Manufacturing Functions Adopting AI
The Manufacturing Leadership Council’s (MLC) recent study on the future of Industrial AI in manufacturing reveals both the immense potential of AI and the cautious approach manufacturers are taking in its adoption. While the world is witnessing fierce competition for AI supremacy, the manufacturing sector remains deliberate in its approach. According to the study, 57% of manufacturers are still in the experimental phase, seeking to identify the best applications for AI. Only 29% have integrated AI into formal corporate strategies, and 28% are applying it operationally. Yet, nearly all (96%) expect their AI investments to increase significantly in the coming years.
Early Stages of AI Adoption
The fact that 46% of manufacturers are still in the “defining a roadmap” phase, and 39% are engaged in small-scale pilot projects, underscores that the manufacturing sector is still in the exploratory phase of AI adoption. This is a clear indicator that while AI’s potential is widely recognized, manufacturers are proceeding with caution, likely due to the complexities and risks associated with integrating AI into existing operations.
My take: This cautious approach is not necessarily a drawback. By carefully planning and experimenting, manufacturers can better understand the specific needs and challenges of their operations, which will lead to more effective AI implementations in the future. It’s a strategic move to avoid the pitfalls of hasty adoption, which could lead to costly mistakes.
Challenges in Measuring AI’s Impact
A significant finding is that 61% of manufacturers lack specific metrics to measure the effectiveness of their AI deployments. For those who do have metrics, the focus is primarily on asset availability and quality of results. This gap in measurement is telling—it suggests that many manufacturers are still grappling with how to quantify the benefits of AI. Without clear metrics, it becomes challenging to justify AI investments or understand its true impact.
My take: The lack of metrics highlights a broader issue within the industry—the difficulty in translating AI’s theoretical benefits into tangible, measurable outcomes. This is likely a reflection of the early stage of AI adoption, where the full range of AI’s capabilities is not yet understood or realized. Developing a robust set of metrics will be crucial for manufacturers as they move from experimentation to full-scale implementation.
The Workforce Conundrum
Contrary to the common fear that AI will lead to job losses, 32% of manufacturers anticipate that their workforce headcount will increase due to AI, while only 21% expect it to decrease. This finding challenges the narrative of AI as a job killer and instead positions it as a tool that could create new opportunities within the industry.
My take: This suggests that AI in manufacturing is likely to shift the nature of work rather than eliminate jobs. As AI takes over routine and repetitive tasks, human workers will be freed to focus on more complex, value-added activities. This could lead to a more skilled, adaptable workforce, but it also underscores the need for significant investment in training and reskilling programs.
The Strategic Role of AI
With only 29% of manufacturers having formal corporate AI strategies, it’s clear that AI is not yet fully integrated into the strategic planning of most companies. This lack of integration could hinder the scaling of AI initiatives across the enterprise.
My take: AI’s success in manufacturing will depend on how well it is aligned with broader business strategies. Companies that treat AI as a standalone project, rather than a core part of their digital transformation, risk missing out on its full potential. Strategic alignment will be key to ensuring that AI projects are adequately funded, supported, and scaled effectively.
Budgeting for AI Training
There is a significant gap in AI training, with 65% of manufacturers having no dedicated budget for AI education. This is a critical issue, as the success of AI implementations hinges on the ability of the workforce to effectively manage and operate AI systems.
My take: The lack of training budgets could be a major bottleneck in the widespread adoption of AI in manufacturing. Without proper training, companies will struggle to harness the full power of AI, leading to suboptimal outcomes. It is imperative for manufacturers to prioritize training and upskilling to ensure that their workforce is prepared for the AI-driven future.
Three Key Recommendations for Manufacturers:
Prioritize the Development of AI Metrics: To effectively measure and optimize AI’s impact, manufacturers must develop clear and actionable metrics. This will help not only assess the ROI of AI projects but also fine-tune implementations to meet business objectives better. Start by focusing on metrics that align with key operational goals, such as production efficiency, cost savings, and quality improvements.
Integrate AI into Strategic Planning: AI initiatives should not exist in isolation. For AI to truly transform manufacturing, it must be integrated into the company’s overall strategy. This involves creating cross-functional teams that include members from IT, operations, and corporate strategy to oversee AI adoption and ensure that it aligns with long-term business goals.
Invest in Workforce Training: The future of manufacturing will be shaped by how well companies prepare their workforce for the AI revolution. Establish dedicated budgets for AI training and reskilling programs. This will not only enhance the effectiveness of AI implementations but also help in attracting and retaining talent in an increasingly competitive market.
References:
Manufacturing Leadership Council - The Future of Industrial AI in Manufacturing, 2023: https://bit.ly/IndustrialAI2023
