EMUG Tech delivers seven industrial AI service areas: AI Strategy and Use-Case Consulting to identify and prioritise highest-return AI investments; Generative AI for Engineering productivity and knowledge management; AI for Manufacturing and Quality to reduce defects and optimise production; Predictive Analytics for asset reliability and supply chain performance; Computer Vision for automated inspection and shop floor monitoring; Engineering AI Assistants and Agents that augment technical decision-making; and RPA and Intelligent Automation to eliminate high-volume manual workflows. All programs follow the EMUG PRISM Framework.

EMUG PRISM is the five-phase AI and intelligent automation delivery methodology: Prioritize — AI readiness assessment and use-case scoring against ROI potential and data readiness; Roadmap — AI solution architecture design including SAP, PLM, and MES integration points; Implement — data pipeline development, model training, and production deployment with MLOps; Scale — expansion from pilot to multi-site or multi-function deployment; Monitor — continuous performance monitoring, drift detection, retraining, and AI governance compliance. PRISM-delivered AI programs reach production 3× faster than unstructured approaches and achieve sustained adoption rates above 85%.

Industrial AI refers to AI applications designed specifically for engineering and manufacturing environments — where models must operate on sensor data, CAD files, production records, and quality inspection images from factory environments with strict latency, safety, and reliability requirements. Industrial AI differs from general enterprise AI in three key ways: it must integrate with SAP, PLM, and MES systems rather than standalone analytics platforms; it operates in regulated environments requiring AS9100, IATF 16949, and EU AI Act compliance; and it requires engineering domain knowledge to design models that reflect actual manufacturing processes, not just data patterns.

A focused single-use-case AI program — for example, a computer vision defect detection model for one production line — typically reaches production deployment in 8 to 16 weeks using EMUG Tech’s pre-built AI accelerators and PRISM Framework. A multi-use-case AI program covering predictive maintenance, quality inspection, and document extraction typically takes 6 to 12 months to reach full production deployment across all use cases. The most critical factor is data availability: programs where structured historical data exists deploy faster than programs requiring new data collection infrastructure.

EMUG Tech designs all AI solutions to integrate directly with SAP and PLM systems rather than operating as isolated analytics tools. Specific integrations include: AI quality inspection results writing directly to SAP QM quality notifications; predictive maintenance alerts triggering SAP PM work orders; generative AI engineering assistants accessing Teamcenter or Windchill knowledge bases through RAG pipelines; and RPA programs automating data entry between PLM, SAP, and MES systems. SAP and PLM integration is designed into the AI solution architecture at the Roadmap phase, not retrofitted after deployment.

The minimum data infrastructure for industrial AI deployment depends on the use case: predictive maintenance AI requires 12 to 24 months of historical sensor data with known failure events; computer vision inspection AI requires 500 to 5,000 labelled images per defect class; generative AI knowledge assistants require structured document repositories in PLM or SharePoint with consistent metadata; and RPA programs require stable digital processes with consistent input formats. EMUG Tech’s PRISM Prioritize phase assesses data readiness for each candidate use case and identifies whether data collection or preparation work is required before model development can begin.

EMUG Tech embeds AI governance into every PRISM-delivered AI program: EU AI Act risk classification is performed at the Roadmap phase to identify high-risk AI systems (such as AI used in safety-critical manufacturing decisions); data governance frameworks define data lineage, quality standards, and access controls; model explainability is implemented for AI systems making consequential decisions; bias assessment covers training data composition and model output fairness; and AI governance compliance reporting is delivered through the MONITOR phase. For global manufacturing organizations, AI governance frameworks are designed to comply with EU AI Act requirements and equivalent regulations in other markets.

AI and intelligent automation programs are delivered across 20 countries: Germany, France, UK, Netherlands, Sweden, Italy, Spain, Poland, Czech Republic in Europe; India, Japan, South Korea, China, Malaysia, Thailand in Asia-Pacific; UAE and Saudi Arabia in the Middle East; USA, Canada, Mexico, Brazil in the Americas. Engineering and AI delivery centers in Hyderabad, Germany, and Dubai with on-site AI program delivery capability at client manufacturing facilities globally for both implementation and ongoing MLOps and model governance programs.