Challenge
Considering Ametek handles highly sensitive CUI, ITAR, and FDA-regulated data across a decentralized enterprise, how confident are you that your R&D and engineering teams aren't already inputting proprietary specs into public LLMs like ChatGPT to speed up their work—and what is your verifiable mechanism today to ensure 'zero data training' outside your firewall while still providing the generative AI tools the business is demanding?
What If...
What If... your engineers, service technicians, and compliance officers could ask a complex question in plain language and receive an instant, trusted answer sourced simultaneously from live data in SAP, customer records in Salesforce, and engineering drawings in Siemens PLM, without ever switching screens?"
Given Ametek’s acquisitive nature, if we look below the surface of our active ERP landscape, what is the cumulative, fully loaded annual cost of maintaining the 'zombie systems' inherited from past deals and perhaps more critically, how many unpatched security vulnerabilities exist right now within those forgotten legacy repositories that we can no longer effectively monitor?
We know data silos exist between our Engineering V-Model (Siemens/Dassault PLM) and Operations (SAP ERP); can you currently quantify the 'Cost of Poor Quality' (CoPQ) resulting specifically from manual data re-entry errors, version conflicts, and lost context as complex product data tries to cross that chasm during New Product Introduction (NPI) cycles?
What If... you could transform regulatory compliance from a reactive, panicked audit response into a proactive, invisible shield—where every piece of unstructured data (like emails or CAD files) is automatically classified for CUI/ITAR or FDA relevance the moment it’s created?
What If... you could accelerate the 'time to synergy' of a new acquisition by 30-50%, rapidly ingesting their valuable historical data into a secure archive and immediately decommissioning their risky, costly legacy servers on Day One?
