Crossing the Enterprise AI Chasm

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 knock 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 chasm¹. 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
• Why are we still using excel for collecting key data points?

The good news: AI can help enhance data. But only if you know what you’re feeding it and if you have path to validate.

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 Agent AI, Generative and Document AI come with different assumptions and capabilities. 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?
• Can we work out how business users will 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. Exacerbated by quick demos generated by AI Coding agents and "Vibe Coding".

Security reviews, change management, user testing, prompt tuning add friction, but for good reason. In enterprise environments, a 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. How do we validate AI use cases before momentum turns into mistrust?

With AI, the volume of ideas isn’t the problem. It’s clarity. Business teams think in outcomes, not technical requirements. And visuals often go farther than a document full of specs.

But enthusiasm alone doesn’t signal readiness. If the team can’t support the change, or if the organization hasn’t cleared key hurdles, early excitement will quickly turn into resistance.

Ask yourself:

• What conditions need to be true before we move this forward?
• Are we setting realistic expectations that the business can trust?
• Do we have a clear path to support, refine, and scale after 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 the loud use cases. The technology is powerful. But in the Enterprise it's not plug-and-play.

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

Takeaways: Educate the business on AI and its applicability. Make sure you've demonstrated you understand their process and pain points. Make sure you have the tech stack, data readiness in place to increase the chance of success and getting into production.

¹The "chasm" concept references Geoffrey Moore's technology adoption framework from his influential 1991 book "Crossing the Chasm: Marketing and Selling High-Tech Products to Mainstream Customers."

Martyn is the CEO of Nymbl, a consulting and application development company helping enterprises close the gap between idea and impact. With a focus on data architecture, low-code platforms, and AI enablement, Martyn works with CIOs and executive teams to navigate the complexity of building software that actually gets used.