What Gets Me Up in the Morning: Information Management in the Age of AI

People ask me what keeps me excited about information management. It's a fair question—on the surface, organizing records and managing data systems doesn't exactly scream "thrilling career."

But here's what they're missing: we're right in the middle of one of the most transformative periods in the history of information work. And information professionals? We're positioned to be the heroes of this story.

The Opportunity Hidden in the Overwhelm

Organizations everywhere are drowning in data. There's this pervasive feeling of overwhelm—too much information, too many systems, too many decisions to make about technology that's changing faster than anyone can keep up with.

AI is generating even more information. Every interaction, every process, every transaction is creating data. The mass of it all is staggering.

And in the middle of this chaos, information professionals can step up and say: "Look, we can help you. We got this."

That's what gets me up in the morning.

Automating the Repetitive So We Can Focus on the Interesting

Here's what AI and automation mean for our field: we can finally offload all those repetitive tasks that have consumed so much of our time and mental energy.

All those things that you, as a person, thought you had to do manually? Done. Now you can move on to the really interesting stuff.

Instead of spending weeks manually reviewing documents for classification, we can use machine learning to process at scale. Instead of drowning in the backlog, we can use intelligent tools to help us identify what matters and what doesn't.

But—and this is crucial—a human will always need to be in the loop. AI isn't replacing information professionals. It's amplifying what we can do. It's giving us superpowers.

Analyzing Challenges and Opportunities in Technology

What excites me most is analyzing the challenges and opportunities that technology presents to our field. We're not passive recipients of whatever technology vendors decide to build. We're active participants in shaping how these tools actually work in practice.

We get to ask questions like:

• How do we maintain context when AI is categorizing information?
• What does "good enough" accuracy look like for different use cases?
• How do we balance automation with human judgment?
• What are the privacy and security implications of these tools?

These aren't just technical questions. They're philosophical questions about the nature of information, memory, and organizational knowledge. And we get to help answer them.

Brainstorming and Collaborating on How to Organize Information

One of my favorite parts of this work is the collaborative problem-solving. You connect with folks from across the organization, and you discover they have needs and questions. Maybe they didn't even know to come talk to you.

Then you get into this circular conversation where everyone is contributing to a common goal. You're helping them see how they could get better access to information, how they could use it more effectively, where it's located and what other resources connect to it.

It's creative work. It's people work. It's puzzle-solving work. And no two days are the same.

Delivering Information Before People Even Ask

Here's the ultimate goal that keeps me motivated: delivering information to people when they need it, and possibly even before they ask for it.

Imagine a world where the right information surfaces at exactly the right moment in your workflow. Where you don't have to remember to go looking for that policy document because the system already knows you're about to need it based on what you're working on.

That's predictive data delivery. And it's not science fiction—it's where information management is heading when we combine good information architecture with intelligent systems.

The Future Is Multidisciplinary

The challenges we're facing require people who can think across traditional boundaries. We need information professionals who understand technology. We need technologists who understand information science. We need archivists who can code. We need data scientists who understand context and preservation.

There's a reason computational archival science is emerging as a transdisciplinary field. The problems we're solving don't fit neatly into old categories.

And that means there's room for all kinds of backgrounds and perspectives. Your unique combination of skills isn't a liability—it's exactly what the field needs right now.

Why This Matters

At the end of the day, what we do as information professionals has real impact. We help people find answers. We reduce frustration. We make organizations more efficient and more compliant. We preserve what matters and eliminate what doesn't.

In the age of AI, that work becomes even more critical. Because as we generate more data and rely more heavily on automated systems, the principles of good information management—context, access, preservation, organization—become the foundation that everything else is built on.

Get those fundamentals wrong, and your AI initiatives will fail. Get them right, and you unlock incredible potential.

What Energizes Me

So what gets me up in the morning?

Learning new things. Analyzing challenges. Finding creative solutions. Collaborating with smart people across disciplines. Seeing the "aha" moment when someone realizes how information management can solve a problem they've been struggling with.

And knowing that as overwhelming as the information landscape feels right now, we have the tools and the knowledge to navigate it. We just need to step up and use them.

That's what excites me about information management. And I think it's just getting started.