AI might be the biggest buzzword in business right now, but for manufacturers, the question isn’t if AI will change things, it’s how, where, and when. AI is beginning to reshape key functions in the supply chain and at this point, manufacturers should think less about adopting AI generally and more about using it where it has the most impact.
AI Isn’t Magic. It’s Math.
SnapChip helps manufacturers solve a specific problem: sourcing electronic components faster and more effectively. By analyzing the historical data buried in decades of purchasing decisions, SnapChip can automate RFQs, reveal alternative sourcing options, and improve purchasing reliability – especially in high-mix, low-volume environments where demand shifts constantly and traditional procurement strategies fall short.
This grounding in reality is a key theme of concerns that are shaping its place in manufacturing. AI is at its least effective when it’s guessing. It really shines when it’s tasked to analyze. And when it’s trained on clean, contextual data, it becomes a strategic advantage.
The Right Tool for the Right Problem
For manufacturers, that means resisting the urge to “AI-ify” everything. Some problems don’t need intelligence; they need better process, better communication, or better structure. Others, like parts procurement, quality inspection, or predictive maintenance, might be perfect candidates for AI acceleration because they involve pattern recognition, structured data, and repeatable workflows.
Amtech and SnapChip have collaborated to identify these right-fit use cases together. In their work with shared customers, SnapChip’s AI supports Amtech’s existing software to speed up processes that were previously manual, error-prone, or opaque.
When Collaboration Makes AI Work
Generative AI in isolation rarely succeeds – it needs context from operators, alignment with systems, and feedback from the people who actually use the tools. This is one reason iteration is a critical part of adoption. As AI tools surface new insights or generate novel options, users learn to trust the output, especially when they can verify it against known behavior. That trust, built over time, makes it easier to take the next step and apply AI to new decisions or workflows.
This kind of collaboration matters between people and AI and between vendors and manufacturers. As Jay says, “We want to surround the customer with capability.” That means blending smart software with deep manufacturing knowledge so tools perform.
Beyond the Hype: What AI Is Actually Good At
The takeaway is that AI isn’t a strategy, though its adoption can and should be strategic. AI is a capability that’s especially effective when applied to:
- High-volume data analysis (like sourcing history or quality data)
- Pattern recognition in complex systems
- Automation of tedious or time-consuming tasks
- Augmenting human decision-making (not replacing it)
Everett’s story with SnapChip is a real-world case study in how targeted AI can bring measurable improvements to long-standing challenges. Upgrading business with AI instead of reinventing it.
And for manufacturers looking to stay competitive, those upgrades are starting to look less like science fiction and more like the next phase of operational excellence.