As colleagues across campus share the technology dilemmas that are hindering their institutions’ progress into the future, one point comes up frequently:‑Hourly job running the campus? Where to find the time to perform the disconnection‑Edge practice? ”
To understand why we are so busy today, it helps to look back to the 1990s and early 2000s, when higher education faced decreasing government support and increasing reliance on tuition. Universities have always been told to “strip down” and run like a business: increase revenue, manage expenses, and somehow do more with less.
Technology vendors were happy to address this as a pain point and promise great results. They frequently promised that the move online would create huge numbers of paying students with no increase in costs.
As a result, higher education began massive technology spending, which increased year after year. Campuses have tended to follow the lead of vendors and what they offer. Ministries purchased tools to solve immediate local problems. Central IT departments were drawn into the role of security guard and maintenance personnel (a necessary task) but were rarely given the authority to create a coordinated enterprise design.
On most campuses, the majority of time and resources are spent on system maintenance rather than innovation. vastan uncoordinated technology ecosystem – a system acquired in response to local needs, funded from local budgets, justified in local languages, and then quietly stitched together by human labor.
In many ways, we’re still paying the price of the software era in the form of meetings, workarounds, integrations, shadow processes, manual coordination, data silos, conflicting definitions, and more.‑The general idea that “we’ve always done it this way.”
The core of this software‑Era Strategy was not about designing companies. It was to buy tools. We treated technology as a product rather than a service built into the organization’s operating model.
I made a pile of bricks.
Characteristics of the software era
The stacked bricks look impressive until the weather improves. Brick walls are resilient because of the mortar. There is design, standards, ownership, governance, maintenance, and most importantly, shared intent.
Mortars were often missing in higher education. I bought some good bricks. I also bought some expensive bricks. However, without a corporate mortar, each additional brick creates more surface area to maintain and more seams to create problems.
This is the hidden answer to the question of time. Every unmortared brick becomes someone’s meeting, spreadsheet, workaround, and weekend.
Another defining feature of the software era is that technology was often used to digitally replicate existing practices rather than rethink them. The goal was to do the same thing with a new interface.
That approach will fail in the AI era.
era of AI
AI is more than just “software”; it’s something smarter. It’s an accelerator. It amplifies whatever system you feed into: processes, culture, governance, data quality and alignment, or lack thereof. If your The institutional pattern is fragmentation.AI will eagerly increase fragmentation. If the pattern is a coordinated practice, AI will scale it.
Look at a lot of the conversations around AI right now. Instead of thinking, “How do we use this to improve student learning and organizational performance?” we’re putting effort into modifying our tools to fit the workflows of the past decade. Substantive questions aside Quest. Even legitimate concerns, such as integrity, can be a way for institutions to sidestep the deeper work of redesigning their practices to better serve their students.
When the AI era becomes the “software era, but with chat,” we’ll simply add one more block and call it a transformation.
But where should I find the time?
Unfortunately, there are no magic time lockers. This is a matter of priorities and operating models.
Strangely enough, when a crisis occurs, we find time. Cancel the meeting. Re-prioritize. We move money. we make decisions. we do the work. So when we say we’re “too busy,” we’re often talking about something more specific. “This is not a high enough priority to replace other priorities.”
If you want to bring AI to your campus, you need to prioritize it at all levels across your institution, not just one office or one committee. And priorities must be realistic.
AI technology is changing rapidly, but corporate strategy is not a one-year decision. Thinking from 5 points of view‑Strategy for the year. Attempting to force corporate change into a single budget cycle will make pilots permanent, tools substitute for policy, and committees substitute for decisions, as higher education is known for.
The goal is not speed. The goal is direction, alignment, and sustaining momentum.
Here is my list of recommendations on how to achieve enterprise AI adoption in manageable steps.
Treat AI as a capability, not a procurement
Understand that this will take time and that there will be pushback if you try to force it. AI is not yours install. This is a set of capabilities to develop and manage, including data-ready, process-ready, policy-ready, people-ready, and culture-ready.
Yes, I will buy the tools. But tools are downstream of intent. Tools come and go. abilities are compounded. But if your AI strategy sounds like a shopping list, you’re repeating the software era.
Plan for institutions, not technology.
Where do you want your institution to be in five years? What is the most important student experience? What administrative functions need to be made faster, clearer, and more humane? What outcomes define the success of your campus? Retention, completion, quality of learning, affordability, staff sustainability, research capabilities, trust in compliance, etc.
Only once we can answer these questions should we choose where AI fits.
For your plan to be effective, it should include the following: culture and governancebut that doesn’t mean a new bureaucracy whose job is to decide yes or no. it means a decision‑We create a system that helps educational institutions make the right choices, avoid misalignment, and avoid tool sprawl.
In the age of AI, governance is not a brake. That’s your handle.
Build enterprise AI as a transition, not a lip‑and‑replace fantasy
Recognize that you are moving away from software‑Ecosystems and tools in a fragmented era‑central and silo‑Heavy for AI‑Age-appropriate operating model, i.e. ability‑Central, governed, and consistent.
In other words, order matters. You cannot and should not try to solve everything at once. Choose high quality sets in small quantities‑value Use cases that matter to institutionsThis is manageable and requires building the enterprise capabilities needed later, such as identity, data access patterns, security, model evaluation, change management, and process redesign.
well‑The use cases you choose deliver double the value. Deliver value now and build the infrastructure for the next 10 projects.
Please stop please do it alone
Colleagues and colleagues can help you understand what you need. Consortiums help reduce duplication of effort. We call for discussion among institutions on how best to approach AI. Shared playbooks help you avoid the trap of software obsolescence. And internal partnerships such as IR, IT, academic affairs, student success, finance, legal, accessibility, faculty, etc. are not optional. Work in the AI era is diverse.‑Functional in design. If one office owns the AI, you’ve already lost.
The solution is a change in governance and operating model that treats AI as a corporate priority, supported by five criteria.‑Annual planning — not as a new tool purchase.
By doing so, you can avoid repeating the mistakes of the software era. That’s how we quit busy manufacturing and build campuses that can absorb the future without disrupting processes.
