CIO Corner: Six Mistakes Enterprises are Making with Agentic AI

As I’m writing this, I have CNBC and Bloomberg on in the background. Even only half-listening, the tenor of the headlines is unmistakable:

“Total agentic AI funding in 2025 declined by roughly 62% compared to 2024.”

“Wave of AI euphoria that swept over Wall Street last year was unjustified”

“Bank of America survey reveals that ‘AI bubble’ is the biggest concern for credit investors”

“A sharp drop in valuations of leading AI companies would have dire consequences for the US economy.”

The hype curve around AI and agentics is deflating. Rapidly. There’s a new ambiguity in the valuations. What felt inevitable twelve months ago now feels unsteady.

That’s not because the technology isn’t powerful. It’s because the execution hasn’t kept pace with the expectation. We were sold a frictionless future. A promised land of optimization and efficiency gains, where autonomous agents think and act on our behalf.

Instead, many organizations are encountering a messier reality. The output can be extraordinary, but it takes real effort to get there. And we’ve all been making a few false assumptions along the way.

Here are the biggest mistakes I see companies making around agentic AI.

Top 5 enterprise agentic AI mistakes

1. Thinking that it’s easy

When I was seven, I got a 7,500-piece LEGO Millenium Falcon. The picture on the box looked incredible. But when I opened it, the instructions were missing. Imagine my disappointment, frustration, and confusion.

That’s what companies are feeling now as they try to integrate agentics into their businesses. They expected to plug and play. But these platforms require context engineering, integration, guardrails, and governance. Now they’re faced with a pile of parts and no direction.

In the weeks since assembling a dedicated AI team of workflow automation specialists and former Big Three consultants, I’ve seen our pace accelerate. But even with tenured talent it’s still technically demanding. For CIOs, the ambition of the possible must be matched by the discipline of enablement.

2. Optimizing tasks instead of workflows

Another common pitfall is task fixation. Can an agent draft this report? Summarize this ticket? Design this deck? Those are tasks. The true value of agentic AI is realized when it’s embedded across whole functional workflows. And workflows are very different. They contain tasks, but they also contain activities, updates, dependencies, escalations, decision points, and feedback loops. A task is a piston. A workflow is an engine. When we focus only on isolated use cases, we miss the larger operational opportunity. Transformation happens when you redesign the engine, not just grease the parts.

3. Confusing ‘cool’ with ‘valuable’

When an agentics initiative stalls, my first question is always: what are we measuring? If it’s not a leading indicator tied directly to business value, it’s a problem. Internal tool adoption. Number of new automations. Git commits per week. These metrics are shiny and seductive. But they don’t increase margins, reduce risk exposure, or accelerate revenue. Honestly—even with experienced engineers, a robust platform, and the right partners, it’s still hard to know whether we’re building value or just building something cool. And you can’t always rely on the business to know the difference.

4. Neglecting the basics

In their rush to deploy agents, many organizations neglect their data foundation. Before introducing autonomy, ask:

• Is our unstructured data accessible?
• Is it consolidated into a single source of truth?
• Is it protected from cyber threats?
• Has it been curated?
• Are our systems intentionally connected?

Agentic AI is an amplifier. If your data is fragmented, agents will amplify inconsistency. If your data is unprotected, agents will amplify vulnerabilities. If your systems are disconnected, the value of your agents will disappear down the cracks. There is a maturity model here that cannot be bypassed. If you can’t execute reliable enterprise search, you are not ready for agentics.

5. Training without changing mindset

At engineering school, one class divided the room: systems design. Not technology, but logic. Conditional reasoning, if/when statements, do/while loops. Some people could wrap their brains around it. Others couldn’t. It became immediately clear who the engineers were in the room and who weren’t. (The others switched to business school after that semester.)

Agentic AI demands systems thinking at scale. It requires us to see the enterprise as an interconnected whole, not a collection of parts. Instead of optimizing an element in isolation, we must consider the ripple effects of that change—horizontally and vertically. And crucially: we must be ready to redesign the whole. Training alone won’t get us there. You can teach tools, but you cannot rewire mindset in a workshop. And mindset must precede mechanics here.

Transformations begin with people

Transformations begin with people. They end with tech. Not the other way around. So take the tools out of the picture for a moment. Have we adapted how we think, build, and operate? Have we thought outside the box and asked ourselves, “what if?” Or are we simply layering new tech into old mental models?

These questions need to be asked at the level of business DNA. And answering them will take a combined internal effort from the CIO, COO, and CHRO.

I believe that the technology is ready. The real question is: are we?

CIO Corner is the executive lens on what’s next in IT, delivered by Nasuni CIO, Dalan Winbush. With a firsthand perspective from the frontlines of IT leadership, Dalan unpacks what it really takes to modernize infrastructure, harness AI, and lead through complexity. This series tackles the hard questions CIOs face today, such as scalability, resilience, velocity, and value. It’s a candid look at how cloud and AI are reshaping enterprise IT — from someone who’s doing it, not just talking about it.