What Must Change for Artificial Intelligence to Pay Off?

IndustryWeek’s “What Must Change for AI to Pay Off?” argues that most organizations are repeating an old mistake: buying sophisticated AI tools before redesigning how people and machines actually work together. Instead of transforming operations, this leads to “automated dysfunction”—systems that misroute work, frustrate customers and employees, and fail to move core business metrics because workflows, handoffs and decision rights were never rethought. The article stresses that AI success is now an operating-model issue, not a technology issue, and calls for leaders to start with the work itself: mapping where rules versus judgment are needed, identifying high-impact failure points and treating AI as a teammate to onboard, not software to install.

To unlock real ROI, the piece outlines a practical playbook: study the work before buying tools, redesign workflows with clear human–AI handoffs and guardrails, and replace generic training with role-specific, on-the-job learning that teaches people when to trust, question or override AI. It also emphasizes measuring what truly matters—how AI reduces errors, rework and cycle time, and how much employees trust and effectively use these systems—rather than tracking deployment stats. Ultimately, IndustryWeek highlights a bigger leadership shift: managers must become orchestrators of hybrid human–digital teams, accountable for the quality, ethics and performance of AI-augmented work, so that technology amplifies capable teams instead of adding new layers of complexity.

Read the entire article here.