How to set the course toward 2040
Manufacturers and their factory managers will need to take action in four areas to enable hyper-automated factories:
Workforce transformation: Experienced workers are retiring, while younger generations show little interest in manufacturing careers. In the U.S., manufacturers will need to fill 3.8 million roles over the next decade (according to The Manufacturing Institute). At the same time, companies are investing three times more in AI technology than workforce training, making it harder to develop the talent needed for the future. Without immediate action, manufacturers risk losing critical knowledge and long-term competitiveness.
Manufacturers will need to embed AI-driven learning into daily operations and redefine roles. The future workforce will shift from manual labor to process oversight, decision-making and optimization, requiring new skills in automation, data analytics and AI integration. Companies like JLR are investing $25 million annually to equip employees with the skills needed to transition into technology-driven roles.
Committing to automation: Sixty-three percent of factory managers prioritize automation to boost efficiency and cut costs today, yet a gap remains between current priorities and the factory of the future. For example, only 38% target implementation of hyper-automation when building new units. Most focus on automated warehouses synchronized with manufacturing—an essential step, but not enough for future resilience and competitiveness.
To bridge this gap, companies must choose between upgrading existing sites or building hyper-automated factories. Xpeng Motors, for example, deployed 264 intelligent robots across production, and BMW’s humanoid robots boosted efficiency by 400% at their Spartanburg, S.C., facility. It starts with acknowledging that automation isn’t a future concept, it’s shaping industries today.
Use AI to move from assistance to autonomy: Contrary to popular belief, AI adoption remains low today, despite its ability to optimize how work gets done. Nearly one-third of factory managers still hesitate to use generative AI, despite two in three recognizing its importance. This reluctance stems from lingering mistrust, lack of awareness and poor data quality, which hinders the development of reliable AI models and large-scale integration.
To build the factory of 2040, manufacturers need to set the course now. This means moving beyond basic data collection to real-time AI decision-making and deploying AI co-pilots across operations — from predictive maintenance to supply chain coordination. Of course, what also matters is investing in cleaner data, scalable AI models and workforce upskilling to manage AI-driven systems effectively.
Keep strengthening the digital core: Digitization is the foundation for the hyper-automated factory. And yet, our survey found that most factory managers are still focusing on digitization measures that should, arguably, already be in place. By far their highest priorities are cybersecurity measures (77%), followed by the implementation of manufacturing execution systems (70%) and cloud platforms (67%).
Critical components such as digital machine and product twins, IIoT or even edge computing are not key priorities for nearly half of the factory managers surveyed. Yet without these technologies, factory managers won’t make the leap to Design for Manufacturing (DfM). DfM is a building block of hyper-automated factories where production lines dynamically adapt. It means already thinking about how to manufacture something when designing it, instead of figuring out how to manufacture it after. Taking DfM principles into active automated production requires digital twins of product, process and system to enable rapid virtualized simulations.
Factory managers understand these enablers well. Now they must turn that understanding into actions that serve them well in the current environment and support their longer-term vision.
Brian R. May is managing director, Industrial North America at Accenture.
