• AI has become a hiring buzzword, and four employees explained how they added it to their job titles.
• An AI engineer said showcasing side projects was pivotal to transitioning from software engineering.
• Two non-technical Microsoft employees said having a humanities background was a strength.

AI is the buzziest word on the job market — and many workers want to know how to pivot into it.

As many technical workers upskill to stay on the cutting edge, others are moving into AI-focused roles from entirely different industries.

Many companies are pouring an eye-watering amount of money into AI, cutting some roles while adding new positions tied to the technology.

Against that backdrop, moving into AI could be a way for workers to future-proof their careers as the employment market reshuffles. AI engineers, consultants, strategists, and researchers rank among the top five fastest-growing roles in the US, according to LinkedIn's Jobs On the Rise 2026 report.

There's no single path into AI, and Business Insider spoke with four workers who took very different ones. Read on to learn how they pivoted their careers.

Natasha Crampton, Microsoft chief responsible AI officer

Natasha Crampton got her start as an attorney and is now Microsoft's first chief responsible AI officer.

Her job includes working side-by-side with engineering, sales, and research teams to ensure they uphold principles as they build AI systems. It also includes external work, such as helping establish new laws and standards in the space,

Crampton studied information systems in addition to law, and said she always had an interest in the intersection of technology, law, and society. During the strictly legal phase of her career, she said she always worked on technological issues, such as helping Microsoft draft contracts.

She said people looking to move into tech from other fields should start by using the technology themselves. She added that many technical skills are learnable, so coming from a different background shouldn't limit someone's ability to help shape it. She said, "a huge amount of the value" lies at the intersection of technical knowledge and insights from the social sciences.

Georgian Tutuianu, Hubspot AI engineer

Georgian Tutuianu has had several transitions in engineering, from structural to traditional to software to AI at HubSpot.

Tutuianu said that his ability to get technically in the weeds was an asset during the interview process, and showed he had experience with AI.

He also highlighted that his résumé has a section dedicated to personal projects. Tutuianu said he included one AI project, but it was enough. He said it came up naturally in the interview because he was asked about a time he used or built an AI agent.

"It was a juicy project where I could talk about it, and that was good enough," Tutuianu said.

Tutuianu said he also had to do a take-home coding assignment and review it with the hiring manager afterward, but there was no algorithmic component to the interview.

"Instead of the typical software engineering way that these interviews go, which is 'go solve this algorithm in front of me,'" Tutuianu said. "It's more of 'can you build the things that we care about? Show me."

Jai Raj Choudhary, StackAI engineer

Jai Raj Choudhary transitioned from a data-focused role to an AI engineer at AI agent startup StackAI.

The 24-year-old said he got his job by reaching out to StackAI's cofounder multiple times on LinkedIn. Choudhary said he had used the company's platform as a student, so he messaged the cofounder and started posting about StackAI, offering advice to the company.

He said he thinks he got offers from StackAI was because he understood data quality, the edge cases for the clients, the matrix, and the failure modes of the AI model or any LLM systems that were being used.

He said moving to San Francisco, where 9-9-6 culture existed, helped open his opportunities in the space.

"It's not like a 9-to-5 cushy job," Choudhary said. "We work 9-to-9, six days a week. You wake up, you think about the problem that a client had, and you sleep thinking about what is not fixed yet."

Also, taking a job at a startup that helped him grow and devoting himself to continuous learning made a big difference. Choudhary said he spent hours studying every day.

Brit Morenus, Microsoft senior AI gamification program manager

Brit Morenus, a 37-year-old senior AI gamification program manager, studied English, communications, and marketing in college. She started at Microsoft about 13 years ago as an executive assistant, and for the first five and a half years at the company, she was a contract worker.

She later moved into a role focused on gamification — using game mechanics to teach and market Microsoft's products.

She spent about a year getting certifications that taught her about game mechanics, and in that position, she became a full-time employee. Six years later, she had the opportunity to start gamifying learning about AI, and spent three months learning about it.

Her advice to others who want to transition is to resist letting fear keep you from stepping outside your comfort zone. She also said that with AI roles, you need to learn how it works, not just use it. Morenus added that she doesn't regret her English degree because it's now more important than ever to understand how to apply the English language to AI.

"A lot of it is more English language than it even is AI," Morenus said.

Did you pivot into an AI role? We want to hear from you. Contact the reporter via email at aaltchek@insider.com, or via secure-messaging platform Signal at aalt.19.

• Marie Pabelonio, a Google editorial lead, graduated from college with an English degree in 2009.
• She highlights the value of her English degree in adapting to AI's impact in the tech industry.
• AI helps her meet deadlines and focus on the bigger picture, but a human touch is still essential.

This as-told-to essay is based on a conversation with Marie Pabelonio, a 38-year-old editorial lead at Google, based in the Bay Area. This story has been edited for length and clarity.

I've been at Google since 2019, and as a writer, I knew AI would affect my role.

Looking back on my career trajectory, it feels like nothing short of a miracle that I ended up where I am. I graduated with an English degree in 2009, right after the financial crisis, and I'm now an editorial lead in people operations at Google, where I co-lead a small team that drafts and editorializes about 4,500-plus pages of HR policies. I've used AI to automate processes, refine drafts, templatize, and meet deadlines that would be impossible otherwise.

At this point, anyone, regardless of whether they're a writer or not, has felt it: Is AI going to automate me? Is it going to eventually just replace my job? I don't think I work more or less because of AI; I just work very differently.

I was a humanities major and fell into Big Tech

The job market felt very volatile when I entered it, which I think a lot of young people entering the workforce today feel.

I didn't have a career plan. I was an English major because I loved reading and writing, and if I found a job where I could do that and build a specific skill set on top of it, I would be OK.

My first job was as a fact-checker for the publishing arm of an industrial supply company, and then I became a copywriter in the advertising and marketing space. In 2016, I moved from Chicago to the Bay Area and became an editor at Amazon's subsidiary, Goodreads. I stayed in the Bay Area and made my way to Google by 2019.

I wasn't surprised that AI changed my job right away

We've heard the word "unprecedented" so much in the last six years or so that nothing surprises me anymore, including AI.

My team works with stakeholders and policy designers to interpret and draft policies, whether they're return-to-office, hybrid work, or immigration policies. There are areas where AI is useful in our work, and the tool has helped us regain more strategic time by automating tactical parts of our process.

This includes training the AI on standard article structure, to include four sections like background, key details, process, and related resources, formatting consistencies, including where headlines, a bulleted list, or a table would be used, and five to seven non-negotiable details the user needs to know from the policy.

I think there's still a lot of room for that human touch in that process. Once I have the output, I spend my time on the more strategic pieces, like verifying tone and voice, determining whether the article actually achieves the user goal, and how it fits with the broader content strategy of other articles.

In our writing, the goal is to inject humanity and warmth as much as possible, especially when explaining human resources topics like an employee's health insurance, compensation, performance reviews, and career growth. AI can't do that by itself.

AI saved me when I had a tight deadline

Around the time we started using AI, I had a big project to update existing policies, and I was on a tight deadline. I spent a lot of time upfront strategizing about how I could use AI to accelerate my work and meet my goals.

To address the overwhelming number of first drafts, I used AI to template a structure for readability, created a checklist for tone, style, and quality, and because of that was able to focus more on streamlining stakeholder reviews to check for accuracy. I met my deadline with a few days to spare. This was when it clicked for me that AI was changing things in a huge way, when this deadline looked really impossible, and then it wasn't.

Still, there were many times I had to validate and tweak the outputs. I never felt I could use AI as my secretary and leave it alone to do whatever it wanted.

Studying the humanities gave me a particular edge in the AI job market

I think there will be more of a premium on how we think, not what we know.

When it comes to writing, it's about being able to articulate the reasons behind your choices. Why this phrase and not that? Why put this insight here and not there? There's a rationale behind your judgment.

In job interviews, the question of how you use AI at work will inevitably come up now, and your AI output is only as good as your input. Good writers can get better, but bad writers can get worse, and just because you're writing fluently doesn't mean you're writing well. Studying literature so closely helped me reflect more on questions instead of answers.

This is the time to brag about how you develop your own sound judgment and how you use that judgment in your AI inputs. As good as it is to develop hard skills, it's just as important, now more than ever, to focus on soft skills too.

Do you have a story to share about your writing job in tech or AI? Contact this reporter, Agnes Applegate, at aapplegate@businessinsider.com.

• Kanav Bhatnagar's job title, forward deployed engineer, has been described as the "hottest role in AI."
• He said his job is to be a customer-facing engineer who tailors products for clients.
• Context-switching and communication are important skills for FDEs, he said.

This as-told-to essay is based on a conversation with Kanav Bhatnagar, 24, a forward-deployed engineer at Rippling, an HR tech company, who lives and works in New York City. The following has been edited for length and clarity.

I got into software development because I wanted to build cool stuff.

Amazon hired me as a software engineer out of college, and it was a big learning opportunity, teaching me the fundamentals of engineering.

But it was a behemoth of a company, and I eventually wanted to work in a smaller environment where I could take more personal ownership over product decisions and learn more on the job.

After 2 ½ years at Amazon, I interviewed at a sales startup called Actively AI, where I landed a role in forward-deployed engineering.

The "FDE" role was popularized by Palantir, and it has been described as the "hottest role in AI." I liked that it combined software engineering with understanding business.

I spent roughly six months at Actively AI before I joined the AI-forward HR tech company Rippling as a senior FDE, in October 2025.

I've now been an FDE for roughly a year. Put simply, I'm a customer-facing engineer who tailors our product to each client. They describe their challenges and needs, and I build solutions and customizations.

Here's what my day-to-day is like, and the skills you need to break into this role.

My primary job is listening to customers. The results are very rewarding.

Software engineers can feel far removed from customers, because they often can't see their impact. In this job, I'm closer to the front lines.

A core software engineer can build something that serves the majority of use cases, but AI tools usually need more customization to work properly than regular software features. That's when an FDE steps in.

For example, a restaurant chain might have a labor-intensive process for tracking their payroll data that involves spreadsheets and manual data entry, which I'd help them to eliminate within Rippling's platform by using custom code and AI.

My primary job is listening to customers and understanding their problems, which was a learning curve for me, coming from a software engineering background. On a day-to-day basis, I'm in a lot of customer meetings, including visiting businesses who use our product to talk with employees about their experience with it. I probably spend an equal amount of time coding solutions and interacting with our core product teams.

Context-switching is an important skill to master in this job, where you could go from talking to a customer to debugging something to jumping onto another customer call shortly after.

I don't rely on an engineer to code something for me. I make a lot of decisions about the shape of the product and how to execute on it, which I really enjoy. It's very rewarding when a customer looks at what I've built after multiple iterations and says, "This is exactly what I wanted."

Technical and communication skills are equally important as an FDE

I think it would be pretty hard, although not impossible, to become an FDE without a technical background. With the dawn of vibe coding, it might become easier, though.

In my experience, FDE interviews feature technical rounds that test your coding skills, like in traditional software engineering interviews. You also have to show you can talk with any customer, including non-technical people, by asking the right questions to understand a customer's problem, and talk through how you'd design the solution.

To prepare for interviews, I have used consulting industry interview questions, which require you to explain how you'd meet client requests. I think both fields overlap, requiring rapid diagnosis, clarifying questions, and a clear plan of action.

There's probably more breadth than depth of technical knowledge required. In today's age of rapidly evolving technology, I try to spend time outside of work understanding what's new in the AI world and what new AI tools I can be using in my workflow by talking to colleagues and researching online.

I think my job is preparing me to be a founder one day

I'm interested in founding my own company one day, and I've previously heard someone describing the FDE role as a founder bootcamp. It provides a good foundational layer for entrepreneurship, helping you understand how a business functions from the sales process to how to build things.

The FDE role is evolving and no one really knows what direction it's heading in. Even if AI turns out to be unprofitable, I think FDEs will still have a place because of the demand for customer software. Products are becoming easier to build, and people in this role will be needed to handle large contracts with clients.

Palantir is an example of a company that's had FDEs since the 2010s, even before AI was mainstream.