The most expensive AI project is the one that demos beautifully and never ships. It is also the most common. Independent research suggests only around 5% of AI pilots produce measurable P&L impact, and roughly two-thirds of organizations have not scaled AI beyond experiments.
Why pilots stall
The failure is almost never the model. Three patterns show up again and again:
- Bolt-on, not redesign. AI is dropped into an unchanged workflow, so it saves seconds instead of transforming the process. High performers are ~3x more likely to redesign the workflow around AI.
- No owner, no metrics. A successful demo has no business owner, no success criteria, and no financial model — so it cannot graduate.
- Governance as an afterthought. Data access, security, and evaluation are left until the end, where they quietly kill the launch.
Adoption is the multiplier on every ROI figure. The 10-20-70 pattern seen in leading programs puts ~70% of the effort into people, process, and change — not models.
Closing the gap
Moving from pilot to production is a design and operating-model problem: pick use cases with a real owner, redesign the workflow, build evaluation and guardrails in from the start, and measure adoption alongside outcomes.
Celadon’s AI Enablement and Advisory engagements exist for exactly this, and it starts with the prioritization done in an AI Audit.
Get AI into production
Celadon stays accountable from prioritization through production and adoption — so pilots become systems that run.
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