For manufacturing enterprises, the advent of artificial intelligence (AI) will reshape the source of value creation, the formation of new business models, and the delivery of value-added services such as mass customization, predictive maintenance, and “product servitization” (i.e., the process of building revenue streams for manufacturers from services related to your products). As AI becomes more prevalent in various aspects of business management and operations, investing in people will become even more important. AI and automation will not displace people but rather combine their capabilities in new ways to create new forms of value and new opportunities. The manufacturers that identify how to empower their workforces through AI applications will create the greatest value going forward.
For manufacturers who fear they are behind the curve in preparing for this AI revolution, you are not alone: Just 5% of MAPI member companies have mapped where AI opportunities exist and developed a clear strategy for sourcing the data that AI requires. And 56% of companies currently have no plans to do so. The picture improves over the next five years, as 14% of respondents expect to have completed such mapping by then, while 63% expect to be in the process of doing so. This summary outlines the key findings and recommendations to guide manufacturers in the next leg of the transformative journey with artificial intelligence and their workforce.
AI Making Inroads in Manufacturing. AI is most commonly deployed in industrial robotics, machine vision, intelligent products, machine learning, and cobots. Over the next five years, industry leaders expect significant growth in predictive systems and in their use of AI to manage intelligent supply chains. Manufacturers also expect significantly increased use of robotic process automation (RPA).
Significant Technical and Workforce Barriers Remain. At present, a lack of clarity about how to implement AI solutions, and a lack of interoperability between equipment are the most significant barriers to deployment. However, these are paired with significant workforce challenges, including a lack of employees with the necessary digital skills to implement AI or understanding of how to define the AI skills needed.
New Roles Emerging for Humans and Machines. AI will generate new roles where human capacity will reign supreme (e.g., creating and judging) and others where machines will outperform humans (e.g., iterating and predicting). Hybrid roles will arise where humans will enable machines (e.g., in training, explaining, and sustaining) and where AI will augment human capabilities (e.g., in amplifying, interacting, and embodying).
New-to-World AI Jobs on the Way. Few organizations have introduced dedicated new job categories focused on AI. However, such jobs are emerging. Fully 43% of manufacturers have added “data scientists/data quality analysts” to their workforces, and 35% more expect to do so within the next five years. A sizable proportion of manufacturers are also creating “machine learning engineers or specialists” (33% today, 70% within five years), “collaborative robotics specialists” (29%), and “data-quality analysts” and “AI solutions programmers/software designers” (26%).
Demand for Fusion Skills. “Fusion skills” refer to the combination of human and machine talents within a business process to create superior outcomes to either working independently. Fusion skills will be needed in training, explaining, and sustaining activities (such as human judgment enabling improvement in the performance of machines); in expanding employees’ capabilities (such as machine intelligence enabling humans to make decisions); and in tasks in which humans and machines will jointly excel together (such as iterative processes in which each learns from the other).
AI-Skilled Workforce in Short Supply. In terms of developing an AI-savvy workforce, a plurality of manufacturers believe the biggest barrier to acquiring AI-skilled workers arises from an insufficient number of graduates with the needed knowledge and skills graduating from educational programs. This is closely followed by difficulty in attracting skilled candidates due to reputational issues revolving around manufacturing, and by a perceived lack of mechanisms to retrain existing workers with the requisite AI skills.
Learning and Development Vital to Unlocking AI Potential. Manufacturers are pursuing several strategies to promote the development of AI-skilled workers.
- A majority of companies expect to pursue a combination of both retraining and hiring employees with the needed AI/data science skills over the next five years
- Many are building relationships with local academic institutions (including high schools, community colleges, and universities)
- Some have turned to massively open online courses (MOOCs) and other forms of online education
- Some have developed internal training courses
At the same time, reflecting how early the application of AI is for many manufacturers, almost half are not yet supporting AI skills development for their workers.
Six Recommended Elements of the Manufacturing Evolution
A sustainable AI and workforce transformation will require a deliberate and comprehensive change management strategy. We have six recommendations for business leaders:
1. Create teams to drive digital transformation in the enterprise.
- The digitalization of manufacturing, including the application of AIbased solutions, heralds the most significant transformation to manufacturing in a generation.
- Manufacturers will need dedicated teams to navigate this transformation, such as digital command centers and digital business teams tasked with leading the deployment of emerging digital technologies.
2. Define an “AI governing coalition” for AI transformation.
- Manufacturers should define their own AI transformation strategy to assess the company’s processes and operations and then evaluate how the application of AI-enabled systems could transform and improve them.
- This “AI governing coalition” of business, IT, HR, and analytics leaders owns activities such as setting the direction of AI projects, analyzing problems to solve with AI, and managing internal change.
3. Evaluate AI and workforce transformation readiness.
- A workforce transformation strategy should consider what AI-specific jobs need to be created and how to provide relevant AI training to employees at every level.
- Manufacturers need an inventory of what AI skills the company will need, to ascertain to what extent internal resources can fill these needs, or skills that need to be acquired externally, and develop a plan to train and upskill workers.
4. Set measurable objectives for digital and AI transformation.
- Companies shouldn’t deploy AI technologies for technology’s sake—all implementations of digital technologies should address a clear business need and be supported by a reasonable return on investment rationale.
- Manufacturers should define annual objectives for how the application of AI can help meet key performance indicators such as overall operating efficiency and productivity growth.
5. Redefine digital and physical product innovation processes.
- The advent of digitally-based innovation creates a need to speed time to market, but this presents a challenge to the stage-gate models used to manage product development and innovation cycles.
- Companies will have to modify their product development processes to accommodate digital transformation while still meeting key safety, reliability, and product quality standards for their finished products.
6. Overinvest in communication for change management.
- Effective practices include developing a communications process to explain the implications of AI applications and solutions to employees, customers, and partners.
- Some companies have already set up worker councils to facilitate dialogue between the front office and the front line about how the advent of AI will change workers’ roles and responsibilities in the AI era.