The current state of artificial intelligence (AI) in manufacturing tends to raise more questions than answers, given the hype around AI being the only path to competitiveness. However, a recent SAP insider survey titled AI—State of Adoption paints a conservative picture of the manufacturing sector.
According to this survey, 40% of manufacturing organizations have no plans to implement AI. However, an almost equal number of respondents (39%) plan to implement AI in the next 12-24 months or are evaluating it. Only 21% have actually used AI or are currently implementing it for their organization.
These figures suggest that while AI adoption is progressing, organizations are moving cautiously. This emphasizes the need for continued development and validation of AI technologies to meet their needs.
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In organizations planning to utilize or already implementing AI, the desire to improve business processes is an overriding factor in their adoption. In fact, the primary driver, as highlighted by 32% of respondents, is the need to automate repetitive and time-consuming tasks. This suggests a strategic shift by organizations towards enhancing operational efficiency and reallocating human resources to tasks that can significantly impact their overall productivity and competitiveness. The study indicates that cost reduction objectives are also important in organizations’ efforts to streamline operations and improve financial performance.
But these drivers only scratch the surface of AI’s potential to transform manufacturing. AI can be positive for any industry, provided it is set up using a pragmatic approach. ERP providers like QAD have taken steps to help manufacturers realize this goal for their people, processes and systems.
What this means for users
For organizations that use a pragmatic approach to AI, the benefits include:
Assistance — ERP systems like the ones created by QAD use AI to amplify human intelligence and increase productivity and efficiency by assisting users in daily tasks.
Holistic solution — A pragmatic approach to utilizing AI includes combining generative AI with solutions like machine learning and statistics, which form the core design of an AI model that can be customized according to the organization’s needs and business.
Responsible AI modeling — The AI models developed using a pragmatic approach integrate quality, privacy, and compliance with manufacturing processes. This limits biases by ensuring data quality and diversity. AI is also connected to full knowledge bases when using Large Language Models (LLMs) to help users retrieve the most relevant data.
Pragmatic AI — AI designed with a practical approach can answer direct business use cases and needs, keeping tangible customer return on investment in mind throughout its design.
