GenAI Adoption – Challenges in Manufacturing Enterprise

3 mn read

While discussions have been ongoing regarding the use of fine-tuned Large Language Models (LLMs) for specific enterprise needs, the high cost associated with cloud-based LLMs, including subscription fees and API usage charges, is becoming increasingly evident. This cost barrier has been a major hurdle for many enterprises seeking to transition GenAI-powered solutions from pilot programs to production environments.

Conversely, Small Language Models (SLMs) appear to be a more suitable option for businesses seeking specialized applications due to their lower cost and resource requirements. Enterprises typically operate with constrained budgets. Piero Molino (Chief Scientific Officer & Cofounder of Predibase, creator of Ludwig, formerly of Uber’s AI Lab), predicts that SLMs will be a major driver of enterprise adoption in 2024 due to their attractive financial proposition.

But within the enterprises sector, Manufacturing Enterprises will likely to be one of the slowest adopters of GenAI in their operations. Especially Medium and Small manufacturers. Let us explore the reasons because the combination of Industry 4.0’s data collection and connectivity with GenAI’s analytical and generative capabilities has significant potential to transform manufacturing into a more autonomous, intelligent, and efficient system.

Business Hurdles


The high cost of Large Language Models (LLMs) is a major hurdle for their adoption in manufacturing. LLMs require massive computing power for training and inference, making the enterprises reliant on cloud providers. However, cloud provider fees can scale rapidly with model size and usage, and vendor lock-in can be a concern.

Small Language Models (SLMs) offer a potential solution. Their lower computational footprint makes on-premises deployment a possibility. However, implementing SLM requires expertise in machine learning and LLM training, which some enterprises may lack. Hiring additional staff or finding a vendor with this expertise is an option, but maintaining an SLM on-premises can be complex and requires significant IT infrastructure.

For many manufacturing enterprises, the complexity and cost of on-premises SLM maintenance might outweigh the benefits of reduced cloud costs. This could lead them back to cloud based SLMs, landing them where they started.

Security Concerns

Security concerns around data privacy are a major hurdle for manufacturing companies considering both external vendors and cloud adoption. Usually Medium and Small manufacturers have viewed cloud with apprehension.

Change Management

Implementing Generative AI (GenAI) solutions can necessitate significant modifications to existing manufacturing software and may require changes to current processes. While change management might be straightforward for greenfield projects (entirely new systems), most implementations will be brownfield projects (upgrades to existing systems). Manufacturers are understandably hesitant to disrupt well-functioning manufacturing processes unless there’s a compelling reason. Therefore, a robust business case and a well-defined plan for minimizing disruption during change management are crucial.

Technical Hurdles

Data Challenges

GenAI models require large amounts of clean, labelled data to train effectively. Manufacturing processes can be complex and generate data that is siloed, inconsistent, or proprietary. So, unless there are existing Observability solutions which has captured sensor telemetry over period, the manufacturer cannot directly introduce a GenAI solution. Additionally, companies may be hesitant to share this data with external vendors.

Integration Complexity

Integrating GenAI solutions with existing manufacturing systems can be complex and require expertise in both AI and manufacturing technologies. Vendors may need to have experience working with similar manufacturing systems to ensure a smooth integration. Existing vendors may have to be roped in for the integration, which would incur additional cost. Integration governance could become complex.

Lack of Standardization

The field of GenAI is still evolving, and there is a lack of standardization in tools and techniques. This can make it difficult for companies to evaluate and select the right vendor for their needs.


SLMs are likely less susceptible to hallucination and bias compared to LLMs. SLMs are trained on a smaller amount of data, typically focused on a specific domain or task. SLMs have a simpler architecture compared to LLMs. Hence, they are less prone to situations where the model invents information or connections that aren’t there.

Data Quality Still Matters. Even with a smaller dataset, bias can still be present if the training data itself is biased. Bias, in case manufacturing systems, is about plant-shift based bias, machine life bias, role importance bias, vendor bias etc. Bias can also start building up through feedback loop from new production output.

Less Established Tools and Expertise

There are fewer established tools and frameworks specifically designed for SLMs compared to LLMs. Finding experts with experience in implementing SLM-based GenAI solutions might be more challenging.


What you will notice is that though there is cost reduction by using SLM instead of LLM, the challenges and hurdles remain almost the same. The hesitation that existed in Manufacturers for LLM based solutions remains for SLM based solutions. In many cases preventing the manufacturers moving from pilot to production. That hesitation needs to be tackled on priority basis to unlock the potential of SLM for the future of smart manufacturing.

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