Most Companies Are Still Just Playing With AI

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Everybody’s got an AI strategy. Every platform claims to be AI-powered. Every vendor deck has a slide about how their product uses machine learning to deliver transformative outcomes.

Most of it is still theater.

I had a conversation with David DeSanto, CEO at Anaconda, recently for the TechSpective Podcast, and what struck me most was how honest he was about where enterprise AI actually stands. Not where vendors want it to be. Not where the headlines say it is. Where it actually is.

A lot of organizations have run pilots. Some have solid proof-of-concept projects. A handful have built internal tools that genuinely save teams time. But very few have moved AI into real production across the business. There’s a big difference between “we’re experimenting” and “this is how we work now.”

That gap is where most companies are stuck—and it’s not because the technology doesn’t work.

The demo almost always looks good. A model produces useful output. A prototype saves someone a few hours. The problem shows up when you try to scale that across an enterprise environment. Suddenly you’re dealing with data governance questions, security concerns, reliability issues, and a fundamental trust problem: can we actually rely on what this thing produces?

Those issues don’t show up in the demo.

Open source plays an interesting role here. It’s always been central to the data science world, and that hasn’t changed. Developers and data scientists are still experimenting constantly—new models, new frameworks, new workflows. Open ecosystems make that possible. But they also create real headaches for organizations trying to manage dependencies, maintain security, and keep things consistent across teams.

Innovation versus governance. That’s the tension nobody has fully figured out yet.

Something else worth noting: AI is changing what technical expertise actually means. Tasks that required specialized skills a few years ago can now be partially automated. That sounds like it should reduce the need for expertise—but it mostly just moves where that expertise matters.

Technical teams spend less time writing code from scratch and more time framing problems, evaluating outputs, and validating results. Knowing how to ask the right question—or spot when an AI’s answer is subtly wrong—can matter more than generating the answer in the first place.

That’s a real shift in how those jobs work, and most organizations are still figuring out how to adapt.

Trust is the underlying issue running through all of this. Organizations can’t treat AI like a magic box that produces correct answers. They need to understand how models work, how their data is being used, and how outputs are generated. Without that visibility, it’s hard to rely on AI for anything that actually matters.

And the challenge isn’t really technical. The technology works well enough. What’s hard is building the infrastructure, governance, and culture around it—getting security teams, data scientists, developers, and business leaders to actually work together instead of operating in separate lanes.

That collaboration doesn’t happen naturally. It has to be built deliberately.

AI also tends to change the process, not just speed it up. Teams aren’t just doing the same work faster—they’re working differently, exploring problems differently, testing ideas differently. Machines are becoming collaborators in that process rather than just tools. Adapting to that takes time.

The organizations that figure it out won’t be the ones with the most advanced AI technology. They’ll be the ones that put in the unglamorous work—governance frameworks, cross-team alignment, careful validation of what the AI actually produces.

That’s less exciting than the vendor pitch. But it’s closer to what real progress looks like.