Should We Invest ₹1 Crore in AI for Predictive Maintenance?
A CNC machining company evaluates a predictive maintenance proposal and discovers that a pilot-first approach significantly reduces capital exposure.
Structured perspectives on ROI, governance and AI integration for manufacturing SMEs.
The following case studies illustrate common situations faced by manufacturing and process-driven SMEs evaluating artificial intelligence investments. These examples demonstrate how readiness assessment, ROI modeling, governance design, vendor evaluation, and risk architecture can improve decision quality before capital is committed.
A CNC machining company evaluates a predictive maintenance proposal and discovers that a pilot-first approach significantly reduces capital exposure.
A textile exporter compares competing AI proposals and identifies hidden costs, vendor lock-in risks, and unrealistic ROI assumptions.
A factory struggling with quality and planning challenges discovers that process stabilization may create more value than immediate AI adoption.
A Coimbatore pump manufacturer evaluates forecasting AI and uncovers significant data readiness challenges before investment.
A manufacturing SME faces pressure to adopt AI because competitors are doing so and learns why competitive pressure alone is not a sufficient business case.
An AI proposal appears attractive until integration costs, governance requirements, training expenses, and exit risks are modeled.