AI Adoption in Manufacturing Shows Promise Despite Implementation Hurdles

Artificial intelligence is rapidly reshaping the manufacturing sector, with 93% of industry leaders believing organizations that fully integrate AI will gain a significant competitive edge, according to a new report from KPMG International.

The report, "Intelligent Manufacturing: A Blueprint for Creating Value through AI-driven Transformation," surveyed 183 senior manufacturing AI leaders across eight countries to understand how AI is being implemented to reduce costs and expand revenues in the industrial sector.

The research found that AI adoption in manufacturing is already showing promising results, with 96% of organizations reporting operational and efficiency improvements. Additionally, 45% have experienced measurable financial improvements, with 62% achieving a return on investment greater than 10%.

Manufacturing companies are investing heavily in AI technologies, with 36% allocating more than 10% of their total IT budget to AI initiatives. Looking ahead, 77% plan to increase AI investments over the next year, with 71% expecting growth of more than 10%.

Despite enthusiasm for AI, implementation challenges persist. Fifty-six percent of manufacturers reported data-related issues when implementing AI solutions, while 40% face workforce challenges including skills gaps and resistance to change.

The report outlines a three-phase approach to AI integration: Enable (building AI foundations and enabling people), Embed (integrating AI into workflows and products), and Evolve (transforming business models and ecosystems). This framework helps manufacturers progress from isolated AI applications to enterprise-wide transformation.

"To unlock the full potential of AI, manufacturing companies should move beyond isolated use cases and reimagine how intelligence is embedded across the enterprise," said Jonathon Gill, Global Head of Industrial Manufacturing at KPMG International.

The report highlights how AI is being applied across manufacturing operations, including predictive maintenance, quality control, supply chain optimization, and production scheduling. As manufacturers progress to more advanced implementations, AI capabilities could eventually enable self-optimizing factories, autonomous supply chains, and circular manufacturing networks.

This shift comes as the industrial sector faces increasing pressure to improve efficiency, sustainability, and resilience amid global competition and market volatility.

The research also emphasizes the importance of building trust in AI systems through responsible development, proper governance frameworks, and workforce upskilling to ensure successful adoption and implementation.