AI Won't Simplify Your Work—It Will Intensify It

New technology has always changed how we work. The internet changed how we interact. Search engines changed how we find information. Cell phones changed how we communicate. AI is changing how we produce.

And if you're expecting that to make work easier… you might be in for a surprise.

AI Doesn't Reduce Work—It Changes It

One of the biggest lessons I've learned—and heard other early adopters discuss—is this: AI doesn't simplify work. It intensifies it.

Sure, it can generate content faster. It can summarize, brainstorm, and draft entire strategies. But it can also lead you down rabbit holes, mislead you, even dupe you. The most effective users of AI know where their expertise is, and more importantly, where it isn't. You have to challenge a bot. Force it to verify. Ask it follow-up questions. Make it explain itself. That's what it takes to get meaningful output from all those costly tokens.

I run multiple AI systems at once—different tools, different tasks, different conversations happening in parallel. And honestly, it reminds me of my days managing Security Operations Centers. You've got people at your desk, someone in chat, someone else in email, all asking questions on different topics at the same time. Swap "people" for "bots" and the feeling is remarkably similar. Even though AI isn't human and isn't (yet) prone to human feelings, the intensity of managing all of it is real. It's something anyone using bots on a daily basis needs to learn to manage.

You don't get less work. You get a different kind of work.

The Bigger Problem: You Can Now Produce Way Too Much

The second challenge is one I still struggle with: AI makes it ridiculously easy to overproduce.

Need a report? Done. Need five variations? Also done. Need a completely different direction? Why not ten?

Before AI, producing content took time. That naturally forced prioritization. Now the bottleneck isn't production anymore—it's judgment.

To understand why that matters, it helps to think about the DIKW model—a hierarchy that grew out of a 1989 paper by Russell Ackoff titled "From Data to Wisdom". The basic idea:

• Data is raw facts.
• Information is data that's been processed, structured, and organized to reveal patterns and context.
• Knowledge is information with enough context to actually apply somewhere.
• Wisdom is knowing what matters and when to act on it.

AI is fantastic at turning data into information—and in some cases, knowledge. But wisdom? That's still on us humans.

The real risk now isn't drowning in data. It's drowning in the information and knowledge that's been made available, digestible, and served up as a never-ending feast. You and your team will be able to create and produce so much faster that it becomes vital to ask the right questions before you start. And that starts with asking why. 

So What Do We Do About It?

The solution to both of these challenges depends on strong human leadership and strategy—not better AI.

Slow Down. Seriously.

When everything speeds up, your instinct is to go faster. Resist that.

If you feel overwhelmed, that's not a signal to produce more. It's a signal to pause. There's a phrase people use—"touch grass"—and it sounds funny, but I've seen the idea behind it play out in every operations center I've ever worked in. Stop. Take a breath. Ask yourself and your team what the current challenges and priorities actually are. Some things can wait. The great thing about bots is they're not going to get mad if you don't respond in five minutes.

Talk to Humans

This one sounds obvious, but it's easy to forget when you're deep in AI workflows.

In the workplace, this means making sure you and your teams are syncing, talking, and communicating—not just prompting. It means facilitating real conversations in team meetings. This is what pulls people out of rabbit holes and grounds them back to what the priorities are. If your team isn't regularly aligning, you'll end up with five people using AI to produce ten different directions—none of which line up.

AI scales output. Communication keeps it relevant.

Be Ruthless About Priorities

Just because you can produce something doesn't mean you should. Before you generate anything, ask:

Why am I doing this? 

Who is this for? 

What decision or outcome does this support?

If you can't answer those quickly, don't start generating yet. AI rewards clarity. It punishes vague thinking with a lot of very polished nonsense.

Lead With Strategy, Not Tools

This is the big one—and where strong leadership is absolutely required for any organization that wants to adopt AI as a producer.

Leaders need to outline a strategy. Not just a goal—a strategy they want to implement. Production must be focused on directly applying, implementing, or supporting that strategy. Your team shouldn't just be "using AI." They should be using AI to move something specific forward. Otherwise, you're just creating more work faster and spending tokens.

Final Thought

AI is changing how and what we produce. But good human communication and planning still outperform many of our most advanced bots—for now—and this is where leaders need to focus their energy.

Everything may be changing on the production side, but long-term planning, strategy, and clear communication remain the key to adding value and context to data. Knowing when to apply it is how we find wisdom.

And in a world where you can create anything, the real skill is knowing what's worth creating.

 

- Written By Ben Tolen & edited by AI. Check me out on Linkedin.