7 questions to ask before choosing an AI materials intelligence platform
In a recent report, Deloitte found that 84% of manufacturers are already seeing measurable value from AI, yet only 20% of AI use cases have been scaled. The question has moved from “Should we invest in AI materials intelligence?” to “What should we be investing in?”
For AI in electronics materials intelligence, some of those markers are already here. Here are 7 questions to ask before selecting an AI materials intelligence platform.
Where does its data come from? Can critical information be traced back to authoritative sources?
Can you verify its answers? Does it show the evidence behind the output rather than simply generating a response?
How does it reason? Is there a defined, repeatable process between source data and conclusion?
Does it understand electronics? Components, BOMs, materials, suppliers and regulations create a very different data environment from general enterprise AI.
Can it handle change? Can it continuously account for changing regulations, supply conditions, component status and external events? How does it do so?
Does it augment expertise? Does it remove research and repetitive analysis while keeping experts in control of consequential decisions? Or does it
Is it truly AI-native? Look beyond the AI label. Can the platform autonomously research, validate, monitor, and analyze changing regulatory and materials information, or does it still depend on the same manual processes and static databases underneath?
