manageable steps:

Understand that this will take time, and trying to force it will create backlash. AI is not something you install. It is a set of capabilities you cultivate and govern, and it includes data readiness, process readiness, policy readiness, talent readiness and cultural readiness.

Yes, you will buy tools. But tools are downstream of intent. Tools come and go. Capabilities compound. If your AI strategy sounds like a shopping list, however, you are repeating the software age.

What do you want and need your institution to be in five years? What student experiences matter most? What administrative functions must become faster, clearer and more humane? What outcomes define success for your campus — retention, completion, learning quality, affordability, staff sustainability, research capacity and compliance confidence?

Only after you can answer those questions should you choose where AI fits.

To make the plan effective, it must include culture and governance, but that does not mean a new bureaucracy whose job is to decide yes or no. It means the decision‑making system that helps the institution make the right choices, say no to misalignment and avoid tool sprawl.

In the AI age, governance is not a brake. It’s your steering wheel.

Realize that you are transitioning from a software‑age ecosystem that is fragmented, tool‑centric and silo‑heavy to an AI‑age operating model that is capability‑centric, governed and coherent.

That means sequencing matters. You will not fix everything at once, and you should not try. Choose a small set of high‑value use cases that matter to the institution, that can be governed and that force you to build enterprise muscles you’ll need later — identity, data access patterns, security, model evaluation, change management and process redesign.

A well‑chosen use case pays twice: it delivers value now and builds infrastructure for the next 10 projects.

Peers and colleagues can help you understand what you need. Consortia can reduce duplicated effort. Seek out discussions among institutions on how best to approach AI. Shared playbooks can help you avoid the software age traps. And internal partnerships — IR, IT, academic affairs, student success, finance, legal, accessibility and faculty — are not optional. In the AI age, the work is cross‑functional by design. If one office owns AI, you have already lost.

The solution is to treat AI as an enterprise priority and an operating model shift — backed by governance and a five‑year plan — rather than as another round of tool buying.

That’s how you avoid repeating the mistakes of the software age. It’s how you stop manufacturing busy, and it is how you build a campus that can absorb the future without breaking in the process.