AI On The Shop Floor: Are Manufacturers Accounting For The Full Cost?

Claudio Laterreur, CIO/CDO, Technology and Business C-Suite Executive, Transformation, Turnaround, & Growth, Advisory and Board Member.

Artificial intelligence (AI) has become one of the most compelling opportunities in manufacturing. Applications ranging from predictive maintenance and production optimization to quality improvement, operator assistance and increasingly agentic AI can make factories more responsive and productive.​

However, enthusiasm can create its own blind spot. Most manufacturers understand that AI depends on good data, yet many recognize their operational information is not where it needs to be. Machine data, ERP transactions, maintenance records, quality information, logbooks, spreadsheets and operator knowledge frequently remain fragmented, delayed or poorly contextualized. Second-hand data will inevitably produce second-hand AI predictions and/or recommendations.

On the shop floor, an incorrect recommendation can influence quality, safety, production, customers and financial performance. Data readiness is a prerequisite, and it should not be the end of the AI readiness discussion.

The broader question is, “Are manufacturers evaluating the full cost and risk of AI, or primarily the cost of implementing it?” The traditional business case is familiar, i.e., software, implementation and people measured against expected value. As AI moves closer to operational recommendations and decisions, manufacturers should also consider human oversight, governance and revalidation, cybersecurity and privacy exposure, potential liability, and the computing and energy infrastructure supporting AI. These costs may not sit neatly inside the original project budget, yet they remain part of the economics of deploying AI responsibly.

Let’s break down some of the key dependencies to consider.