The AI‑Ready Workforce Starts with Real Work

There’s no shortage of conversation about AI right now—strategy, disruption, long‑term impact. Most of it is forward‑looking. What’s getting less attention is what organizations are dealing with today: employees are already using AI, and leadership has to decide whether that happens intentionally or not.

From my perspective, the AI‑ready workforce isn’t something you declare after a training program. It’s something you build over time by putting AI into real work, setting clear expectations, and learning what happens.

At our institution, we’ve taken a practical approach: structured AI pilots focused on administrative operations. The goal is straightforward—understand how AI fits into daily work before trying to scale it.

Start Where the Work Is

AI doesn’t enter the organization in big, strategic ways at first. It shows up in routine work: drafting, summarizing, searching, pulling information together.

That’s why we focused our pilots in areas where that type of work is constant:

These are high‑volume, high‑touch functions. People are working with information all day, and in many cases interacting directly with students or staff.

The pilots aren’t about rolling out AI broadly. They’re about learning—where it helps, where it doesn’t, and how people adapt their work when it’s available.

Readiness Comes from Experience

We’ve done the awareness sessions and introductions to AI. Those are useful, but they don’t make someone ready.

Readiness comes from using the tools and seeing where they hold up—and where they don’t.

Across areas like Advising and the Welcome Center, staff have picked this up quickly. They learn to question outputs, adjust responses, and recognize when something doesn’t sound right.

That’s the real shift. AI doesn’t remove accountability. If anything, it reinforces it.

That kind of judgment develops through experience, not training alone.

Guardrails Still Matter

We didn’t leave this open‑ended. Early on, we put some structure in place:

That level of clarity makes a difference. Without it, people either hesitate to engage or use tools in inconsistent ways. With it, they can move forward with some confidence.

At the same time, those guardrails aren’t final. They continue to evolve as we learn more from actual use.

Trust Becomes the Limiter

As long as AI is helping draft or summarize, adoption comes fairly easily. As soon as it starts supporting analysis or recommendations, trust becomes the issue.

We’ve seen that most clearly in Research Analytics & Reporting. AI can speed up insight generation, but if the output can’t be explained or validated, people won’t rely on it.

That’s where transparency becomes important—not as a technical feature, but as a practical need. People have to be able to understand what they’re looking at and decide whether it makes sense.

The Work Is Changing

A lot of the conversation around AI still focuses on job loss. What we’re seeing is more of a shift in where time is spent.

People are spending less time drafting or searching and more time on:

• Handling exceptions
• Working directly with students
• Making judgment calls
• Thinking about how processes could improve

The work is still there. It just looks different.

And in many cases, it raises expectations. You need people who can apply judgment, not just follow a process.

Governance Is Becoming Practical

One of the more encouraging changes has been how governance is showing up in daily work.

People are starting to ask the right questions:

• Should I use AI here?
• Is this appropriate data?
• How should I review this output?

Those questions aren’t coming just from IT anymore. They’re coming from areas like HR and Advising.

That’s an important shift. It means responsible use is becoming part of how work gets done, not something handled separately.

Different Work, Different Approaches

We’ve also kept a clear distinction between administrative AI and academic AI.

Our teaching and learning efforts focus on faculty use, student engagement, and instructional models. That environment benefits from flexibility and exploration.

Administrative operations require more consistency and accountability. Trying to treat those the same doesn’t work.

Each has its place, but they need different approaches.

What This Means Going Forward

AI readiness isn’t something you wait for. It develops as people begin to use these tools in real situations.

What seems to work is a fairly simple approach:

• Start with real use cases
• Provide enough structure to guide behavior
• Let people learn through experience
• Adjust based on what actually happens

It’s not overly complicated, but it does require attention and follow‑through.

The workforce becomes AI‑ready the same way it has adapted to every other shift—through use, expectations, and accountability.

The difference this time is just how quickly it’s happening.

About the Author

Scott McCollum is the Chief Information Officer at Sinclair College in Dayton, Ohio. He leads enterprise IT strategy, cybersecurity, and digital transformation initiatives, with a focus on practical and governed adoption of emerging technologies.