AI Over 40 Series - Week 15: Why Agentic AI Isn't Solving Your Problems

When every demo looks impressive—but none of them solve your real problems

In October, I attended the Dynamics Community Summit, where I delivered a Premier Sponsor Spotlight on AI. Before my session, though, I sat through presentation after presentation focused on agentic AI—vendors showcasing autonomous systems, consultants outlining theoretical frameworks, and everyone promising that AI agents are about to transform business as we know it.

The demos were polished. Some were genuinely clever. But as I watched them, one thought kept surfacing: Is this solving the problems my clients are actually struggling with?

After my session, I spent time talking with customers at our booth. Over and over, I heard the same frustration expressed in different ways: “We keep seeing agentic AI demos, but they don’t address the broken processes we deal with every day. These examples don’t connect to our reality.”

That’s when it became clear to me: vendors aren’t failing because they’re building bad demos. They’re failing because they’re answering the wrong question.

The question everyone is really asking

“What are the agentic AI use cases that solve our real problems?”

IT leaders, business executives, and consultants all want a concrete example that shows how autonomous AI agents will finally fix the operational issues that have lingered for years. The assumption is simple: if we could just see the right use case, we’d know how to deploy agentic AI ourselves.

But after three days of conversations, I realized something uncomfortable. The reason these use cases don’t match real-world problems isn’t technological at all. It’s because the problems most organizations live with can’t be solved by adding better technology—agentic or otherwise.

We’ve never had a technology shortage

Most organizations already have more automation technology than they know how to use. Workflow tools that only handle basic routing. Power Automate sitting idle inside Microsoft 365. Maybe even RPA platforms from earlier initiatives.

Yet broken, manual, inefficient processes persist everywhere.

Why? Because technology has never been the real blocker.

For more than 30 years, the same barriers have quietly defeated process improvement efforts—and agentic AI doesn’t remove any of them.

The seven barriers that stop real improvement

These barriers are familiar, even if we rarely name them.

1. We normalize broken processes and stop seeing them as problems.
2. We can’t clearly articulate how work actually gets done.
3. No one owns processes end-to-end across departments.
4. Exceptions dominate the work, defying clean automation.
5. Fixing things feels too expensive or too disruptive.
6. We lack imagination about what’s possible.
7. And finally, everything feels “good enough” to avoid action.

Agentic AI doesn’t magically overcome these realities. In fact, it often avoids them. That’s why vendor demos feel disconnected—they’re carefully chosen to exclude ambiguity, politics, ownership gaps, and messy exceptions.

They’re not bad use cases. They’re just not your use cases.

Why AI literacy matters more than AI Agents

This is where AI literacy becomes far more valuable than autonomous agents.

When you use AI as a thinking partner instead of a replacement worker, something changes. You begin to see the barriers you’ve been working around for years. AI can help you inventory frustrations you’ve normalized, clarify processes you struggle to explain, surface ownership gaps, and categorize exceptions that feel overwhelming.

It can help you prototype ideas cheaply, explore solution approaches you didn’t know existed, and even quantify the hidden cost of “good enough.”

That’s not agency. That’s literacy.

AI literacy doesn’t automatically fix broken processes. What it does is help you understand them well enough to decide what’s actually worth fixing—and how.

Sometimes the answer is automation. Sometimes it’s better documentation. Sometimes it’s clearer ownership. And sometimes it’s accepting the reality of exceptions instead of feeling guilty about them.

Literacy before agency

Agentic AI will eventually transform how organizations operate. The technology will mature. Platforms will improve. Capabilities will expand.

But the barriers won’t disappear on their own.

If you can’t define your processes today, you won’t be able to delegate them to AI agents tomorrow. If no one owns work end to end now, AI won’t resolve organizational politics later. And if you haven’t invested in fixing high-value problems with simpler tools, you won’t suddenly prioritize them just because “agentic AI” is the buzzword of the moment.

The organizations that succeed won’t be the ones rushing to deploy agents. They’ll be the ones building AI literacy—learning to see their problems clearly, understand the barriers behind them, and choose the right tools for the job.

Often, those tools won’t be AI agents at all.

This week’s challenge: Start small and personal

Pick one barrier that resonates with your own work. Identify a single process you control that’s stuck behind it. Then spend 30 minutes with an AI tool exploring why that barrier has kept it broken.

Don’t aim to fix the organization. Aim to understand your own work better.

That’s how literacy develops. And that’s where real transformation begins.

This post is part of my “AI Over 40” series. It first appeared on LinkedIn: AI for the Over 40 [Week 15]: Why No One Can Show You How Agentic AI is Going to Transform Your Business.

Next Up: Why No One Can Show You How Agentic AI is Going to Transform Your Business.

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