We know that AI is everywhere now, but where does it actually matter in manufacturingcost analysis?
In a recent episode of the Altium Academy podcast, our CEO Sasan Hashemi outlined two practical, transformative ways AI is reshaping how manufacturers interact with product cost data. For cost engineers facing increasingly complex decisions about materials, suppliers, and carbon footprints, these applications deliver measurable value.
Sasan identified two distinct areas where AI delivers measurable impact: solving the persistent challenge of data categorization and fundamentally changing how teams interact with cost information. Both represent significant shifts in how cost engineering teams can work more efficiently.
The first challenge when discussing AI in any industry is establishing what we actually mean by the term. As Sasan explained:
This distinction matters. For cost engineering specifically, both approaches play distinct roles. Traditional machine learning excels at pattern recognition and predictive modeling. LLMs bring something different: the ability to understand context, categorize unstructured data, and interact naturally with users.
One of the persistent headaches in manufacturing is data comparability. When you're analyzing thousands of components across multiple suppliers, products, and technologies, how do you find meaningful similarities?
Sasan identified this as a core problem where AI delivers immediate value:
Finding comparable components goes far beyond simple keyword matching. A PCBA component might be described differently across various suppliers, CAD tools, and internal systems. Traditional search requires exact matches or predefined categories, which breaks down when dealing with diverse data sources and naming conventions.
AI-powered categorization can understand functional similarities even when the terminology differs. The technology recognizes that two components serve the same purpose despite having different part numbers, coming from different suppliers, or being described in completely different ways across your systems.
At Tset, this means cost engineers can quickly identify comparable components when evaluating alternatives, conducting should-cost analyses, or benchmarking supplier quotes. The system organizes components based on what they actually do rather than just alphabetically or by part number.
The second use case Sasan described might be even more revolutionary for how teams work with costing data daily. Traditional software operates on a predetermined principle: developers build specific views and dashboards, and users adapt their workflows to fit those fixed interfaces.
AI fundamentally changes this dynamic. Instead of building every possible view a user might want, the software can generate custom insights on demand based on natural language queries.
Sasan illustrated this with a practical example:
This approach acknowledges that different stakeholders need different things from the same dataset. Rather than building separate dashboards for every role, AI can tailor the experience to each user's actual questions.
New team members can ask questions in their own words. Cross-functional collaboration becomes easier when each department can query shared data in ways that make sense for their specific needs. The barrier between having data and actually using it effectively gets much lower.
This conversational approach to data interaction fundamentally changes how teams can scale their cost analysis capabilities without requiring everyone to become software experts.
Sasan predicts this shift will define the next decade of software in manufacturing: "I think this is major and that will very much dominate the SaaS vertical for the next 10 years."
What stands out in this discussion is the pragmatic perspective. Sasan positions AI as a tool that makes existing cost engineering work faster, more accessible, and more accurate.
The technology addresses real pain points: data categorization that currently takes hours, cross-functional communication that requires multiple handoffs, and the challenge of making complex cost models understandable to diverse stakeholders.
Watch the full post episode from Altium Academy: