Enterprise AI has entered a new phase: beyond the hype, beyond the prototypes, and into the hard part—production.

We’ve seen this before with other technologies. The early excitement gives way to tough, often uncomfortable questions. And right now, there’s a widening gap between what many organizations say they’re doing with AI and what’s actually making it into the hands of end users.

This isn’t a warning about AI’s potential. It’s a reality check about its implementation.

There’s a chasm between prototype and production.

Most AI initiatives start strong. The demos are polished. The promise is clear. And leadership buy-in often comes quickly.

But moving from a successful proof-of-concept to a deployed, trusted, and adopted solution? That’s where things break down.

That gap is the chasm. And most organizations aren’t ready for it.

Let’s talk about what’s in it—and the conversations CIOs should be having now.

1. Is our data actually ready for AI?

We all know data quality matters. But many AI projects skip the groundwork. They rely on brittle, siloed data pipelines and assume models can make sense of it all. In reality, even agentic AI and large language models need structured, validated input to be effective.

Some key questions:

• Have we identified the systems and people responsible for stewarding our core data domains?

• Do we know what “good” data looks like for the use cases we’re prioritizing?

• Can our infrastructure reliably supply data to these models in real-time or near-real-time?

The good news: AI can help enhance data. But only if you know what you’re feeding it—and why.

‍

2. Are we including the business early enough—and in the right way?

There’s still a gap in how AI is explained across the enterprise.

Terms like “Agentic AI,” “Generative AI,” and “Document AI” all sound similar, but come with different assumptions, capabilities, and risks.

Most business users don’t know the difference. And yet they’re the ones who will ultimately decide whether an AI system is trusted or rejected.

Before rolling out anything, teams should be asking:

• Have we clearly communicated how these technologies work—and what their limitations are?

• Do business users know where they fit in the loop?

• Have we validated that AI is the right solution for the problem at hand?

Sometimes the first step isn’t building the model—it’s reframing the problem.

‍

3. Are we ready for the last 10%?

Even when a pilot is successful, the final stretch to production is deceptively difficult.

Security reviews, change management, user testing, prompt tuning—it all adds friction. And in enterprise environments, one poor experience can trigger a total loss of trust.

A few important realities:

• The stakes are higher in production. Errors are more visible. Accountability is real.

• Building trust requires consistency. If you get one chance to prove value, you have to be ready.

• It’s better to delay a launch than to erode confidence in the platform.

‍

4. Should we even socialize AI projects yet?

Sometimes, sharing a prototype too early can do more harm than good.

If your teams aren’t ready to support adoption, or if you haven’t cleared the organizational hurdles, excitement can turn into disappointment—and disappointment into distrust.

Ask yourself:

• What needs to be true before we share this with the business?

• Are we setting expectations we can meet in the short term?

• Are we ready to support and refine this post-launch?

‍

This isn’t just a tech project. It’s a transformation.

CIOs who are serious about getting enterprise AI into production need to lead differently. This isn’t about chasing shiny use cases. It’s about laying the groundwork—for data, for people, and for trust.

The technology is powerful. But it’s not plug-and-play.

And the companies that take shortcuts now may find themselves further from value than when they started.

So before you build another demo, ask the harder question:

Are we ready to cross the chasm?

‍

Start a conversation with us!

‍