Three Ways to Leverage Industry Expertise for an AI Career – A Guide for Non-Technical Leaders

When most professionals think about “AI consulting” they tend to think about technical machine learning services, like: Building our data infrastructure, crafting and testing new algorithms, interesting AI systems into existing IT infrastructure.

Last month we ran a podcast series on the AI in Industry podcast on the theme of “Advancing Your Career in the Era of AI”, with a focus on how non-technical professionals can become more valuable in the market, and can become involved in AI projects and initiatives, without ever learning to code.
I received twice as much feedback on this series as any other series we’ve ever run on the podcast - which surprised me.
It surprised me because I think about everything on Emerj.com as being useful for nontechnical professionals. We’ve built our editorial calendar and our products around the needs of nontechnical professionals who want to make the most of their careers, but this recent series spoke to that topic directly.
But hitting directly on the theme of “Advancing Your Career in the Era of AI” clearly hit a cord.
For that reason, I’ve decided to release a three-part video and article series on that same topic, breaking down the lessons that were most important for me - and sharing a bit of my own story going from small-town martial arts instructor to international AI speaker and strategist.
Before getting into the small-town martial arts instructor part, I’d like the share a pivotal Silicon Valley conversation that changed the course of my career:

In this article, I'll showcase 6 examples of nontechnical professionals who used their business and subject-matter expertise (not their coding ability) to have more exciting careers in AI, and I respond directly a number of questions and comments from Emerj subscribers about AI knowledge for career advancement.

In the classic business book Good to Great, author Jim Collins talks about the different approaches for technology adoption between high-performing and average companies. Collins' research indicated that high performers tend to adopt technology as an accelerant to an existing, working strategy - while underperformers tended to adopt technology in an attempt to jumpstart a change in direction or strategy that they haven't yet undertaken.

This article was a request from one of our Catalyst members. The Catalyst Advisory Program is a coaching program involving one-to-one advisory, weekly group Q-and-A with other Catalyst members, and a series of proprietary resources and frameworks based on insights from AI leaders at the world’s largest enterprises (AI adopters and buyers), and successful AI vendors and service providers. Learn more and apply emerj.com/catalyst.