Artificial intelligence has become the hottest topic in the job market, and it’s not just engineers riding this wave
Artificial intelligence has become the hottest topic in the job market in 2026, and that is not an exaggeration at all. Companies of every size are racing to build AI-focused teams, creating roles that simply did not exist two or three years ago. What really stands out in all this movement is that it is not just engineers and data scientists who are benefiting. Professionals in design, communications, law, and even executive assistance are finding real paths into this space, each in their own way and building on what they already brought to the table.
According to LinkedIn’s Jobs On the Rise 2026 report, roles like AI engineers, consultants, strategists, and artificial intelligence researchers are among the five fastest-growing positions in the United States. Meanwhile, major companies are pouring staggering amounts of money into AI, cutting some traditional positions while simultaneously opening brand-new roles tied to the technology. The picture is clear: anyone who can position themselves in this space right now is building an extra layer of protection for their own career in a market that is reorganizing itself in real time.
The question that lingers is: can you really make this transition without a 100% technical background? The answer, at least for five real professionals who have already made this move, is a very concrete yes. One of the most interesting cases is a professional who came from UX design and now works explaining AI to the world, in a role that blends everything she already knew with what the market needs most right now. But she is not alone in this story. 👇
Throughout this article, you will meet five completely different career paths, each with its own lesson on how to break into the world of artificial intelligence from places that, at first glance, seem pretty far removed from it.
What the AI market actually needs beyond code
Before diving into the stories themselves, it is worth understanding why areas like design, communications, and even law have become such strategic pieces within artificial intelligence teams. For a long time, AI system development was confined to labs, spreadsheets, and lines of code. The final product was functional but was rarely designed for a real person to use with ease. That is when the market started noticing a huge gap: what good is a powerful AI if nobody can interact with it intuitively?
That question opened up space for a professional profile that goes far beyond making the interface look pretty. Today, anyone working in design within AI projects needs to understand how models make decisions, how to present results in ways that make sense to the end user, and most importantly, how to create experiences that build trust in something many people still view with skepticism. This requires a combination of critical thinking, empathy for the user, and a reasonable sense of how the technology works under the hood. You do not need to be an engineer, but you do need to speak the engineer’s language well enough to bring human needs into the conversation.
The result is that roles like AI Experience Designer, Conversational Designer, and AI Content Strategist are popping up at tech companies, consulting firms, and even public sector organizations. And most of these positions ask for exactly what professionals from humanities backgrounds already have in spades: the ability to synthesize information, a user-centered perspective, the skill to communicate complex ideas in simple terms, and experience working with cross-functional teams.
From lawyer to Microsoft’s first chief responsible AI officer
Natasha Crampton started her career as a lawyer and now holds a position that simply did not exist before: she is the first chief responsible AI officer at Microsoft. Her work includes collaborating side by side with engineering, sales, and research teams to ensure that responsibility principles are upheld throughout the building of AI systems. Beyond her internal work, she also contributes to shaping new laws and regulatory standards in the sector.
Crampton studied information systems in addition to law and always had an interest in the intersection of technology, legislation, and society. Even during the strictly legal phase of her career, she worked on technology-related issues, such as helping Microsoft draft contracts tied to digital products. That combination of expertise meant that when the moment came, the transition to leading responsible AI felt like a natural step.
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One of Crampton’s most interesting observations is that much of the real value lies at the intersection of technical knowledge and social science insights. According to her, many technical skills can be learned, so coming from a different educational background should not limit anyone’s ability to help shape the future of AI. Her tip for anyone who wants to make this move? Start using the technology in your everyday life, understanding how it works in practice before worrying about formal specializations.
From structural engineer to AI engineer at HubSpot
Georgian Tutuianu has been through several transitions within engineering. He started as a structural engineer, then moved into traditional software engineering, and most recently made the switch to AI engineer at HubSpot. A trajectory that shows how the ability to adapt and keep learning can open doors even when the starting point seems far from the destination.
One detail that made a difference for Tutuianu during the hiring process was the section of his resume dedicated to personal projects. He included just one AI project, but that was enough. During the interview, the topic came up naturally when they asked about a time he had used or built an AI agent. In his words, it was a robust enough project to fuel a rich conversation, and that was all it took to demonstrate his hands-on experience.
Another aspect Tutuianu highlighted was the format of the hiring process. Instead of the classic software engineering approach, which typically asks candidates to solve algorithms live, the evaluation at HubSpot was more practical: he completed a take-home coding assignment and then reviewed the work with the hiring manager. The logic was simple — show us you can build what matters to us. This shift in format is a sign of how the AI market is increasingly valuing real execution ability over isolated theoretical knowledge.
Persistent networking and total dedication at StackAI
Jai Raj Choudhary made the transition from a data-focused role to AI engineer at StackAI, an artificial intelligence agents startup. And the way he landed the job is a masterclass in initiative: he used the company’s product while he was still a student, then started sending messages on LinkedIn to StackAI’s cofounder. He did not stop at the first attempt. Choudhary also began publishing content about the platform and publicly offering suggestions to the company.
According to Choudhary, what really tipped the hiring decision was the fact that he deeply understood data quality, customer edge cases, relevant metrics, and the failure modes of the AI models and LLM systems being used. In other words, he did not just know how to operate the technology — he understood where it broke and why.
Choudhary also mentioned that relocating to San Francisco made a significant difference in the opportunities available to him. He described an intense routine, working from 9 a.m. to 9 p.m., six days a week, at a pace that reflects the so-called 996 culture that has been spreading through Silicon Valley. For him, working at a startup was essential for accelerating his learning, and continuous studying outside of work hours completed the process. He says he dedicated hours every day to skill-building, without breaks.
From executive assistant to AI gamification manager at Microsoft
Brit Morenus is one of the most inspiring stories on this list. She studied English, communications, and marketing in college. She joined Microsoft about 13 years ago as an executive assistant, and for the first five and a half years, she worked as a temporary contractor. From there, she moved into a role focused on gamification — using game mechanics to teach and promote Microsoft products.
Morenus spent about a year earning certifications in game mechanics, and it was in that position that she became a full-time employee at the company. Six years later, the opportunity arose to apply gamification to artificial intelligence learning. She invested three months studying the technology in depth before fully taking on this new responsibility as senior AI gamification program manager.
One thing Morenus makes a point of emphasizing is that she does not regret her English degree. Quite the opposite — she believes that deeply understanding how language works is more important than ever when working with AI, especially with language models that depend on text quality to generate good results. Her advice for anyone looking to make the transition is straightforward: learn how the technology actually works, do not just use it on the surface. And more importantly: do not let the fear of not being ready stop you from taking the first step.
From UX design to AI communications: the story of Sajani Lokuge
Sajani Lokuge is perhaps the most emblematic example of how UX design can serve as a direct bridge into the world of artificial intelligence. She started her career as a user experience designer and, about a year ago, made the transition to leading communications and content strategy around AI at the company where she worked. Her role now is essentially translating what AI does for audiences who need to understand the technology without being technical.
For Lokuge, the transition was the logical next step. She already had a background in computer science, years of experience translating technical problems into visual solutions for users, and she had built a public presence on LinkedIn focused on design and AI careers, which today has around 26,000 followers. When the opportunity to formally change roles came up, she already had all the necessary pieces in place.
What Lokuge emphasizes is that despite the job title being different, the skill set she uses daily is very similar to what she applied as a UX designer. The difference is the object of the work: instead of designing screens and interfaces, she now designs how people understand an entire product category. It is design of comprehension, not pixels. And that ability to make the complex accessible is exactly what companies need most right now.
For anyone considering a similar move, Lokuge advises taking the leap before feeling fully prepared. Technology evolves so fast that everyone is learning as they go, and AI-related skills can be picked up along the way. She also stresses the importance of building a portfolio that demonstrates your capabilities. In her case, publishing content about AI on social media helped prove she could communicate technical ideas clearly, which made her an ideal candidate for the role.
The role of design in AI explainability
One of the concepts gaining the most traction within artificial intelligence teams is explainability — the ability of an AI system to clearly and understandably communicate how it arrived at a particular conclusion or recommendation. This might seem like a technical problem at first glance, but in practice it is a design and communication challenge before anything else. How do you visually represent the confidence a model has in a response? How do you signal to the user when the AI is operating on uncertain ground? These are questions that engineers do not have the time or the training to answer on their own.
This is where designers with a background in UX, data visualization, or visual communication step in with a contribution that no algorithm can replace. The ability to transform complex data into visual representations that people understand intuitively is one of the scarcest and most valued resources within AI teams today. Professionals who are skilled with prototyping tools, have experience with qualitative research, and know how to run usability tests are being hired specifically to work on the presentation layer of intelligent systems, ensuring the final product is understandable, trustworthy, and accessible to different user profiles.
On top of that, as artificial intelligence regulations advance around the world, explainability is shifting from a nice-to-have to a legal requirement in many contexts. The European Union, for example, is leading this movement with the AI Act, which establishes transparency obligations for AI systems deemed high-risk. This means that demand for this type of professional is not a passing trend. It is being built into organizations permanently, with dedicated teams, specific budgets, and well-defined roles.
Common lessons across all these career paths
When we look at these five stories side by side, some lessons come up consistently. The first is that none of these professionals waited until they were fully prepared to start making moves. They identified where what they already knew connected with what the market was asking for and went in that direction, adjusting course as they progressed. This learn-by-doing mindset is especially relevant in the AI field, where the pace of change makes it nearly impossible to be perfectly up to date all the time.
The second lesson is that humanities backgrounds are not an obstacle — they are an advantage. Both Natasha Crampton with her law background and Brit Morenus with her English degree demonstrated that understanding people, language, and social context is just as crucial as understanding algorithms when it comes to developing and deploying artificial intelligence responsibly. Both sides of the equation need to be present for the end result to work.
The third lesson is about visibility and practical demonstration. Georgian Tutuianu included personal projects on his resume. Jai Raj Choudhary published content about the company he wanted to work for. Sajani Lokuge built an audience of thousands of followers talking about design and AI. In every case, showing in practice what they could do was more effective than any certification or degree on its own.
How to start this transition in practice
One of the most common questions among professionals who want to move into the AI space is where to begin, especially without deep technical knowledge. The good news is that the starting point does not need to be a machine learning course or a graduate degree in data science. The most efficient move, at least initially, is understanding how the AI tools already on the market work from the user’s perspective. That means actively using products like ChatGPT, Midjourney, Gemini, Copilot, and others — not just for everyday tasks, but with an analytical eye on the experience they deliver, the friction points, the flows that do not make sense, and the opportunities for improvement.
The second step is to start building enough technical vocabulary to participate in conversations with engineering teams without feeling lost. This does not mean becoming an expert in neural networks, but understanding concepts like prompts, tokens, fine-tuning, RAG, and hallucination already makes a huge difference when collaborating with engineers and understanding why certain limitations exist in the products you will help develop. There are plenty of free and accessible resources out there for this today, from YouTube videos to simplified documentation that tech companies themselves provide.
The third move, and perhaps the most powerful of all, is to pursue hands-on projects, even small and unpaid ones at first. Redesigning the interface of an existing AI tool as an exercise, creating a conversation flow for a fictional chatbot, proposing usability improvements for a real product, and documenting that process publicly are all ways to build a portfolio and show that your transition is already underway.
The AI job market deeply values people who are already getting their hands dirty, even if they are still learning, because the willingness to move and experiment says a lot about how that person will perform on a team that is constantly navigating uncharted territory. The five stories we looked at here prove there is no single right path. What there is, is the willingness to take the first step and the awareness that the skills you already have are probably worth more than you think in this new landscape. 🚀
