Study: N. American manufacturers drive 27% surge in AI adoption since 2022

This year might be the “year of AI implementation,” but the technology actually has had a healthy last two years, with adoption surging 27% as a priority among North American manufacturers since 2022, according to a newly released report from Innova Solutions.

The study from the Atlanta-based technology services firm showed “AI adoption has surged from a priority for 59% of manufacturers to a driving force for 86%, with projections indicating near-universal adoption (93%) within the next two years.”

Another new report: With Gen-AI, excitement is being tempered by reality

Generative AI was a particular focus of the Innova study, which reported that 40% of organizations having already deployed Gen-AI use cases in production environments and are actively expanding their usage, according to an executive summary that accompanied the report, which polled 125 C-suite leaders across manufacturing.

"AI in manufacturing is no longer a future possibility, but a present reality. A successful AI implementation in this new era requires a strategic, three-pronged approach: people, process, and technology," according to the report.

Added Kaushik Doshi, senior VP ad BU Head Manufacturing, Automotive and Technology, at Innova: "Our report emphasizes that while AI is in the driver’s seat, a 'human-in-the-loop' perspective is essential to cut through the noise. Industry leaders are recognizing the critical role of human judgment, creativity, and problem-solving in harnessing the full potential of AI."

"Nearly 44% of new roles and job titles have evolved thanks to human-AI collaboration," Doshi noted. "While AI excels at processing vast amounts of data and identifying patterns, humans bring essential qualities like empathy, intuition, and ethical considerations to the table. By placing humans at the center of the AI revolution, organizations can create a future of work where technology and human ingenuity complement each other rather than work against each other."

Other findings of the report include:

38% of its respondents saw cost reduction as key to AI implementation, suggesting that AI-driven automation and optimization can lead to savings, enhancing profitability and operational efficiency.
40% of these industry leaders reported the significant challenge of integrating AI systems with their existing infrastructure, meaning “legacy” systems, which struggle to meet the wide data needs of modern AI.
The evolution of jobs is driving positive outcomes, including the emergence of new roles and specializations (44%), with AI fostering the creation of all-new titles and skillsets.
Though industrial and automotive sectors are plowing ahead with AI adoption, defense and aerospace (20% in the survey) and process and chemical (30%) are lagging. "Based on the report's findings, supply chain optimization has had a high adoption rate, considering the area of opportunity to potentially leverage AI," Doshi added. "Value realization and productivity gain range from 35% to 50%, depending on process maturity and scale of operation. The trend has been similar across sub-segments of manufacturing, aerospace & defense, automotive, industrial, and process and chemical sectors."

The majority of the 125 C-suite types surveyed by Innova Solutions are navigating the initial phases of AI adoption. Some companies are still formulating ideas, while others are actively developing and testing solutions. A significant portion has already progressed to the early stages of implementation, integrating AI into some of their workflows.

In the report, Innova also details their opinion about Gen-AI's capabilities.

The technology accelerates design cycles, optimizes existing products, automates repetitive design tasks, and generates personalized product recommendations, according to the technology services company.

It also enables real-time anomaly detection, proactive threat hunting, automated incident response, and comprehensive threat intelligence analysis.

Gen-AI also optimizes cloud infrastructure, enables predictive maintenance, personalizes cloud services, and enhances collaboration, and in this era of multimodal large language models, it transforms visual inspection, documentation, training, and human-machine interaction, according to the Innova Solutions report.

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Doshi said: "The report emphasizes that the efficacy of an organization’s AI initiatives hinges on the maturity of its overall data infrastructure. While data maturity encompasses a range of areas—from governance frameworks to data architecture design—achieving core data excellence principles is a prerequisite for successful AI deployment."