The Hype Hangover

Three conferences in three weeks. Two sold vision. One shipped systems.

It’s been a busy month: ScaleUp:AI, Dreamforce, and the Databricks CIO Forum. Each promised to show what’s next for enterprise AI. Each was full of optimism, slick demos, and thoughtful discussions. Walking out of those ballrooms, one feeling kept coming back: the gap between the noise of AI and the work of AI has never been wider.

At two of the three, the energy was electric. The stages glowed, the slides dazzled, and every company claimed to be reshaping the world. But scratch beneath the slogans and you’d find something thinner. Optimism without operations. Panels circled the same talking points: data is the new oil, AI is transforming every function, responsible innovation is key. All true, but all surface-level. It felt less like a movement and more like a performance of progress.

This is performative AI. Technology theater dressed up as transformation. You’ve seen it before: executive keynotes promising autonomy “by next quarter,” product demos run on curated data, and vision decks that mistake inspiration for infrastructure. These moments photograph well. They trend. But they rarely ship.

Then came the Databricks CIO Forum. Smaller stage. Fewer adjectives. But more substance. The conversation wasn’t about hallucinations. It was about latency. It wasn’t about foundation models. It was about data pipelines, governance, and operational integration. You could feel the shift: less spectacle, more systems.

What stood out most was that Databricks wasn’t talking about hypotheticals. Even the founder and CEO, Ali Ghodsi, was there on stage giving the demos himself, jumping straight into use cases with real customer data. It’s rare to see a CEO in the weeds, validating the very problems his customers face. He showed how enterprises are leveraging production datasets to enhance models, build RAG systems, and deploy operational apps. They were tackling real challenges because they already have the data. The pipelines are built, the governance frameworks are mature, and that foundation lets them focus on outcomes.

It was the only conference where leaders talked about failure modes, not just success stories. Where architecture diagrams replaced hype slides. Where the word validation carried more weight than vision.

And it was, paradoxically, the most optimistic of them all. Because it was real.

From Performance to Progress

Two paths emerged. The same week I was hearing about AI “redefining everything,” I was also hearing about teams quietly refining something: automating validation systems, building trust loops, reducing friction in data pipelines. None of these efforts made headlines. But they’re what make AI actually deliver.

This is the recurring theme across enterprise AI over the past year: the progress that matters happens behind the curtain. Invisible AI, the kind built into workflows and decision systems, isn’t photogenic. But it compounds. It scales through reliability, not virality.

At Dreamforce, you couldn’t walk ten feet without bumping into AI announcements. Impressive in demos, less so in deployment. It’s a reminder of something we’ve learned repeatedly: AI without systems is just theater.

Databricks stood apart because it focused on the plumbing. The unglamorous connective tissue that makes everything else possible. As I’ve written before in “APIs, Marketplaces, and the Real Distribution Moat”, APIs are the railroads of the AI era. Without solid infrastructure, you don’t get progress. You get prototypes. The same holds true here: every company wants to build the train, few want to lay the track.

The Pattern Beneath the Noise

Two kinds of AI stories are being told right now:

The companies that win aren’t chasing attention. They’re compounding competence. Their AI doesn’t need a press release to prove its existence. It proves itself every time it prevents a defect, routes a signal, or shortens a decision cycle.

We’ve seen this play out repeatedly. From the validation revolution following Salesforce’s AgentForce launch to the operational rigor emphasized in Databricks’ sessions, the real differentiator isn’t algorithmic sophistication. It’s implementation discipline. Trust isn’t built by announcements. It’s built by uptime.

Three Questions to Separate Theater from Progress

Use these questions to evaluate if your AI initiatives are performative or productive:

1. If this AI stopped working for 24 hours, would operations collapse or would anyone notice? Productive AI is mission-critical. Performative AI is optional.

2. Can you measure the business impact without referencing demo engagement or press mentions? Productive AI drives metrics like reduced cycle time, lower error rates, or faster decisions. Performative AI drives awareness.

3. Does the AI require its own interface, or is it embedded in existing workflows? Productive AI disappears into daily work. Performative AI demands attention to justify its existence.

If you answered “wouldn’t notice,” “can’t measure,” or “requires interface” to any of these, you’re building for applause, not adoption.

Why This Matters for Leaders

For enterprise leaders, this moment calls for a kind of architectural leadership. Less direction, more design. Your job isn’t to chase the next generative breakthrough. It’s to build systems that quietly, repeatedly, and predictably deliver value. To trade hype velocity for decision velocity.

That means shifting questions from:

• What’s our AI story?

to

• What’s our AI system actually doing today that didn’t exist last quarter?

It means celebrating stable infrastructure as much as flashy prototypes. It means measuring trust accumulation, not just engagement metrics. And it means designing environments where AI helps humans make better decisions without demanding attention.

To get there, leaders should focus on a few key use cases that clearly drive measurable outcomes instead of taking a top-down approach that tries to apply AI everywhere at once. The most successful programs start small, learn fast, and expand from proven value rather than broad mandates that spread resources thin.

The irony is that invisible progress feels unsatisfying in the short term. There are no ovations for reducing latency by 60 milliseconds or validating a model 2,000 times before release. But those are the moments where enterprise AI grows up.

What to Do Monday Morning

Pick one high-frequency decision in your organization. Map how long it currently takes from signal to action. Then ask: what would need to be true for this to happen in half the time with equal or better accuracy?

That gap is where invisible AI lives. Build there first.

Track one metric: Demo-to-Production Lag Time. Measure the weeks between when you see an AI capability demoed and when it’s delivering value in your actual workflows. If that number is growing, you’re collecting performative AI. If it’s shrinking, you’re building productive AI.

The Quiet Center of the Movement

The future of enterprise AI will be built by those who see trust as infrastructure, not as a tagline. Who design for momentum that scales itself, not hype that exhausts itself.

So yes, the hype cycle is still spinning. But there’s a hangover setting in. A collective realization that applause doesn’t equal adoption.

And maybe that’s a good thing. Because what comes after the hangover is usually clarity.

If your AI strategy needs a demo to prove it exists, it’s not strategy. It’s theater.

The real work of AI isn’t visible.

And that’s exactly why it’s working.

AI doesn’t need a spotlight. It needs a backbone.