Industrial Products
AI gains traction among manufacturers — yet most face a steep learning curve
As manufacturers continue to adopt digital technologies in their operations, supply chains and products, most are also looking to artificial intelligence (AI) to leverage the data via those technologies to yield greater efficiencies, productivity and growth. While AI has been on the sector’s radar for years, only a minority of manufacturers agree that their organizations are high on the AI maturity curve. About one-quarter of manufacturing enterprises (26%) have processes in place that are fully enabled by AI with widespread adoption, and a further 29% have begun implementing AI use cases, according to PwC’s most recent AI Predictions survey. Meanwhile, the COVID-19 pandemic has had an uneven effect on AI initiatives. The crisis has accelerated AI efforts for about half (54%) of manufacturers, yet it has delayed them for about one-third (31%).
What are they looking to get out of AI?
The top goal of manufacturers’ AI strategies is boosting operating efficiencies and increasing productivity (with 51% agreeing this was a primary goal). Other aims include growing revenue (37%), innovating products and services (33%), and improving internal decision-making (33%).
(Still) raising their AI IQ
Most manufacturers are also setting their sights on building an AI-savvy workforce. Two-thirds (66%) have developed a workforce plan that identifies new skills and roles emerging from AI adoption, and half (50%) have implemented upskilling and continual learning initiatives that include AI. Nearly half (45%) have implemented credentialing programs for data science and more advanced AI skills.
Looking into the future: Priorities and concerns
Despite the traction that many manufacturers are gaining with AI, long-term concerns and perceptions surround the technology. More than one-third of manufacturers (37%), for instance, agree that AI could create new cyber vulnerabilities over the next five years. Other top threats include AI becoming too complex and/or intelligent to understand or control (34%) and an inability to meet demand for AI skills (34%).
Overall, top AI priorities include developing AI models and data sets that can be used across the organization (with 31% agreeing this is a top priority); training current employees with AI systems (28%); and measuring AI’s return on investment (27%). More specifically, the AI and analytic applications that are most important to manufacturers are those that help manage risk, fraud and cybersecurity (with 32% agreeing it is a top priority), automating routine tasks (28%) and gathering forward-looking intelligence (28%).
PwC's Industrial Products Services
Perspectives on critical business issues impacting industrial companies
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