What Moves an AI Pilot Into Production

Asked what moved their AI from pilot to production, 61% of 72 companies pointed to workflow redesign. Data readiness came second at 22%, and executive sponsorship third at 14%.

Study methodology: 72 executives and team leads who took AI into production answered over 21 days through MentionMatch and Featured. Each respondent named a single deciding factor, so the shares add up to 100%. Founders and CEOs made up 54% of the sample, and the other 46% were department heads and technical leads.

What moves an AI pilot into production

Essential statistics: AI workflow redesign and other enablers

• 61% of 72 companies named workflow redesign as the factor that moved AI from pilot to production.
• Data readiness came second with 22% of answers.
• 14% credited executive sponsorship.
• The remaining 3% named MLOps, teamwork across functions, vertical integration or domain tuning.
• Workflow redesign drew nearly 3 times the share that data readiness did.
• Answers about people and process, workflow redesign and executive sponsorship, made up 75% of the total.
• Improvado standardized its advertising data and cut dashboard builds from 6 hours to 15 minutes.

Key takeaways

• Most teams that reached production changed the process around the model. 61% credited workflow redesign, and only 3% gave an answer outside the top 3.
• Data readiness ranked second at 22%, so the technical work that moved pilots forward was mostly data work.
• Executive sponsorship drew 14% and carried the most weight in regulated and legacy-heavy settings, where AI needed significant budget for integration work.
• Improvado’s dashboards went from 6 hours to 15 minutes once the team standardized its data, which shows data readiness at work in one workflow.

Actionable insights

• Redesign the workflow before scaling the pilot, giving AI a defined step and a human sign-off, since 61% credited that redesign with reaching production.
• Standardize source data while the workflow changes, as Improvado did before cutting dashboard builds from 6 hours to 15 minutes.
• Line up an executive sponsor early in regulated or legacy-heavy settings, where the study found sponsorship counted most.
• Don’t rely on model tuning alone to reach production, since MLOps, tuning and every other answer outside the top 3 drew 3% combined.

“Our early pilots failed because we tried to layer AI on top of existing processes. After several failures, we decided to redesign the entire workflow so AI could have a defined role, but never make the final decision without human oversight.” — Roman Milyushkevich, Chief Technology Officer, HasData