Jan Burian
Jan Burian (Head of IDC Manufacturing Insights EMEA, IDC EMEA)

San Francisco-based OpenAI’s introduction of ChatGPT on November 30, 2022, marked a significant milestone in the development of large language models (LLMs) and generative AI (GenAI) technology. The launch by OpenAI, the creator of the initial GPT series, sparked a race among technology vendors, system providers, consultants, and app builders. These entities immediately recognized the potential of ChatGPT and similar models to revolutionize industry.

2023 saw a surge in efforts to develop GenAI tools that are smarter, more powerful, and less prone to hallucinations. The competition led to an influx of innovative ideas and tools aimed at harnessing the capabilities of LLMs. The goal became to leverage these models as ultimate tools to enhance productivity, competitiveness, and customer experience across diverse sectors.

With ChatGPT paving the way, a broad range of organizations and professionals are exploring how to integrate GenAI into workflows and solutions. The widespread interest and investment have underscored the technology’s transformative potential and laid the groundwork for its continued evolution in the years to come.

4 Uses Cases for GenAI in Manufacturing

In manufacturing organizations, the utilization of GenAI-powered tools and solutions is primarily focused on four key areas:

  1. Content Generation: This includes automated report generation, in which GenAI algorithms are employed to automatically generate reports based on predefined parameters and data inputs.
  2. User Interface Enhancement: This involves the integration of chatbots into user interfaces, enabling more intuitive and interactive communication between users and systems.
  3. Knowledge Management: GenAI facilitates knowledge management by providing co-pilot services that help users access and interpret vast amounts of data and information.
  4. Software and Delivery: This encompasses various applications, such as code generation, in which GenAI is leveraged to automate the creation of software code, streamlining development processes.

According to IDC’s GenAI ARC Survey of 2023, manufacturing organizations are actively evaluating or implementing GenAI solutions.

Around 30% of European respondents have already invested significantly in GenAI, with spending plans established for training, acquiring Gen AI-enhanced software, and consulting. Nearly 20% are doing some initial testing of models and focused proofs of concept, but don’t yet have a spending plan in place.

These results suggest steady growth in the adoption of GenAI-powered tools and solutions within the manufacturing sector. The initial hype surrounding GenAI in 2023, fueled by its perceived potential as a “wonder technology,” has evolved into a pragmatic recognition of its capacity to address ongoing challenges such as workforce shortages, skills gaps, language barriers, data complexity, regulatory compliance, and more.

In the manufacturing industry, GenAI is increasingly viewed as an enabling technology capable of facilitating innovation and overcoming barriers to success.

Framework for Manufacturing Organizations to Implement GenAI

To fully capitalize on the potential of GenAI pilots, manufacturing organizations recognize the need for comprehensive frameworks that encompass processes and policies. Key measures include:

  • Data Sharing and Operations Practices: Organizations should prioritize the implementation of practices that ensure data integrity for LLMs developed internally or in collaboration with third parties. This ensures that data used in GenAI models is accurate, reliable, and ethically sourced.
  • Corporate-Wide Guidelines for Transparency: Guidelines should be established to evaluate transparency and track the use of GenAI code, data, and trained models throughout the organization. This promotes accountability in GenAI usage.
  • Mandatory GenAI Awareness and Acceptable Use Training Programs: Mandatory training programs should be implemented to raise awareness of GenAI capabilities and ethical considerations among designated workforce groups. This helps ensure that employees understand how to responsibly utilize GenAI technologies.

As excitement over the capabilities of GenAI has died down, organizations are becoming increasingly aware of the risks posed by potential intellectual property theft and privacy threats linked to the technology.

To address these concerns, many organizations are prioritizing the establishment or expansion of formal AI governance/ethics/risks councils tasked with overseeing the ethical use of GenAI and mitigating risks associated with privacy, manipulation, bias, security, and transparency.

As a manufacturing interviewee in one of my studies put it, “The governance framework is indispensable in ensuring responsible and ethical AI implementation.” This underscores the importance of implementing robust governance measures to ensure the ethical use of GenAI within manufacturing organizations.

Deployment Strategies

Strategies for selecting the right solution for the right use case can vary substantially. A global white goods company, for example, piloted several GenAI-powered use cases in 2023. Its selection and deployment strategy encompassed a range of approaches, including:

  • Off-the-Shelf Solutions: The company utilized ready-to-use, commercially available GenAI-embedded software-as-a-service solutions. These offered immediate access to GenAI capabilities without the need for extensive development or customization.
  • AI Assistants: It deployed AI assistants to support specific tasks within their business processes. These assistants helped, for example, to create designs based on predetermined workflows, providing valuable support and efficiency gains.
  • AI Agents: The company deployed AI agents in complex use cases requiring the orchestration of workflows and decision-making based on AI-driven insights. The agents leveraged GenAI to analyze data and make informed decisions autonomously.

A primary challenge often mentioned in such endeavors is selecting the optimal LLM for company-specific use cases from a multitude of possibilities. With new models and solutions constantly emerging and becoming accessible, this task can be daunting. The selection process typically involves thorough market research, vendor presentations, and internal discussions about the technology framework underlying current and future use cases.

However, the success of GenAI ultimately hinges on the quality and quantity of the data utilized. Curating a diverse and sufficient data set is critical to ensuring unbiased outcomes and maximizing the effectiveness of GenAI solutions. Data curation therefore remains a cornerstone of success in leveraging GenAI technologies.

The Bottom Line

GenAI-powered technology holds immense potential across industries and regions, offering capabilities that traditional machine learning algorithms or neural networks may struggle to match in terms of breadth and depth. GenAI can assist in co-piloting humans, thereby addressing challenges associated with an aging and/or unqualified workforce.

However, organizations must prioritize addressing concerns such as data leakage, biases, and maintaining sovereignty over IT processes running in the background. These issues must be carefully managed to ensure the responsible and ethical implementation of this powerful technology.

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