Over the past year, manufacturers worldwide have made significant strides in adopting artificial intelligence technologies, moving beyond the initial hype to tangible implementations that deliver real business value.
According to Deloitte’s 2024 Future of the Digital Customer Experience survey, more than half (55%) of industrial manufacturers in the United States are already leveraging advanced AI tools to optimise their operations. Meanwhile, 40% are looking forward to increasing their investment in AI and machine learning over the next three years.
However, significant challenges remain on the path to AI adoption. A separate survey highlights that nearly 70% of manufacturers identify data-related issues as the most significant obstacle to AI implementation. These issues include problems with data quality, contextualisation, and validation.
For the Australian manufacturing sector, these statistics take on particular significance. The industry’s success in overcoming these obstacles and effectively implementing AI technologies depends heavily on its ability to access, manage, and utilise high-quality data. As Australian manufacturers set their sights on integrating AI and GenAI into their operations, tackling these data-centric issues becomes paramount. To mitigate these challenges and enhance potential returns, manufacturers may find value in prioritising two AI usages with pre-existing, solid data frameworks.
Customer service applications: Chatbots and beyond
Customer service applications emerge as a prime example of AI implementation in manufacturing. These applications, which are predominantly digital and language-based, offer manufacturers a unique advantage in the AI landscape. They tap into a rich vein of existing data that is often readily accessible and requires minimal preprocessing.
According to Deloitte’s survey, 74% of manufacturers are either planning to use or already rolling out generative AI to enhance customer experiences. This high adoption rate underscores the perceived value and accessibility of these solutions.
A common example of this application is gen AI-based virtual chatbots. These advanced AI tools enhance the customer’s purchasing experience by swiftly analyzing product details and features. This technology not only improves customer satisfaction but also enables human staff to concentrate on more intricate customer needs. AI is also revolutionising after-sales support through combined generative AI-based service manuals and augmented reality technology.
Product design applications: AI-driven product innovation
AI is also making significant transformations in product design, offering manufacturers unprecedented opportunities to revitalise their product lines. A recent forecast by data provider IDC projected that by 2028, half of all large manufacturing companies would be leveraging generative AI to analyse their engineering archives.
This strategic use of AI aims to uncover new opportunities for innovation, particularly in legacy products that may have untapped potential. For Australian manufacturers, this presents a unique opportunity to breathe new life into established product lines and potentially discover entirely new market opportunities.
By applying AI to analyse years of engineering data, companies can identify patterns, trends, and possibilities that human designers might overlook.
A tailored approach for GenAI
As the industry braces for what 2025 has to bring, the strategic implementation of AI, particularly generative AI, emerges as a critical factor for manufacturers seeking to maintain competitiveness and drive growth. As Deloitte argues, the key for manufacturers may lie in identifying targeted opportunities for AI investment. Rather than pursuing broad, sweeping AI initiatives, companies should focus on specific applications that promise tangible returns and align closely with their core business objectives.
To gain deeper insights into AI adoption trends and strategies in manufacturing, explore Deloitte’s 2024 Future of the Digital Customer Experience survey.
