Why 87% of AI Projects Never Make It to Production

In 2024, the RAND Corporation published a report that pulls no punches: More than 80 percent of all AI projects fail. That’s twice the already alarming failure rate of non-AI IT projects. Gartner goes further: 87 percent of data science projects never reach the production phase.

Let that number sink in. Of ten AI projects started today, fewer than two will ever reach real users. The rest die as proofs of concept, as prototypes, as slides in a quarterly report.

The five real reasons

The RAND study interviewed 65 experienced AI practitioners — not theorists, but people who have been building and deploying models for years. What they found only surprises those who have never seen an enterprise AI project from the inside:

1. Solving the wrong problem

84 percent of respondents named this as the primary cause. Leadership says: “Build us AI.” But what exactly should be optimized, for which metric, in which workflow — that remains vague. Teams then build brilliant models for problems nobody has.

2. Data — the unsolved core problem

30 of 50 interviewees named poor data quality as the primary reason. Not missing data — bad data. Inconsistent, incomplete, unrepresentative. Models work in the lab but not in reality, because reality is messy.

““80 percent of AI is the dirty work of data engineering. You need good people doing the dirty work — otherwise their mistakes poison the algorithms.””

3. Technology-focused instead of problem-focused

Teams chase the latest framework instead of solving the real problem. Data scientists experiment with transformer architectures when a random forest would suffice. The individual incentive (padding the resume) conflicts with project success.

4. No infrastructure for production

A model in a notebook is not a product. Without an MLOps pipeline, without monitoring, without a deployment strategy, even the best model remains an experiment. Data engineers are seen as ‘the plumbers of data science’ — and staffed accordingly.

5. Problems that are too hard

Sometimes the problem is simply not (yet) solvable. Not every task is suited for AI. Automating subjective human judgment, computer vision in unstructured environments, predictions with too many variables — AI is not a magic wand.

What the successful ones do differently

The 13 percent that make it to production share something: They start with the problem, not the technology. They invest in data quality before training a single model. They have infrastructure teams that are as respected as the model builders.

Then came GenAI

Gartner predicts: 30 percent of all generative AI projects will be abandoned after proof-of-concept by end of 2025. The reasons: poor data quality, uncontrolled costs, unclear business value. History repeats — just faster.

At the same time, according to McKinsey, 72 percent of all companies use AI in at least one function — but only 14 percent are actually ready for it according to Cisco. The gap between ambition and reality is growing.

What this means for your company

If you want to introduce AI, don’t start with the question “Which model?” Start with: Which problem is costing us money today? Do we have the data in the required quality? Who will deploy and maintain it? If any of these answers is missing — that’s the first step, not the model.

The 87 percent is not a law of nature. It’s the result of avoidable mistakes. The most common: thinking about the solution too early, before the problem is understood.

Sources: RAND Corporation RRA2680-1 (2024), Gartner (2023/2024), McKinsey State of AI (2024), Cisco AI Readiness Index (2023)