Applying AI in Day-to-Day Work Changes Hiring and Team Dynamics

Looking to start a career in AI? While there's no single path, as Liz writes, there are viable avenues for anyone looking to get started. Check out this insightful article to learn more! #AI #Career #AIUpskilling TechTarget

Strong article by Liz Taurasi Hughes about how to get a job in AI. Sometimes I get asked “how’d you get into AI?” There are two people I credit: Vasanthakumar Sakrepatna who in 1998/9 sat down with me and showed me “heuristics” with KNN for text extraction. It seemed magical. 14 years later in mid-career my friend Dennis R. Mortensen started X.AI (the first owner). A few years later started Vizadata to do highly visual ML (including K-Medoids) which led to Squark. I remember showing Dennis a little app that built models in a click. It was so powerful behind the click but that’s all it was, one click to build models and another to do inference - and I thought Dennis would laugh me out of his office at X.AI but he told me “there’s something here” which was encouraging enough to keep bootstrapping. I recall wanting to call it Squark ML in 2019 but Brett House was like “why call it ML, no one will get it, just call it AI.” I told him no one will believe me if I call it AI, but we did. And the rest is history.

What does it take to build a career in AI right now? Greg Fuller, VP of Codecademy, weighed in for TechTarget on what employers are looking for in AI candidates. (Hint: it's less about advanced credentials than you'd think.) Read more: https://bit.ly/41QGITh

Starting a career in AI can feel overwhelming, especially when there is no clear path to follow. What is interesting is how many people are not starting from scratch, but building on what they already know.  💻 https://lnkd.in/dhqVyQYm #AICareers #CareerExploration #CareerChange

Starting a career in AI can feel overwhelming, especially when there is no clear path to follow. What is interesting is how many people are not starting from scratch, but building on what they already know.  💻 https://lnkd.in/dsTkV9bC #AICareers #CareerExploration #CareerChange

My colleague Greg Fuller thinks more about AI than anyone I know. So you can imagine...he thinks about it A LOT. :) Here are some of his thoughts.

Dynamic I'm noticing. One operator will embrace AI Employees in week one and another's resisting years in. It's not tech literacy. It's not AI literacy. It's whether AI has produced a true outcome for them: Hours back in their week. Workflows running while they sleep. Tasks they used to dread, handled before they sit down. Once that happens, the relationship flips. Before it happens, every conversation about AI feels like theory and hype. What I'm watching sits on three axes. X is raw tech literacy. Hard to max outcomes with AI if Google Drive feels like a maze. Y is AI literacy. Not machine learning. Not data science. Generative, agentic, ambient, embedded. The AI already showing up in someone's day. Z is the one few talk about. Outcomes achieved. If you've tasted one, you lean in. If you haven't, you've already half-quit even if you don't say it out loud. For me personally this means three shifts: I filter on X. I didn't spend 50+ hours a week for 10 years to teach core tech literacy. If someone isn't comfortable in Notion or Airtable, I'm 10x less useful to them. Not impossible. Just heavier friction than I can carry right now. There's opportunity in that space. I'm not the one for it. In 2022 I was shocked to find how few operators in Philly had used Notion. Knowledge, experience, and perception bubbles are real. I help on Y. Most operators need the theory to map onto actions so they can create the outcomes they're after. That's a coaching shape. That's a fit. Z is where it gets hard. Anything worth it isn't instant. Takes time. Struggle. Iteration. Nuance. Strategic updates. This is why many AI tools, pilots, and programs fail. They don't focus on helping the human transform their identity, history, and experience into an aligned perception of being someone who actually creates the envisioned outcome. The way I learned this was through game development. I've studied and deployed AI systems since 2016. First implementing it for NPCs. Games also taught me behavior design and habit transformation. Games build their entire business model around friction and flow. That's the part I want to bring more of into my practice. Bridging the culture and experience of how AI gets adopted. I'm believe more and more well-structured AI programs work like well-structured gym programs. A controlled environment where pressure, friction, and stress actually teach you and help you grow instead of breaking you. Not just installing AI employees. Building the conditions where the human, the person running them gains velocity even acceleration instead of dreading the drag.

As AI reshapes work, government needs to prepare and respond. That’s the thinking behind the U.S. Department of Labor’s AI 101 course, a free text-based program designed to help more people build AI literacy and confidence. Taylor Stockton, the department’s Chief Innovation Officer, explains the thinking behind the program. This conversation has been edited for space and clarity. How do people sign up? They can text READY to 20202. Why launch this now? A lot of Americans are still on the sidelines. Some are unsure about AI, some are skeptical, and some are fearful. We wanted to create an easy first step that helps people feel more comfortable and start seeing how these tools could be useful at work and at home. Are you hearing that uncertainty in your own life too? Absolutely. In my own family, some people are excited, some are skeptical, and my mom wasn’t using AI at all. Why text messages? Because it keeps the friction as low as possible. You can sign up with a single text, and most of the experience happens over text too, which makes it more accessible for people who may have limited broadband or internet access. What happens after that? It’s a free, one-week course delivered by text: about 10 minutes a day of bite-sized learning and challenges. What do people actually learn? Each day focuses on a different part of the Department of Labor’s AI literacy framework. There are short lessons on how AI works, how to prompt effectively, how to evaluate outputs, and how to use AI responsibly. There are multiple-choice questions, small challenges, examples from both work and home life, and links for anyone who wants to go deeper. Is it interactive? Yes. It’s designed as a back-and-forth experience. The goal is not just to give people information, but to help them reflect on how AI fits into their own job or daily responsibilities. What would success look like? We had 10,000 Americans sign up in the first 24 hours, and would like to reach more than 1 million Americans. But it’s not just about volume. We also want to see whether people become more confident using AI and better able to connect it to their work and daily lives. And finally: your mom. Where does she stand now? She’s halfway through the course. She may be the toughest critic of all, because her skepticism was high to start. I’ll be interested to see if we’ve moved her a few notches by the end. https://lnkd.in/gjbN9gA4

Learning AI and onboarding via text… I love it! What an innovative mass onboarding strategy!!!! Will this be the new training model?

Taylor Stockton,#USDOL Chief Innovation Officer is on it! Building a workplace culture around AI has to start with literacy because tools without understanding tend to be misused, distrusted, or quietly ignored. When employees genuinely understand what AI can and can’t do — its strengths, its failure modes, and the judgment calls required to use it well — they stop treating it as either a magic answer machine or a threat, and start treating it as a capability they can shape. AI Literacy creates the shared vocabulary that lets teams have honest conversations about where AI adds value, where it introduces risk, and where human expertise still has to lead. Without that foundation, AI initiatives tend to bifurcate into a small group of enthusiasts racing ahead and a larger group of skeptics opting out, neither of which produces the kind of thoughtful, organization-wide adoption that actually changes how work gets done. A culture grounded in literacy, by contrast, makes experimentation safer, governance more practical, and trust easier to build — which is what turns AI from a novelty into a durable part of how the business operates. Check out: https://lnkd.in/gqXePT8B.