More than 80 percent of all AI projects fail — twice the rate of traditional IT projects. That's what the RAND Corporation found through interviews with data scientists and engineers. The root causes are almost never the models themselves. They are unclear objectives, weak data foundations, and the missing path from laptop to production. Why the step from pilot to production is the real hurdle, and how a platform like Vertex AI on Google Cloud shortens that journey.
Every company has an AI pilot by now. Hardly any has AI running in production.
The demos are impressive. A data scientist shows in a meeting how the model predicts maintenance needs, classifies requests, or summarizes text. Everyone nods, the budget for the next phase gets approved. And then something strange happens: nothing. The pilot stays a pilot. A year later it's still running on the laptop of the colleague who built it, and when that colleague leaves, the project dies with them.
This isn't an anecdote — it's the statistical norm. The RAND Corporation, drawing on interviews with 65 experienced data scientists and engineers, found that more than 80 percent of all AI projects fail. That's twice the rate of traditional IT projects. The study also puts a convenient excuse to rest: technology ranks near the bottom of the causes. At the top are poorly defined problems, weak data foundations, and insufficient leadership support. AI projects fail organizationally, not mathematically.
Why getting to production is so much harder than the demo
To understand where pilots get stuck, it helps to look at the difference between a demo and an operational system. A demo has to work once — in front of an audience, with prepared data. A production system has to work every day, with today's real data, under load, and in a way that people who didn't build it can understand and maintain.
Between the two lies a long list of unglamorous work. The model needs an environment where it runs reliably and scales. It needs pipelines that feed it fresh data automatically — not via manual exports. It needs monitoring that detects when prediction quality drops, because models age with their data. It needs access controls and auditability, especially once compliance teams and works councils start asking what the system's decisions are based on. And it needs a process for new versions, so improvements can reach production without putting it at risk.
None of this is rocket science. But all of it is infrastructure — and most companies simply don't have that infrastructure in place for AI. The pilot ran fine without it. Production won't.
What Vertex AI on Google Cloud does differently
This is where the platform question comes in. You can build all of the infrastructure described above yourself, from servers, frameworks, and scripts. Some companies do exactly that and then dedicate a team to keeping it alive. The alternative is a managed platform. Vertex AI is Google Cloud's AI platform and covers precisely the ground where pilots tend to fail: training, deployment, pipelines, monitoring, and model management in one place — plus access to current models like Gemini for generative use cases.
The practical difference comes down to one simple question: what happens when the model needs to go to production? On a platform, that's a defined step. The model is deployed as an endpoint, scales automatically with load, and writes its metrics to a monitoring system that raises an alert when something drifts. Without a platform, that same step is a project that can take months and ties up specialized knowledge. Both paths lead to the destination. But only one is short enough to survive the patience of the management that approved the pilot in the first place.
There's one straightforward point worth noting in favor of Google Cloud: the provider builds the entire stack itself, from TPU chips to models to platform. Demand is validating that strategy — Google Cloud is growing faster than any other major cloud provider, driven by AI workloads. For you as a customer, that means one thing above all: the platform you're betting on is the provider's core business, not a side project.
The data foundation remains your responsibility
For all that a platform takes off your plate, one thing it won't do for anyone is provide their data. According to Gartner, 85 percent of failed AI projects cite poor data quality as a cause, and only 12 percent of companies have data of sufficient quality for AI applications. A model trained on incomplete or inconsistent data will produce incomplete and inconsistent results on the best platform in the world — just faster and at greater scale.
That's why every serious AI initiative has to address the data foundation before the model question. Where does the data the use case requires actually live? Who owns its quality? Does it flow automatically or through manual effort? We covered these questions in depth in our article on why AI initiatives fail, and they apply just as much at the infrastructure level. A platform can turn a solid data foundation into a productive system. It can't do anything with a poor one.
What a realistic path looks like
Based on our experience, projects that successfully make it to production follow a recognizable pattern — and it doesn't start with technology.
It starts with a use case that solves a measurable business problem. Not with the question of what you could do with AI, but with the question of which process is demonstrably costing money or time today. The RAND study identifies the absence of exactly this clarity as the most common reason AI projects die. A good test: if you can't state in a single sentence which metric the project should improve, it isn't ready yet.
The pilot comes next — but from day one on the target infrastructure. This is the point where later success is decided. A pilot running on Vertex AI rather than a laptop isn't a throwaway. The path to production then isn't a rebuild — it's an expansion. The additional cost at the start is manageable; the savings at the transition are significant.
And finally, production — with clear accountability. A model in production needs an owner who monitors its quality, just like any other business-critical application. AI isn't a project with an end date; it's a capability you build and maintain.
How to tell if your pilot is stuck
There are warning signs that appear in nearly every stalled AI project. The first is about people: all the knowledge about the pilot lives in a single person. If only one person can start, feed, and interpret the system, it isn't a system — it's a hobby with a budget. The second sign is the data path: data reaches the model via exports and manual work. What was a shortcut in the demo becomes a breaking point in production, because no one reliably runs an export every morning at six. The third sign is how success is measured: the pilot is considered a success because the demo impressed people, not because a pre-defined metric was reached. And the fourth sign is the quiet death: there is no next milestone with a date. The pilot is just there, open-ended and non-committal — and that's exactly how it eventually disappears.
Recognizing two or more of these signs doesn't mean the project needs to be written off. But the next step shouldn't go into more model work — it should go into structure: a target metric, a data pipeline, a target platform, a named owner. That's less glamorous than a new model. But it's the difference between an experiment and an initiative.
What the team needs to be able to do
Honesty also requires addressing the staffing question. Getting to production takes three competencies, and they rarely live in one person. Someone needs to own the business problem — to be accountable for the use case and to decide what good enough looks like. Someone needs to command the data — to build pipelines and ensure quality. And someone needs to handle operations — to own deployment, monitoring, and security on the platform.
The good news: a managed platform like Vertex AI shifts the effort significantly toward the first two roles, because it dramatically reduces the operational burden. A mid-sized company doesn't need to build a full MLOps team to run a model reliably. It needs clear roles, a platform that handles the heavy infrastructure work, and — where necessary — a partner who fills the gaps until the in-house team can take over. That's exactly the model we practice in our projects, with the explicit goal of making ourselves unnecessary.
What you can review this week
If your organization has an AI pilot running or in planning, ask three questions. First: which metric is it supposed to improve, and has that been written down? Second: what infrastructure is it running on, and is that the same infrastructure where it will eventually run in production? Third: who will operate it after go-live, and does that person know?
If any of these questions has no answer, you know what to work on before investing further in models. That's exactly the work we support: from scoping the use case to building the data foundation to running a production-ready platform on Google Cloud. We know this path works because our own systems run on it — as do the environments of several of our clients. You can find the starting point on our Google Cloud page. All analyses are available in our Google Cloud Insights.