Hotel Algorithmia: AI Hype Cycles and Trapped Data

In 2025, we’re still checking into the same metaphorical hotel we were warned about years ago. “You can check out any time you like, but you can never leave” sounds poetic in a classic rock song, but it hits differently when you're staring at fragmented datasets, vendor lock-in, and AI products that overpromise and under-deliver. Welcome to Hotel Algorithmia, where your models age fast and your data stays behind.

Let’s start with the obvious. The AI market is booming, again. IDC projects global spending on AI to reach $423 billion this year, with enterprise adoption climbing across all industries. But buried in the Gartner 2025 Hype Cycle for Artificial Intelligence is a familiar warning: nearly 70% of AI projects still fail to deliver measurable ROI. That’s not just a statistic. It’s a program manager’s post-mortem waiting to happen.

One reason for this breakdown is the illusion of plug-and-play intelligence. Leaders are often sold on the transformative potential of generative AI without aligning teams, data strategies, or long-term governance. We end up deploying proof-of-concepts that can’t scale and integrating tools that create more silos than they solve.

Here’s what that looks like on the ground: You’ve got a pilot LLM running in marketing, a data science team iterating models in notebooks no one else can access, and dashboards in BI tools fed by manually wrangled spreadsheets. None of it is aligned to enterprise data strategy or clear business outcomes. According to a recent McKinsey report, only 24% of companies say their data initiatives have successfully scaled beyond a few use cases.

Meanwhile, the data stays trapped. Not just in tools, but in cultures. Knowledge is hoarded in SharePoint folders, API keys are passed around like secrets, and version control is optional at best. Data governance is considered a blocker rather than a framework for responsible innovation. We talk about “training the model,” but rarely discuss how to untrain an organization.

There’s also the myth of AI permanence. Models deployed in 2023 are aging fast. As of Q1 2025, fine-tuning models on proprietary data has become 37% more expensive on average due to increased cloud inference costs and licensing changes from providers like OpenAI and Cohere (source: PitchBook, April 2025). We’re still building on sand, and no one wants to budget for retraining when the new shiny demo is already being pitched.

Trapped data isn’t just a technical problem. It’s strategic inertia disguised as innovation. I’ve sat in rooms where executives nod through AI roadmap slides, only to underfund the actual data cleanup required to execute them. It’s easier to believe in an AI future than to audit your past infrastructure.

Hotel Algorithmia isn’t a destination. It’s a cycle. And cycles repeat when no one owns the system. As program managers and future executives, our job isn’t just to deliver AI. It’s to question who benefits, who maintains it, and who pays when it stalls.