AWS Says Entry-Level Workers Need ‘Curiosity’ In the Age of AI

Entry-level jobs in tech are disappearing as artificial intelligence proves capable of performing fundamental tasks. At the same time, with AI adoption rates increasing, knowing how to use these tools has become a useful skill.To illuminate how entry-level workers can bridge this gap by becoming AI-literate or enhancing their AI-native skills, we spoke to Michelle Vaz, managing director of AWS training and certification at Amazon Web Services.

This interview was edited for length and clarity.

AI is reshaping entry-level roles

TechRepublic: There’s a broad understanding that entry-level jobs are disappearing, partially due to the capabilities of generative AI. Have you seen this within your own organization? How do you personally define the AI skills gap?

Michelle Vaz: When I think about what’s happening in the AI space, there’s one word that comes to mind: opportunity. And when I think about what AI is doing, especially for our entry-level workforce, it’s creating opportunities. It’s providing access. It’s providing an accelerated learning opportunity, and it’s providing a differentiated profile of what the entry worker looks like. We aren’t seeing a disappearance of entry-level roles, but what we are seeing is a reshaping of them. We’re making them more accessible to entry-level talent.

Think about some of the traditional barriers you or I may have had early on in our careers. We had to have a certain level of experience. If you’re a software engineer, you [have] to have certain hands-on keyboard coding experience. You [need] to know how to handle data manipulation. Those traditional barriers no longer exist, because AI is helping automate that.

In addition, I talked a little bit about acceleration, but AI is taking what were routine repetitive tasks, which entry- or early-career workers had to do, and it’s making their work more impactful.

New pathways through AI skills

How does having AI skills, such as already knowing how to ask a question of ChatGPT and how to prompt it, create new pathways for early-career professionals?

We put out a recent blog post around the entry-level workforce, and I’ve specifically talked about software development engineers, full-stack developers, DevOps engineers, and how those entry-level roles in the tech space have really now been augmented with AI.

Not only is the accessibility there and the removal of traditional barriers, but what I don’t think we talk enough about when it comes to pathways is the ability for these early-career professionals to have more impact early on in their career. When you look at generations to come, they really talk about mission, they talk about sustainability, they talk about wanting to make an impact. Those things really mattered to them, and that’s what keeps them in the workforce. And that’s what AI is unlocking.

We just sent our interns back to school. But when they were here in the workforce, they were building really cool things for us, I mean, they were spinning up apps on PartyRock that provided immediate value to our organization, with very little support needed. They are learning this as part of their education and they want to bring it back and make applications. This new AI-enabled workforce is carving their own path.

Alternative training pathways provide inroads to AI skills

Why are alternative training pathways like AWS certifications especially suited to folks who work with AI already?

What it’s doing first is acknowledging that there’s a shift. We know that the AI native workforce learns differently. We know that they expect to move faster. They want to make application[s] of what they’re doing, and they want modular ways of skilling. They don’t want to sit in a traditional course, long form. They liked having more modular, bite-sized learning and skilling that allows them to make application[s] right away.

In addition, whether you’re going for a generative AI foundation certification or if you’re doing one of our more advanced professional certifications, we’re finding that the AI-native workforce is building their credentials. And those credentials are transferable. We see that in terms of the certifications achieved. We see it in the consumption of our boot camps. We see it with some of the micro-credentials within our partners. They want to find alternative ways to build their skill set and practice capability.

And then the last one I’d offer is, in addition to you know, alternative pathways, it’s the business impact. They want to know that that pathway is closing the gap for them to be effective in their jobs.

Customer expectations and other forces drive shifts in skills

What market forces are doing the most today to encourage companies to hire AI-literate entry-level employees?

I think there are three areas that I’d focus on. The first one is customer expectations. AI is already shaping the products, services, and experiences we have today. And companies are embracing that they need employees who are more fluent in these genAI tools.

Number two is productivity pressures. Organizations, regardless of where you sit, are looking for opportunities to drive efficiency with generative AI. And entry-level workers who are AI literate can help accelerate that adoption [and find] those efficiencies. And I think companies alone in the market are creating that demand for an AI-literate workforce.

The third one is the pipeline itself. I mentioned this with the interns. You know, universities and training programs are producing AI-skilled graduates and they are looking to place them. And frankly, if companies aren’t hiring them, they’re going to be left behind. We know that AI literacy at the entry level is no longer a nice-to-have, but it’s a business necessity.

How can software engineers prepare for a future with AI?

In the blog post from July, you said software development engineers are experiencing perhaps the most dramatic transformation. How can this group, in particular, be as prepared as possible for the contemporary workforce?

In my own organization I have software development engineers, and I’m having a really great dialogue with my own workforce, within training and certification. The first thing, I think, is just acknowledging that there is a disruption. Engineers are indeed seeing their workflows, their tools, their responsibilities, and the outcomes they need to drive shift — and it’s shifting really fast.

Number two is really the mindset and the adaptability. It’s about having engineers who are continually learning and are able to leverage AI as a collaborator, but not fear that it’s replacing them, and who have that open and curious mindset.

The third one is being prepared. That’s what this conversation today is about: making sure our engineers feel like they have the upskilling and the tools they need to reinvent how they do work. I asked our engineering team, tell me, what are you doing to enable yourself? How are you upskilling yourself? There’s so much that we offer out there within SkillBuilder, and it’s for free. I love the folks that are curious and actionable and want to prepare themselves.

Engineers are not just coders. They’re actually architects, and they work at an AI-enabled level. They’re at the forefront of change because AI is no longer just a tool they have to build, it’s a tool that they are building with. For our engineers to be prepared, they need to think less about writing every line of code themselves and more about how do you orchestrate that? How do you validate that? How do you scale solutions along AI? And so, in this new world, things like system design, critical thinking, applying AI literacy, and the appetite to continually upscale through certifications, hands-on learning, and peer learning are so important.

Cybersecurity professional organization ISC2 defined these as the top five skills needed for entry-level workers.