Google Cloud: Taking Gen AI in Manufacturing Beyond the Hype

Praveen Rao, Global Director, Head of Manufacturing Industry at Google Cloud's Strategic Industries team understands what drives growth within the industrial manufacturing sector. 

With more than 20 years of expertise spanning technology and manufacturing, he believes the manufacturing industry is on the cusp of change. 

Central to this change is generative AI, which he believes is set to become a cornerstone of manufacturing operations over the next decade. 

Praveen argues the technology will drive innovation, improving efficiency and enhancing overall competitiveness.

He references Google Cloud’s recent ROI of Gen AI for Manufacturing & Auto report, where nearly two out of three organisations in the manufacturing and automotive sectors are already deploying gen AI in production.

Additionally, 72% of respondents who have been using gen AI in production are now seeing a return on investment from their gen AI initiatives in at least one use case.

Building on this he shared his insights on which AI trends we will see this year in the sector, signalling this broader shift. 

1. The future of manufacturing is Multimodal

Praveen explains that the multimodal capabilities of gen AI have the potential to revolutionise manufacturing as we know it.

Key to this will be Multimodal AI agents which can act as “sentries” in manufacturing operations, monitoring and analysing various data sources 24/7 and enabling organisations to operate both efficiently and effectively.

Multimodal AI refers to models that can process and analyse both structured and unstructured data from multiple types of data inputs, such as text, images/video, audio/sound and vibrations.

This enables gen AI to provide:

• Descriptive insights: Informing manufacturers about what is happening in their operations
• Predictive insights: Forecasting potential issues or problems in the future
• Prescriptive insights: Recommending actions manufacturers can take to address predicted issues

He notes that the power of knowing when a machine is about to fail is immense.

With multimodality, a system can listen to the vibration or noise of a machine and predict its breakdown.

By analysing a range of data sources, including market trends, weather data and global events, multimodal AI can identify patterns, correlations and trends.

This helps manufacturers to more accurately forecast demand and make data-driven decisions to enhance supply chain resilience.

In the coming year, Praveen predicts that more manufacturers will experiment with these capabilities and identify use cases that improve everything from workplace safety to customer experiences.

2. Gen AI will drive customer-centric manufacturing

Praveen points out that over the past five years, there has been a significant shift towards online shopping, with customers purchasing everything from consumer packaged goods to cars online.

Customers now expect products to be customisable and interconnected, pushing supply chains to adapt to manufacturing needs, rather than the other way around.

He anticipates a shift from the traditional stock-and-sell sales model to a more complex make-to-order model in the future.

To enable this shift, manufacturers need a robust, real-time view of their operations. This begins with data.

Praveen emphasises that manufacturers must unify their IT and OT data and by applying analytical and AI tools on top, they can generate actionable insights that optimise everything from product design and production efficiency to targeted marketing and proactive customer service.

For business operations, Praveen argues that uncertainty is the enemy of profits.

A lack of visibility leads to unplanned downtime, which in turn results in waste and creates further uncertainty in meeting demands, managing inventories and facing supply chain challenges.

Real-time visibility into operations, enabled by OT-IT data integration, is key to reducing uncertainty, as well as excess or insufficient finished goods.

He suggests that to drive product innovation, manufacturers could leverage social media sentiment to pinpoint unmet needs and emerging trends or detect early warnings of product issues, addressing them before they escalate. This is already happening, as 56% of surveyed manufacturing and automotive organisations are using gen AI for new product and service development.

By feeding more diverse types of data—including unstructured data like social media posts and customer reviews—into industry-specific gen AI models, manufacturers can accelerate the design and development of new products that were previously unimaginable, as well as optimise distribution and inventory strategies in real-time.

3. Gen AI can help bridge the manufacturing skills gap

Praveen explains that a widening skills gap is impacting the manufacturing industry, as experienced workers retire and manufacturers struggle to attract or retain new talent with the necessary skills.

This challenge is exacerbated by the rapid pace of technological change, demanding that front-line workers are equipped with real-time problem-solving tools.

He highlights that generative AI offers a powerful solution to the manufacturing skills gap by transforming how knowledge is shared and applied.

Imagine AI-powered agents that capture the expertise of seasoned workers and deliver personalised training programmes to new employees.

These agents could act as virtual mentors, providing guidance, answering questions and offering real-time feedback.

By automating repetitive tasks and augmenting human capabilities, generative AI allows less experienced workers to take on more challenging roles, effectively enhancing the skills of the existing workforce.

In fact, over the next two to three years, 54% of manufacturing and automotive leaders surveyed indicated they plan to use gen AI to increase employee productivity.

Moreover, Praveen adds that generative AI can optimise workflows, predict maintenance needs and even translate languages, making manufacturing more efficient and inclusive.

This not only improves productivity but also creates more engaging and accessible jobs, attracting new talent to the industry.

As we look to 2025, Praveen predicts that the adoption of gen AI in manufacturing will accelerate, with its applicability expanding across a wide range of use cases.

To minimise disruption and ensure a successful transition, manufacturers should implement gen AI technologies gradually, allowing employees to adjust to the changes step by step.

Successful integration, he argues, depends on transparent communication, comprehensive training and ongoing support as well as resources that empower employees to troubleshoot issues and optimise their use of the new tools.

By starting small with pilots, manufacturers can scale up AI implementations and reap the rewards to achieve their vision.

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