Uber Freight CTO: Why 95% of AI Pilots Fail and How to Win

Val Marchevsky, CTO of Uber Freight, shared critical lessons on AI adoption in freight at the Supply Chain AI Symposium 2026, warning that the industry must focus on solving fundamental business problems rather than chasing trendy applications. The rise and fall of the AI wrapper economy demonstrates that companies building shallow solutions on temporary gaps in technology capabilities disappear quickly, while those addressing core operational pain points endure. Uber Freight's success with document processing, reducing manual work by over 50%, came not from a single AI model but from an ensemble approach using specialized tools fitted to specific problems within the broader workflow.

Marchevsky emphasized that freight faces uniquely high stakes for AI implementation due to the cost of failure in consumer supply chains, where human oversight remains essential. The company's adoption of shadow mode testing, running new models in parallel with legacy systems without making live decisions, allows for rigorous A/B comparison before production deployment. A sobering finding cited by Marchevsky: 95% of AI proofs of concept fail due to production issues, not model performance, highlighting the critical importance of involving operational teams from day one rather than relegating AI development to isolated technical groups.

For supply chain professionals evaluating AI solutions, Marchevsky's guidance is clear: scrutinize data governance practices, demand environment-specific benchmarks rather than generic metrics, and prioritize vendors demonstrating a systematic approach to production deployment. The future of freight AI lies not in replacing existing systems but in building agentic software that integrates with legacy infrastructure, a more pragmatic path that acknowledges the fragmented, complex reality of modern logistics operations.