The Obstacles of AI

Is AI the answer to the next manufacturing revolution? Our report, Manufacturing Intelligence, highlights many use cases along the operations and production process. However, not all companies are integrating the growing plethora of AI-based tools and resources into daily operations. Through surveys and interviews, Manufacturers Alliance Foundation has been exploring examples where AI has been a game changer, but what are the obstacles most likely to interrupt AI and digitalization plans?  

According to the HG AI Maturity Index, the manufacturing industry ranks fifth among Fortune 500 companies for their maturity level across industry trends, behind tech, telecommunications, finance, and healthcare. In our survey of manufacturers, about 95% of respondents shared concern over their organizations’ readiness for AI implementation. While the reasons vary for each organization, it finds that most companies have a long roadmap ahead with a lot of barriers to navigate.  

The Primary Obstacles

Learning Curves

The concerns about adoption should come as no surprise. The number one obstacle found in our research was the learning curve for AI applications. In manufacturing executive interviews, we found that many companies use internal steering committees to establish AI projects and monitor progress to disperse the responsibility and collect the knowledge from a variety of key stakeholders. Additionally, manufacturers are utilizing partnerships with vendors specializing in AI solutions, independent experts, and startups to progress their AI and digital technology adaptation plans, assisting against the internal missing skillset along the way.

Even with partnerships and steering committees, manufacturers still have some high-level challenges to fully adopt AI. One manufacturer specifically told us that they would like to see more training offered to utilize AI-based applications and sees the lack thereof as a huge downfall for their organization’s potential to higher levels of implementation and utilization.  

Key Obstacles for Manufacturers Adopting AI

1. Learning curve for AI applications in manufacturing
2. People: skill sets, retrain
3. Cybersecurity concerns
4. Legal issues: IP issues, privacy, built-in unconscious bias in machine learning
5. Intrinsic resistance to AI “taking over” workers

Source: Manufacturers Alliance Foundation survey, 2024.

Evolving Skillsets

This leads to the second obstacle, the need to redefine skillsets and retrain employees to operate AI-based systems. One of the recommendations from Manufacturing Intelligence is to start by measuring workforce capabilities and skills, evaluate and determine what new skills are required for AI integration, and design talent attraction and upskilling programs to source and develop a workforce that can maximize (or at least utilize) the continual emergence of AI-based technology.  

Joanna Cooper of Daimler Truck North America shared “it’s all about upskilling people to add value in a much different way. It’s a fallacy to say that AI won’t eliminate some jobs. It will. But it won’t necessarily eliminate the person’s ability to add value to the organization, and those are two very different things.”

Over half of organizations are addressing the potential impact on talent by offering career development opportunities and incentives for continuous learning and professional growth. Developing and keeping talent is no small feat. With the competition for talent, creating opportunities and prioritizing continuous learning could go a long way. So does fostering the right culture. Forty-four percent of those surveyed are dedicated to fostering a culture of innovation and adaptability to encourage employees to embrace change and new technologies. It is critical for leaders to recognize the opportunities that AI brings and communicate these opportunities to employees. “Part of the leader’s role is to drive a confidence level into the team and say, ‘this can happen, it will happen, and there will be a culture change,’” Martin Smith of Danfoss Power Solutions stressed in our AI report.

How Are Organizations Addressing the Potential Impact on the Workforce?

Source: Manufacturers Alliance Foundation survey, 2024.

Don’t Forget the Digital Obstacles

Digitalization is rapidly expanding the utilization of higher-tech options. As manufacturing remains the most-attacked industry by cybercriminals, risk mitigation, access control, proper compliance remains of upmost importance. When it comes to cybersecurity concerns, safeguarding sensitive information used in AI processes is the priority for manufacturers. The second concern listed by respondents is developing and implementing cybersecurity measures tailored to AI-specific risks.  

Top 7 Cybersecurity Concerns for Manufacturers When Implementing AI

Source: Manufacturers Alliance Foundation survey, 2024.

As manufacturers figure out their next steps with AI, regulatory oversight follows suit. Cybersecurity and data privacy concerns were the top two compliance or regulatory challenges manufacturers face. Federal and state regulators of AI oversight continues to ramp up.

Regulatory or Compliance Challenges Associated with AI

Source: Manufacturers Alliance Foundation survey, 2024.

Want more? AI involves everyone. Stay up to date with AI-related information, research, and events by checking out AI in Manufacturing.