AI Is the New Electricity for Higher Education

Electricity did not simply give people a faster way to light a room. It reshaped buildings, industries, work, communication, and daily life.

Artificial intelligence may create a similarly foundational shift. It is not one isolated technology that will remain inside a single department or platform. It is becoming a capability embedded across the tools people use to write, analyze, search, communicate, create, decide, and work.

For higher education, the important question is no longer whether AI will affect the institution. It already does.

The more important question is whether institutions will shape that transformation intentionally.

AI is not simply another tool to adopt. It is a new layer of capability that will move through nearly every institutional system.

The first applications are rarely the final transformation.

When electricity first became available, many organizations used it to power existing equipment and replicate familiar processes. The deeper transformation came later, when people redesigned factories, homes, products, and patterns of work around what electricity made possible.

We can see a similar pattern with AI.

Early institutional use cases frequently focus on improving existing tasks: drafting messages, summarizing documents, answering common questions, generating ideas, or analyzing information.

These uses can be valuable. They help people build fluency and understand the technology’s capabilities and limitations.

But the larger opportunity will not come only from completing existing tasks faster. It will come from reconsidering how work is organized, how information moves, how decisions are supported, and how students interact with the institution.

AI will become part of the infrastructure.

Institutions sometimes approach AI as a separate initiative: create a committee, purchase a tool, write a policy, and provide training.

Those actions are necessary, but they may create the impression that AI can be contained within a distinct project.

In reality, AI capabilities are being incorporated into learning management systems, productivity platforms, customer relationship management tools, enterprise software, search engines, analytics products, communication systems, and consumer applications.

This means an institution’s AI environment will not consist of one approved tool. It will include a growing ecosystem of visible and invisible capabilities used by students, employees, vendors, and partners.

Governing that environment requires more than maintaining a list of products. It requires shared principles for how the institution evaluates uses, manages information, protects people, and makes decisions.

AI will change the shape of work.

Discussions about AI and employment often focus on whether a technology can replace a person or eliminate a position. That framing is too narrow for most higher education work.

Roles are collections of tasks, relationships, decisions, expertise, judgment, and responsibility. AI will affect these components differently.

• Some repetitive tasks may be automated or significantly reduced.
• Some analytical tasks may become faster and more widely accessible.
• Some work will require greater verification, oversight, and judgment.
• New responsibilities will emerge around governance, quality, privacy, training, and ethical use.
• Human connection, trust, context, and accountability may become even more valuable as automated content increases.

Institutions should not wait for these changes to arrive position by position. Leaders can begin examining which work creates value, which work creates unnecessary burden, and where employees need new capabilities and support.

Access to AI is not the same as readiness.

Purchasing or approving a tool does not make an institution ready to use it well.

Readiness includes the quality and accessibility of institutional information, the clarity of workflows, the confidence of employees, the strength of governance, and the institution’s ability to evaluate outcomes.

An institution with fragmented information and unclear processes may find that AI exposes or amplifies those weaknesses. A chatbot cannot reliably explain a policy if the institution itself has multiple conflicting versions.

Similarly, employees cannot make responsible decisions about AI if expectations are unclear or if they fear that experimentation will be punished.

Experimentation needs direction.

Institutions face a genuine tension. Moving too slowly can leave students and employees without guidance while AI adoption happens around them. Moving too quickly can create risk, reinforce poor practices, and invest resources in tools without a clear purpose.

The solution is not unrestricted experimentation or complete prohibition. It is structured experimentation.

Institutions can create environments where people test defined use cases, document what they learn, examine risks, compare outcomes, and share practices across departments.

This approach treats experimentation as institutional learning rather than a collection of disconnected individual efforts.

Human-centered does not mean technology-resistant.

A human-centered approach to AI does not reject automation or assume every existing task should remain unchanged.

It begins with the people affected by the system. It asks what they are trying to accomplish, where they experience friction, what support they need, and what consequences could follow from a new design.

Sometimes the human-centered decision will be to automate a task so employees have more time for complex or relational work. Sometimes it will be to provide an AI-supported option while preserving access to a person. Sometimes it will be to decide that a high-stakes decision should not be delegated to an automated system.

Human-centered design is not defined by the amount of technology used. It is defined by how intentionally the institution considers human needs, dignity, agency, and outcomes.

Leadership must move beyond tool selection.

AI leadership will require institutions to connect technology strategy with academic strategy, workforce planning, student experience, organizational design, risk, and institutional mission.

The work cannot belong to one office. Technology leaders understand infrastructure and risk. Faculty understand learning and disciplinary practice. Student affairs professionals understand development, support, and the lived student experience. Operational teams understand the workflows through which institutional promises become reality.

These perspectives must come together.

Electricity changed society not because organizations purchased electrical devices, but because people eventually redesigned the world around a new capability.

AI presents higher education with a similar opportunity. The institutions that benefit will be those that look beyond the tool, understand the systems it will enter, and deliberately shape the future it helps create.