Bain & Company Report: How AI is becoming the top priority
Artificial intelligence (AI) is rapidly reshaping the landscape of the machinery and equipment sector, particularly in the realm of procurement.
As advanced manufacturing companies prioritise AI adoption, recognizing it as their top engineering and R&D priority, the procurement function emerges as a crucial domain where AI-driven innovations hold immense potential.
Addressing Industry Challenges Through AI Adoption
Amidst supply chain volatility, cost pressures, and a shortage of skilled labour, AI emerges as a vital tool for machinery executives. In fact, 75% of executives in advanced manufacturing prioritise adopting AI, revealing its transformative potential within the industry. However, many companies underutilize this essential AI enabler despite amassing substantial data.
Unleashing AI's Potential: Key Areas of Impact
Successful machinery firms reveal a key strategy: prior to AI investment, they pinpoint core challenges and assess AI's potential to enhance processes and performance. This involves evaluating AI types like machine learning (ML) and generative AI to leverage data effectively. Early adopters deploy AI across procurement, assembly, maintenance, quality control, and warehouse logistics, achieving significant improvements.
Furthermore, innovative approaches such as generative AI are reshaping knowledge work, synthesising vast data volumes to provide actionable insights. Experimentation with AI service bots demonstrates another frontier, enhancing maintenance processes and field operations efficiency.
By integrating AI solutions into workflows and backend systems, frontrunners in the machinery industry pave the way for enhanced productivity and performance.
Prashanth Parthasarathy, Expert Associate Partner at Bain & Company spoke during the company’s 2024 webinar on the report, he said: “Across the industry, it is no longer why AI, it is more about how to scale AI. Companies are no longer looking at how to pilot AI or look at new POC, they are asking how can I take it across my business function and make it a value.”
Overcoming Common AI Implementation Hurdles
Scaling AI initiatives from successful pilots to broader implementation across manufacturing lines or plants presents a significant challenge, yet it's a crucial endeavour. According to a 2022 MIT Technology Review Insights survey, scaling AI use cases to generate value ranks as the top priority for 78% of executives across industries.
To navigate these hurdles effectively, top-performing companies employ a strategic approach. They meticulously monitor return on investment throughout AI implementation, accounting for all associated costs, including often-overlooked computation costs on the cloud. Regular governance checks, conducted quarterly, ensure that AI investment decisions remain aligned with organisational goals.
Legacy software systems and fragmented data pose additional obstacles, creating a chaotic data environment with low-quality data. To mitigate these challenges, leading teams standardise analytics systems and platforms, enabling multiple AI use cases. Unified data models merge fragmented data sources, enhancing data quality and accessibility.
To adapt to the rapid evolution of AI, leaders embrace modular and loosely coupled components connected via microservices, facilitating seamless software replacement. When integrating generative AI, they ensure that new components enhance existing data architecture. Efficient processes and tools like MLOps/DevOps are integrated into the technical architecture to facilitate scalable AI deployment.
A test-and-learn approach is fundamental to successful AI implementations. Unlike traditional product design, software and AI work necessitate a fail-fast approach using Agile methodology. Close collaboration between plant engineers and AI experts enables iterative refinement of models until they align with company objectives.
Recruiting and retaining employees with strong AI skills pose ongoing challenges for machinery companies. To build in-house AI capability, many enlist external experts to train existing employees and enhance data literacy across the workforce. Strategies such as automating simple tasks and developing expert squads for complex AI use cases help retain skilled workers, ensuring sustained proficiency within the organisation.
Addressing these challenges requires a multidimensional approach. Leaders identify areas where AI unlocks maximum value, tailor technology to address core problems, and integrate it seamlessly with existing IT and operational technology setups. Flexible and scalable technology solutions ensure adaptability to evolving needs, while fostering a data culture that integrates AI skills and practices into the operating model.
As machinery executives increasingly embrace AI, companies experimenting with and deploying solutions are setting new benchmarks for productivity and performance. Those deferring investment in AI risk falling behind in an industry rapidly evolving towards AI-driven innovation.
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