Finding value with AI and Industry 5.0 transformation


“To realize the promise of Industry 5.0, companies must move beyond cost and efficiency to focus on growth, resilience, and human-centric outcomes,” says Sachin Lulla, EY Americas industrials and energy transformation leader. “This requires not just new technologies, but new ways of working—where people and machines collaborate, and where value is measured not just in dollars saved, but in new opportunities created.”

An MIT Technology Review Insights survey of 250 industry leaders from around the world reveals most industrial investments still target efficiency. And while the data shows human-centric and sustainable use cases deliver higher value, they are underfunded. The research shows most organizations are not realizing the full value potential of Industry 5.0 due to a combination of:

• Culture, skills, and collaboration barriers.
• Tactical and misaligned technology investments.
• Use-case prioritization focused on efficiency over growth, sustainability, and well-being.

The barrier to achieving Industry 5.0 transformation is not only about fixing the technology, according to research from EY and Saïd Business School at the University of Oxford, it is also about bolstering human-centric elements like strategy, culture, and leadership. Companies are investing heavily in digital transformation, but not always in ways that unlock the full human potential of Industry 5.0.

“We’re not just doing digital work for work’s sake, what I call ‘chasing the digital fairies,’” says Chris Ware, general manager, iron ore digital, Rio Tinto. “We have to be very clear on what pieces of work we go after and why. Every domain has a unique roadmap about how to deliver the best value.”

Download the full report.

This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by MIT Technology Review’s editorial staff. It was researched, designed, and written by human writers, editors, analysts, and illustrators. This includes the writing of surveys and collection of data for surveys. AI tools that may have been used were limited to secondary production processes that passed thorough human review.



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