Building AI Fluency Across a 96-Year-Old Manufacturer

Continuous Improvement • Workforce Development • Technology Adoption

Challenge

As generative AI began drawing attention across manufacturing, we faced a question familiar to many industrial companies: how to evaluate a rapidly evolving technology without getting distracted by hype or left behind by inaction.

There was no crisis driving urgency. We had navigated COVID and government funding disruptions that took down competitors. The business was strong. But leadership recognized that waiting for AI to become a problem was not a strategy.

The specific challenges were:

Separating practical AI capability from market hype
Identifying where AI could create real value across sales, finance, operations, and quality
Building working fluency across a cross-functional team – not just among early adopters

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Solution

We engaged DVIRC for a two-session, hands-on generative AI training program led by Dan Hughes, Co-Founder of ClariteeAI. Ten team members participated from across the organization: sales, finance, operations, quality, and senior leadership.

The sessions were built around our actual workflows and data – not generic AI overviews. Participants worked through prompt engineering, content generation, and model evaluation. The program also helped the team draw a clear distinction between true AI use cases and what was really data management work, then applied a structured framework to identify and risk-assess specific opportunities inside Ehmke’s operations.

Training components included:

Hands-on prompt engineering using real Ehmke scenarios
Comparative model evaluation across AI tools
Structured methodology to identify and prioritize use cases
Risk assessment process for sequencing implementation

Results

By the end of the program, we had a working inventory of 22 prioritized AI use cases. The continuous improvement team is now working through risk assessments and selecting which to pursue first.

Use cases identified include:

Inventory management and vendor performance tracking
Shop floor scheduling optimization
Faster, more precise customer proposal and presentation development
A longer-term initiative to consolidate vendor, inventory, and capacity data into a single AI-accessible database

Team members across all experience levels came away more comfortable using AI tools in their daily work – refining prompts, understanding model differences, and treating AI as a two-way tool rather than a simple search engine.

References

https://www.microwavejournal.com/articles/36761-tpy-4-radar-setting-new-standard-in-airspace-threat-detection

Team Lead: Daniel Nguyen, Director of Engineering – Ehmke Manufacturing Company, Inc.

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