Can AI Decide Who You Should Call?

Summary

In this live-recorded episode of The Blueprint Tour from the IIBEC Metro New York Chapter’s Building Smarter with AI conference, hosts Kenneth Shultz (Engineering Director at PermitZIP) and Carter Huddleston (Electrical Principal Engineer at PermitZIP) sit down with Nicholas Ceme, Business Development Engineer at Peikko North America.

With a background in structural engineering and a transition into technical sales, Nicholas shares how he’s leveraging AI to solve a problem every professional faces: too many leads, not enough time. He breaks down how he’s building Python-based tools that analyze large volumes of contacts, prioritize high-value opportunities, and automate follow-up workflows.

The conversation explores the real-world applications of AI in engineering and business development, from lead intelligence and data synthesis to prompt engineering and workflow automation. The trio also dives into deeper topics, including how AI is reshaping hiring dynamics for junior engineers, the rise of engineers building their own AI systems, and the growing importance of privacy, local AI models, and data control.

Candid, technical, and forward-looking, this episode offers a practical look at how AI is moving beyond theory, into the daily decision-making processes that drive modern engineering businesses.

“What about the 15 or 16 leads I don’t get the chance to follow up with?”

Keywords

Artificial intelligence, AI in engineering, lead generation, sales automation, business development, workflow automation, prompt engineering, Python automation, structural engineering, Peikko North America, Nicholas Ceme, PermitZIP, IIBEC conference, AI tools, data analytics, engineering workflows, local AI models, future of work

“AI is very good at taking massive quantities of data and telling you what matters.”

Takeaways

• AI can analyze and prioritize leads, helping professionals focus on the highest-value opportunities.

• Engineers are increasingly building their own AI tools using Python and automation frameworks.

• AI is more effective in workflow optimization and communication than in full design replacement, at least for now.

• Hiring dynamics are shifting, with AI increasing the productivity of senior engineers and reducing reliance on junior roles.

• Prompt engineering and structured context significantly improve AI output quality.

• Privacy concerns are real, local AI models and data anonymization are becoming critical considerations.

• AI is evolving from a productivity tool into a decision-making layer across engineering and business development.

“My program is all in Python, it pulls in models and analyzes everything.”

Chapters

1. Welcome and Conference Introduction

2. Meet Nicholas Ceme: From Structural Design to Business Development

3. The Real Problem: Too Many Leads, Not Enough Time

4. Using AI for Lead Analysis and Prioritization

5. Building AI Tools with Python and Automation Frameworks

6. Prompt Engineering and Workflow Optimization

7. AI vs Engineering Design: Current Limitations

8. Hiring Shifts: AI’s Impact on Junior Engineers

9. Privacy, Data Ownership, and Local AI Models

10. Final Thoughts on the Future of AI in Engineering

“We’re actually seeing a decrease in hiring of juniors.”

Where to Find Nicholas Ceme, PE

LinkedIn · Peikko North America

Where to Find The Blueprint Tour

YouTube · TikTok · TheBPTour.com
Kenneth Shultz (Host) · Carter Huddleston (Host)