Treating AI Like a Junior Employee: Lessons from Marc Kermish on the CAIO Connect Podcast with Host Sanjay Puri

AI works best when treated like a junior employee—trained, measured, and coached. Key lessons from Marc Kermish on the CAIO Connect Podcast.

WASHINGTON, DC, UNITED STATES, December 17, 2025 /EINPresswire.com/ -- Artificial intelligence is often marketed as a magic button—flip it on and productivity soars. But according to Marc Kermish, Chief Technology and AI Officer at Protolabs, that mindset is exactly why so many AI initiatives fail. In a recent conversation on the CAIO Connect Podcast, hosted by Sanjay Puri, Marc offered a grounded, practical perspective on what it really takes to make AI work inside modern enterprises.

His central idea is refreshingly simple: AI should be treated like a junior employee, not a finished product.

AI Isn’t Plug-and-Play—It’s Train-and-Grow
One of the biggest mistakes organizations make, Marc explains, is expecting AI to “just work.” In reality, AI needs onboarding, training, feedback, and patience—just like a new hire. Early outputs may disappoint. That doesn’t mean AI has failed; it means it’s still learning.

This framing is especially relevant in digital manufacturing, where Protolabs operates at the intersection of software and hardware. AI helps take customers from a CAD design straight to production, analyzing manufacturability, tolerances, materials, and compliance requirements in real time. The value is enormous—but only when AI is trained with the right context and expectations.

Where AI Is Creating Immediate Impact
At Protolabs, AI is already delivering value across the manufacturing lifecycle. It analyzes CAD files to ensure parts meet ISO, medical, aerospace, or defense standards before production even begins. It helps engineers choose from an overwhelming range of materials by surfacing recommendations at the moment of design. On the factory floor, AI processes machine and IoT data to predict maintenance needs, keeping operations running at full capacity.

Yet even with these wins, Marc is candid about resistance—especially from engineers. If AI’s first answer isn’t as good as theirs, skepticism follows. Overcoming this isn’t a technology problem; it’s a change management and expectation-setting challenge.

Agentic AI: The Rise of AI Co-Workers
One of the most forward-looking parts of the conversation focused on agentic AI—systems that don’t just respond, but act. At Protolabs, agentic AI is already supporting marketing, finance, sales, and technical teams. From content creation to automating invoice processing to helping sales teams validate compliance requirements, AI agents are stepping into assistant-like roles.

Marc predicts a future where AI agents actually appear on org charts, complete with identity and access management. In his words, leaders won’t just manage people—they’ll manage teams of humans and AI agents working together.

Why Most AI Experiments Fail
Marc likens today’s AI landscape to a science fair where most projects don’t make it past early trials. The reasons are consistent: mismatched use cases, unclear KPIs, poor architectural choices, lack of resilience, and no defined budget. Successful AI initiatives, by contrast, are tightly scoped, measurable, and allowed to evolve through iteration.

Adoption, ROI, and Leadership Reality
Even widely adopted tools like Microsoft Copilot often see usage drop after 30 days. The culprit? Lack of training. Marc emphasizes that prompting is a skill everyone must learn, not a niche role. Communities of practice, shared prompt libraries, and AI champions inside each function make the difference between novelty and sustained impact.

Measuring ROI doesn’t have to be complex. At previous roles, Marc tracked hours saved through simple manager conversations and lightweight tools—directionally accurate, but powerful enough to justify investment.

The Bottom Line
AI isn’t a silver bullet. It’s a junior teammate with massive potential. Organizations that invest in training, patience, and experimentation will unlock real value. Those waiting for perfection will be left behind.
As Marc advises aspiring Chief AI Officers: stay curious, experiment daily, and amplify the conversation. The future of work is already here—and it’s learning fast.

Upasana Das
Knowledge Networks
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