Eric Lealos - Test Bench to Tech Stack: Applying AI in Manufacturing
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In this episode of *Conversations on Applied AI*, host Justin Grammans interviews Eric Lelos, a seasoned data and analytics leader with over 20 years of experience across manufacturing, healthcare, and financial services. Lelos shares his career journey—from engineering student to data engineer to AI practitioner—highlighting how the evolution of technology, from early internet days to today’s generative AI, has reshaped his work. He emphasizes a pivotal shift in mindset: moving from designing tests to pass or fail products to capturing rich data to understand why products fail. This insight forms the foundation of his work with TestBench, an AI-driven system that helps manufacturing operators diagnose and fix product failures in real time, using natural language processing and deterministic workflows. Lelos argues that generative AI is not replacing humans but amplifying their impact, especially when combined with strong data engineering. He warns against superficial AI adoption, stressing that many organizations are stuck in pilot purgatory due to cultural inertia, lack of skilled teams, and overhyped promises. He advocates for embedding AI into operational workflows, particularly in manufacturing, where small improvements in yield can yield massive cost savings. The episode closes with a call to action: focus on the industry you care about first, then apply AI as a tool to drive transformation. Key takeaways include: 1) Redesign testing systems not just to pass/fail but to capture diagnostic data; 2) Use AI to turn tribal knowledge into scalable, repeatable processes; 3) Generative AI is a powerful assistant, not a replacement, especially for complex engineering tasks; 4) Cultural resistance and lack of skilled teams are bigger barriers than technology; 5) Manufacturing is ripe for AI innovation, with even 5% yield improvements translating to millions in savings. The overall sentiment is highly positive, reflecting optimism about AI’s transformative potential when applied thoughtfully.
Redesign manufacturing tests to capture diagnostic data, not just pass/fail outcomes.
Use AI to convert tribal knowledge into scalable, repeatable fix recommendations.
Generative AI acts as a powerful assistant, not a replacement, for engineers and data professionals.
Cultural inertia and lack of skilled teams are bigger barriers to AI adoption than technology.
Even small yield improvements in manufacturing (e.g., 5%) can result in millions in annual savings.
Reimagining Testing: From Pass/Fail to Diagnostic Intelligence
“If you would actually change your perspective about it just slightly and say, I want to capture a bunch of data and find out why my products are failing. You would design the test differently and you would design the flow differently.”
From Data Engineering to AI: A Career in the Tech Evolution
Lelos traces his career from computer science student to data engineer, highlighting key moments like working with early data analytics platforms (Information Advantage, Cognos) and the transformative impact of modern tools like Snowflake, Databricks, and DBT.
The AI Revolution in Manufacturing: TestBench in Action
“We now make good recommendations to the operator. So now I can essentially like have all of these different operators floating around all these different benches and they don't have to know as much about products.”
The Cultural and Operational Barriers to AI Adoption
“I think increasingly there are fewer and fewer of those people who are really like digging into these data challenges for whatever reason. I think we're just in a phase right now of the world where a lot of people either don't know or they're not empowered to have an impact.”
The Future of AI: AI-Assisted Engineering and Industry-Specific Innovation
Lelos concludes by urging listeners to focus on industries they care about—like manufacturing, healthcare, or agriculture—and apply AI as a tool to solve real problems. He sees generative AI as a game-changer for knowledge workers, accelerating learning and execution.
“If you would actually change your perspective about it just slightly and say, I want to capture a bunch of data and find out why my products are failing. You would design the test differently and you would design the flow differently.”
“It's not going to actually replace doctors. So don't stay away from being going to med school because of this. In fact, there's a very good chance that this is going to increase the demand for doctors.”
“We now make good recommendations to the operator. So now I can essentially like have all of these different operators floating around all these different benches and they don't have to know as much about products.”
Host
Guest
Eric Lelos
person
Justin Grammans
person
TestBench
product
Minnesota
place
Applied AI
organization
ChatGPT
product
Med Device
other
Quantified Mechanics
organization
LabVIEW Test Stand
product
DBT
product
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