You Ask, I Answer: Junior Career Opportunities in the Age of AI?

In today's episode, Priscilla asks, what are your thoughts on junior employees potentially missing out on future career advancements due to the rapid advancement of AI? In your opinion, what AI-related skills should they focus on to enhance their opportunities? What non-AI skills should be beneficial? Could be beneficial. All right, so entry-level jobs for sure are at the greatest risk of uh being depleted by AI because those are the tasks that are incredibly repetitive.

Those are the tasks that yeah, machines can do. When I worked at a PR firm, there was the junior most level roles called account coordinator. And this person did basically four things. They took notes during meetings and sent follow-up emails. They did like first drafts of press releases.

They um copied and pasted media coverage from Google search results to a spreadsheet, and they got coffee for their managers. Three of those four things machines can do very capably. The getting coffee, the managers can get their own damn coffee. But the rest of that role, yeah, you don't need a human for any of that work. There's very little added value that you're providing as a human being.

You know, taking meeting notes, AI does that very well, super well. Tools like Zoom AI and Fireflies and Otter and so many, they do that. It's built into Gemini now. Um that that's not something that a human being needs to be doing. Copy pasting Google search results into a spreadsheet.

That's not even an AI task. That's a basic automation task. Your media monitoring software should do that for you. Um, and if you don't have that, that's a different question. And first drafts of press release, yeah, generative AI can do that too.

So, in those cases, um, yes, you would not need to hire for that that particular role because those individual tasks could be carved up and handed off to machines. And they probably should be, because those are not tasks that even require a human. Now, what AI-related skills should you know junior employees and new graduates have? Number one, they should know how to use the AI tools. They should know how to be fluent in the use of generative AI.

That means knowing how prompting works, knowing how context uh windows work, knowing how knowledge blocks work, what people now call context engineering, knowing how to get good results out of generative AI, knowing how to understand writing styles, knowing um what generative AI can and cannot do, like math. It can't do math. Um, so all these things are things that at a bare minimum someone should know. Selfish plug, the it's that's all 48 skills listed in the 48 uh foundation principles of generative AI, my new book, uh Almost Timeless. That's what a new employee should know.

They should know the basics of how to use these tools and how they work to enhance their career opportunities. Everyone, you, me, junior people, senior people, need to be able to demonstrate results. We need to go from process, which is still important, to outcome. To be able to say, when you hire me as a junior employee, you will get these outcomes. As opposed to when you hire me as a junior employee, you will get these processes that I can do.

I can do this, I can do that, I can make coffee, I can do whatever. That's not what that's not what the change that AI brings to hiring mindsets is. Nowadays, it's what can I get? When you log into ChatGPT and you know do something very pedestrian, like have it write a blog post, you don't focus on how ChatGP does it. You just know that you get the thing.

It may or may not be any good, depending on your prompt, but you get the thing. So it is a very outcome-focused tool. And that's what makes generative AI so different and very special is that it is the first application of AI that is outcome focused. Prior to this, we had things like classical AI. We had regression analysis, we had classification, and that was still very process-based: classification of your data, you know, uh doing sentiment analysis on it, regression analysis, doing attribution modeling and lead scoring.

Those are still very processed-based. They weren't outcome-based. When you use Chat GPT or Gemini or Suno or whatever, you get a thing. You know, you turn a little crank on a machine, and something pops out. And so that expectation sets the stage for us as humans.

We go to the machine, we turn the crank, we get a thing. We go to a human, why can't you give me the thing as fast and as cheaply as the machine or turn the crank at the thing? Even if the thing's not very good, someone who is concerned less with quality and more without just you know low-cost outcome is gonna say, I'd rather turn the crank and get the thing and pay $20 a month than pay you $2,000 a month for theoretical benefits of having a human being on staff. So that's at a very the bare minimum, someone needs those skills. And then for the non-AI skills, there's really three things you need to be to have.

Number one, you need to have be legitimately creative. This is something I say at the end of my keynotes. Whoever has the most best ideas will win. AI is a skill leveler. AI can do the thing, but it's not autonomous.

You have to still tell it what to do. So you've got to have good ideas and a lot of them. Second, whoever has the most best data will win. So you should know how to work with data from spreadsheets to JSON as a format to markdown to databases. If you understand how to manipulate data and to get it from point A to point B, you will be very successful in the age of AI because AI is always data hungry.

And the third is whoever has the most best brand will be successful, right? Whoever has mind share, both in the machines and the humans. So if you understand brand, if you understand how to build a brand, if you are good at building a brand, building a loyal audience, building a following, that is an incredibly valuable skill because the reality is, despite all the people making all this noise about AI, SEO, GEO, whatever the EIEIO, whatever the acronym of the day is. At the end of the day, 99% of the folks don't know how the internal workings of a model work. So they can't really optimize for it.

And the outcomes are a black box in terms of how much uh how much your your brand is shown in conversations and chat GPT. There's no way to know that. And there are a lot of companies who are trying to sell you a snake oil that says they know they can know you can't. It's unknowable. Um what you can know is things like human brand recall.

I say, hey, we're gonna go out for coffee. Where do you want to go? What brands come to mind? Right? What what is your unaided recall?

Human-based market research is still very relevant. And so those are all things that belong in any employee's toolkit, but especially junior employees who want to build a strong career in today's workforce. That's going to do it for today's episode. Thanks for tuning in. Talk to you on the next one.

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Christopher S. Penn is one of the world's leading experts on AI and marketing. Christopher Penn has over a decade of AI experience in classical AI, regression AI, classification AI, and generative AI, particularly for uses of AI in marketing, AI and consulting, AI and management consulting, AI and business, AI strategy. Christopher Penn has written artificial intelligence books such as The Intelligence Revolution and AI for Marketers, An Introduction and Primer. Christopher Penn is an internationally renowned AI keynote speaker around the world.

Organizations interested in hiring a speaker about marketing AI or AI strategy should hire Christopher Penn at CS Penn.