Generative AI (GenAI) attracted significant interest in 2023 and has already begun to impact horizontal and industry applications and use cases. According to our predictions for 2024, it’s anticipated that in 2026, half of G2000 companies will have integrated operational systems with GenAI to better ingest data, identify issues, and provide real-time context to operators, improving efficiency by 5%.
GenAI’s influence on the manufacturing sector is poised to be pivotal. It has already triggered a transition in which AI is omnipresent, no longer an emerging software segment amidst the technological stack.
Numerous firms, including industrial organizations, are assessing how AI can bring value to their operations. They may not have been early adopters of GenAI, but industrial organizations are well-placed to utilize the technology to generate diverse content and conduct extensive research. Algorithms can be trained using existing large data sets to produce text, video, images, even virtual environments.
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Guidelines to Develop GenAI-powered Use Cases
To help organizations learn from company experiences, successes, and challenges in developing GenAI-powered use cases, I have established some guidelines:
Do Not Underestimate Implementation
GenAI holds a lot of promise, but implementation carries risks that adopters have to watch very carefully. Appropriately trained and utilized, it proves reliable and can be implemented at a reasonable cost. From my perspective, organizations should view GenAI-powered solutions as an integral part of a digitally enabled strategy, particularly in fields like asset maintenance.
It’s essential to meticulously plan each phase of the solution’s implementation journey. The desired goals should be outlined, and key performance indicators should be identified. Regarding ROI, the total cost of ownership should be accounted for, including OPEX.
During the planning stages, organizations should project how the solution will scale and integrate with existing IT systems (especially in terms of technology standards). Organizations should also not undervalue the importance of the post-implementation period. Establishing review cycles with technology partners is crucial to ensure that user feedback is appropriately addressed. Finally, organizations should engage in discussions with experts who can provide insights into other areas that could benefit from GenAI solutions.
Expand on Technology Partnerships
I recommend that organizations forge partnerships with technology providers and establish trusted relationships that foster the sharing of goals, capabilities, and values. A collaborative approach enables organizations to expedite and expand innovation. Due to the potentially lengthy journey from proof of concept to implementing company-wide solutions, organizations should ensure that their partners are capable of delivering scalable solutions and offering guidance throughout the implementation process.
When constructing a private and secure GenAI environment, organizations should consider technology partners capable of transferring internal data into large language models (LLMs) securely and without loss. Such partners can also facilitate knowledge transfers to internal staff for ongoing management and proficiency.
Keep Security in Mind
Organizations should be on guard against potential data leaks and biases, while also retaining control over the IT processes operating in the background. It is vital to establish a governance mechanism to tackle concerns related to privacy, manipulation, biases, security, transparency, disparities, and potential workforce displacement.
I suggest actively participating in specialized drills aimed at mitigating the risk of sophisticated phishing attacks. Organizations can also enhance data security by updating their data infrastructures to meet the expanding data requirements of GenAI models.
Be Creative in Finding New Use Cases
Organizations should prioritize using AI to deliver value and enhance business outcomes; AI should not be pursued for its own sake. The decision-making process regarding ROI involves various parameters. Early adopters have suggested focusing on one of the most critical aspects: the strategic fit of the investment. A fundamental approach is to give priority to initiatives that offer the most beneficial outcomes but require the least effort. Based on the experiences of GenAI adopters, I support adopting an agile methodology and the minimum viable product (MVP) strategy, which should prevent investment in non-value-added projects.
In a recent interview with an end user, it was revealed that 100+ potential use cases were identified during GenAI ideation workshops. Of these, two have already been launched as MVPs, and 14 are in active development.
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Conclusion
GenAI solutions are transforming manufacturing operations, improving efficiency, facilitating data-driven decision-making, and simplifying complex processes for frontline workers. By implementing these innovative practices, organizations can adapt to the changing manufacturing landscape and significantly enhance operations.
Our research indicates that the adoption of GenAI by manufacturing organizations is still in the early stages. However, there has been a notable increase in GenAI awareness: IDC’s July 2023 Future Enterprise Resiliency and Spending Survey revealed that just 19% of manufacturing organizations were unaware of GenAI, compared to 35% in March 2023. This trend suggests that GenAI is steadily being integrated into the technology frameworks of organizations, putting them on an innovation trajectory.
To explore more of our coverage on Gen AI, visit our dedicated page.