Top Generative AI Use Cases in Manufacturing

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Apr 25

In February 2024, AI reached a peak in Google searches, a testament to its explosive impact across industries. Its global search volume continues to outshine even pop culture icons, being searched almost five times as often as Taylor Swift. This isn’t just a trend; it’s a testament to how AI is reshaping the business world and sparking a curiosity that transcends entertainment.

The appetite for AI knowledge is insatiable, and yet, people struggle to find answers to their questions.

According to a study from MIT Technology Review Insights, there is a tangible wave of optimism and ambition in the manufacturing sector to harness AI, especially in engineering, design, and factory operations. This is evidenced by the number of manufactures that are researching or experimenting with generative AI.

Key findings from the MIT report

Widespread Exploration:
- 64% of manufacturers are researching or experimenting with AI, while 35% have begun deploying use cases.
- Most common applications: product design, conversational AI, and content creation.
Investment Surge:
- 58% plan to increase AI spending by over 10% in engineering and design, and 43% for factory operations.
- Larger manufacturers are leading the charge, with 77% boosting investment significantly.
Barriers to Scale:
- 57% cite data quality issues, and 49% mention talent shortages as key constraints.
- Only 23% of manufacturers report their production data is suitable for AI models, highlighting a need for better data integration and governance.

My take

The explosive growth of generative AI in manufacturing signals a major shift in the industry. Companies are no longer dabbling; they're diving in headfirst to harness AI's potential. The need for speed, innovation, and efficiency is driving this adoption, making AI a game-changer. However, data quality and talent shortages are significant hurdles. Manufacturers must prioritize robust data infrastructure and upskill their workforce to keep pace. Those who seize this opportunity and integrate AI effectively will not just survive—they'll thrive in the new era of smart manufacturing. The message is clear: adapt or be left behind.

Compared to MLC’s Industrial AI report

In June 2023 Manufacturing Leadership Council came out with a report on the future of Industrial AI in manufacturing that looks at industrial AI across broader operations, from ERP to supply chain management. Both reports (MIT and MLC) identify common challenges, such as data quality issues and talent shortages, and note significant investment increases—over half of manufacturers plan to boost spending. The MIT report suggests a more integrated, cross-functional approach to AI deployment, whereas the MLC highlights that many companies are still developing formal strategies. The implications are clear: as AI becomes more pervasive, manufacturers that effectively address data and talent challenges and adopt a cohesive strategy will gain a competitive edge in the evolving industry landscape.

Three pieces of advice for manufacturers

Invest in Data Quality and Integration: With 57% of manufacturers in the MIT report citing data quality as a barrier, it’s crucial to enhance data management systems. Improving data integration will enable more effective AI deployment across all functions.
Develop Cross-Functional AI Teams: The MIT report highlights the success of cross-functional teams in scaling AI projects. By combining IT, AI specialists, and business leaders, manufacturers can ensure that 29% of operational AI projects, as noted in the MLC report, progress beyond the pilot stage.
Prioritize Workforce Upskilling: With the MLC report stating that 32% of companies expect workforce increases due to AI, investing in training can help bridge talent gaps. This ensures that employees are prepared for new roles as AI takes on more routine tasks, fostering a more innovative and agile workforce.