CIOs shift from mass AI experiments to focused deployment strategy

CIOs are abandoning the “shotgun approach” to AI experimentation, dramatically reducing the number of proof-of-concept projects they launch after experiencing high failure rates and disappointing returns on investment. Organizations that previously ran hundreds of AI pilots are now focusing on just 30 strategic initiatives, with individual business units limiting themselves to three to five targeted experiments that align closely with operational needs.

The big picture: The era of widespread AI experimentation is giving way to a more disciplined approach as companies realize that focused, outcome-driven deployments deliver better results than casting a wide net.

• An April 2024 IDC survey found organizations running an average of 37 AI proof-of-concepts, though experts suggest many large companies were actually running hundreds of pilots.
• “We’re seeing a marked shift from high-volume experimentation to more focused, outcome-driven AI deployment,” says Bhrugu Pange, managing director for AI solutions at AArete, a consulting firm.

Why this matters: The strategic pivot reflects hard-learned lessons about AI implementation, with companies discovering that success comes from embedding AI deeply into specific operational workflows rather than experimenting across multiple use cases simultaneously.

• Organizations are now treating AI projects like traditional research and development initiatives, expecting returns over a two- to three-year horizon instead of demanding rapid ROI.
• The shift addresses fundamental inefficiencies where companies were “trying to solve the same foundational problem many different ways,” according to Jason Hardy, chief technology officer for AI at Hitachi Vantara, a data management company.

What successful deployments look like: Companies are achieving measurable results by targeting high-friction workflows with comprehensive AI solutions.

• One AArete customer’s finance department created an AI-driven invoicing solution combining generative AI, natural language processing, and optical character recognition, delivering improvements in both cycle time and accuracy.
• “This effort, sourced from within the [finance] function itself, delivered measurable improvements in cycle time and accuracy — outperforming several parallel experiments that lacked operational anchoring,” Pange explains.

Strategic advice for CIOs: IT leaders facing pressure to launch numerous AI projects should redirect conversations toward impact and long-term value creation.

• “Don’t mistake speed for progress,” advises Chandra Venkataramani, CIO of TaskUs, an IT outsourcing provider.
• He recommends anchoring decisions to clear business goals and employee outcomes while building trust in deployed AI tools with both teams and customers.

The counterargument: Some experts warn against reducing experimentation too drastically, emphasizing the continued value of rapid iteration with proper governance.

• “Rather than worry about the rate of movement from proof-of-concept to production, put in place the systems that let you very quickly try out new ideas,” says Nancy Gohring, senior research director at IDC, a market research firm.
• Thomas Robinson, chief operating officer at Domino Data Lab, an AI platform company, argues that “the key isn’t fewer proof-of-concepts, it’s governed velocity — being able to experiment quickly, learn fast, and scale what works, all while maintaining compliance and control.”