State of AI Waste 2026 v1

The v1 separates the model, collected anonymous signals, and conclusions that require a larger sample. It does not claim definitive statistical truth; it shows how Harmondale classifies leaks and what evidence will be needed to publish robust distributions.

That transparency avoids two mistakes: selling an empty report as market research, or waiting for a perfect dataset before explaining the measurement frame. The page makes the model readable now and states what will be strengthened next.

Spend, adoption, leaks, and role drift structure every diagnostic. These four categories connect very different symptoms to a common language: rising cost, usage that changes nothing, hidden exposure, or work that bends without new value.

The public report shows how these categories combine. A company can have visible spend waste and a more serious shadow AI leak. Another can show strong adoption but lose value in review and exceptions.

Anonymous signals cover team size, measurement maturity, declared use cases, waste symptoms, risky tools or behaviors, and priority levers. They are designed to reveal patterns, not expose a specific company.

• Measurement score
• Dominant leak
• Spend symptoms
• Priority levers
• Confidence level and limits

The v1 does not yet rank sectors, publish average spend benchmarks, or turn signals into a universal norm. Those conclusions require a larger sample, sufficient response quality, and safeguards against sample bias.

That limit is deliberate. A serious report should say what it knows, what it assumes, and what it cannot yet defend. It is the same tone Harmondale applies to individual audits.

Use the report as a map of questions to ask before funding more AI. Where is spend accumulating? Which use cases have real proof? Which risk remains invisible? Which role is changing without support?

The page is most useful when read alongside your own diagnostic. The contrast between public trends and internal signals helps decide whether your problem is common, unusual, or more urgent than expected.

The report will be updated when the anonymous sample is large enough to publish useful distributions. Future cuts should distinguish company size, measurement maturity, dominant function, leak type, and levers actually prioritized.

The criterion is not volume for its own sake. A useful data cut should help a team make a better decision: which evidence to gather, which spend to control, which use case to scale, or which risk to handle first.