How to Keep AI Pilots From Failing in Your Organization
Most AI pilots in enterprises fail before they deliver results. MIT researchers found that the large majority don't reach their stated outcomes, and more than half of businesses plan to shut down their AI pilots this year due to poor performance, according to a Solvd report.
The difference between success and failure often comes down to three factors: leadership accountability, data access, and governance structures.
Find the Right Champion
AI projects that move past the pilot stage need a sponsor who owns the outcome and can drive adoption across the organization, said Mark Schmidt, CIO at Westlake, a manufacturer of petrochemicals, plastics, and building materials.
"You have to have the right sponsor," Schmidt said during a panel at the MIT Sloan CIO Symposium in May. "That sponsor has to have the drive and the vision to take it through."
A strong champion creates a ripple effect. When one project succeeds under committed leadership, others in the organization replicate what worked.
Watch for Early Signs of Traction
One reliable indicator of pilot success is how employees actually use the tool. At Corning, staff members incorporated AI model outputs into their forecasting process, but they didn't blindly accept the results.
"They're not blindly using that forecast that the AI model gave," said Soumya Seetharam, SVP and chief digital and information officer at the materials science company. "They're looking at it, comparing it against what the legacy process did and then putting judgment on top."
When employees weave AI tools into existing workflows and treat them as one input among several, the project is gaining genuine traction.
Build Data and Governance First
Two structural elements determine whether a pilot succeeds or stalls: access to quality data and clear governance rules.
Without the right data sets to power the tools, "you're picking something that's probably too difficult at this point in time," Schmidt said.
Corning created a governance council for AI and machine learning that includes the general counsel and top executives. The council approves which AI use cases get published in a common marketplace, allowing other workers across the organization to access tools that both work and meet company standards.
Document What Goes Wrong
Organizations tend to celebrate and document their successes while ignoring failures, said Vipin Gupta, a former CIO turned advisor. That's a missed opportunity.
Learning from what didn't work is as important as replicating what did. Without that discipline, teams repeat the same mistakes across different pilots.
For more on managing AI at the executive level, see our AI for Executives & Strategy resources and AI Learning Path for CIOs.
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