Digital Transformation

Your 2027 AI Plans Have a Competitor, and It Is Your ERP

SAP’s ECC 6.0 maintenance deadline is creating a major resource crunch just as manufacturers are investing in AI, with both initiatives competing for the same budgets and scarce internal expertise. Manufacturers should use the S/4HANA migration as an opportunity to clean up master data and document processes, creating the foundation needed for scalable AI rather than simply moving existing problems into a new system.

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A Model Went Dark for 18 Days. AI Sovereignty Is Now an Operations Problem

The temporary shutdown of Anthropic’s frontier AI models showed that geopolitical decisions can disrupt production-critical AI services overnight, making dependence on a single model provider a new operational risk. For manufacturers, the priority is to build AI architectures with backup models, clear fallback plans, and greater control over critical workloads rather than relying on one external provider.

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AI Won’t Fix Slow RFQs Until the Product Logic Is Under Control

For complex manufacturers, the biggest AI opportunity lies in the quote-to-delivery process, but only if product configuration rules, data, and system integrations are already governed and aligned. AI can accelerate quoting, yet without a reliable digital thread across CPQ, ERP, PLM, and production systems, it risks scaling errors rather than improving margins and operational performance.

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Siemens’ CTO on industrial AI: train doors matter more than brakes

Industrial AI delivers the most value in everyday, high-frequency processes, like predicting train door failures, where accuracy, reliability, and domain-specific data are critical. However, the real challenge isn’t the technology itself but organizational readiness, including data sharing, workflow integration, and preserving expert knowledge.

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Claude Mythos just satisfies. What it tells us about AI in industrial settings.

Anthropic’s Claude Mythos marks a major leap in AI, enabling advanced multi-document reasoning, long-context analysis, and self-correcting workflows that extend far beyond cybersecurity into complex industrial tasks. However, the real bottleneck is no longer model capability but organizational readiness—especially data quality, infrastructure, and governance needed to fully leverage these systems.

Claude Mythos just satisfies. What it tells us about AI in industrial settings. Read More »