UX design and artificial intelligence: two worlds that connect in an unexpected way
UX design and artificial intelligence seem like completely different worlds at first glance. One deals with how people interact with screens, buttons, and digital flows. The other works with language models, neural networks, and large-scale automation. Hard to imagine a bridge between the two, right?
But for Sajani Lokuge, a 26-year-old living in Colombo, Sri Lanka, these two universes connected in a way she never expected. And her story, originally published by Business Insider as a personal account, is one of those that makes you rethink what it really means to make a career transition in today’s tech landscape.
Without planning a career change, without sending resumes to other companies, and without taking one of those certification courses everyone assumes is mandatory, she went from lead UX designer to AI Content Manager at the same industrial AI software company where she was already working.
And the most interesting part of this story isn’t just the new title. It’s the path she took to get there and what that path reveals about how the skills of someone who works in user experience design fit, almost naturally, into the world of AI communication. 🤔
If you work in tech, are thinking about switching fields, or closely follow the advancement of artificial intelligence, Sajani’s journey is going to make a lot of sense to you.
From lead UX designer to AI Content Manager: how it all happened
Sajani studied software engineering and started her career as a UX designer. Over time, she grew within the company until she reached the position of lead UX designer, leading projects and translating complex technical problems into solutions that made sense for the end users of the products.
Alongside her technical work, she started building a public presence on LinkedIn. She published content about design careers and about artificial intelligence. That consistent effort of sharing knowledge and perspectives about the industry ended up generating an audience of roughly 26,000 followers on the platform.
About ten months ago, a very specific need emerged within the company: someone who could explain what the company was building in terms of AI to both internal and external audiences. Leadership already knew Sajani’s work, had already seen how she thought and how she communicated complex ideas in an accessible way. The transition was almost natural.
Today, as AI Content Manager, she leads the communication and content strategy around artificial intelligence for the company. That includes developing messaging around AI adoption, producing and moderating a monthly global town hall broadcast to all employees worldwide, and hosting and producing a videocast series called Voices of Industry, where she talks with senior leaders from different areas about how they’re adopting industrial AI in their operations.
Her routine varies quite a bit. Some weeks are more focused on video production and travel. Others involve strategic communication. And there are also weeks centered on sales enablement, helping the sales team better communicate the differentiators of the AI products. It’s a multifaceted role that demands flexibility and, above all, the ability to adapt to different contexts without losing message clarity. 🎙️
What does a UX designer have to do with AI communication?
At first glance, the answer might seem to be: not much. After all, UX design is about flows, wireframes, usability testing, and user journeys. And AI communication seems like territory for journalists, engineers, or researchers.
But when you stop and think about it more carefully, you realize that gap is much smaller than it appears.
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Sajani spent years understanding how people interact with digital products — what confuses them, what engages them, what frustrates them, and what makes someone keep using a system or simply abandon everything halfway through. That trained and sharp eye is exactly what makes a difference when the subject is explaining artificial intelligence to an audience still figuring out what this technology can and can’t do.
As she explains, the core work of a UX designer is figuring out what someone is trying to do and why they’re getting stuck. That skill transfers directly to AI communication. When planning a town hall or producing a podcast episode, the process is the same: research first, understand who’s going to watch, what those people already know, and what questions they’ll walk away with.
Effective AI communication isn’t about using the right technical terms or citing the latest language models. It’s about clarity. It’s about understanding that the person on the other side of the screen might be confused, anxious, or simply curious, and that they need an explanation that makes sense in their context, not in the context of whoever developed the technology.
That is, essentially, what a UX design professional does every single day: translates complexity into something the user can understand and use. When Sajani started working with AI content, she wasn’t starting from scratch. She was applying a skill set she already mastered in a new context.
And that point is key to understanding her story. The career transition didn’t happen because she decided to abandon everything she knew. It happened precisely because she knew how to recognize what she already had and realized it held value somewhere else. That completely changes the way you look at a career switch, especially in a market that’s still figuring out which professional profiles make the most sense within the artificial intelligence ecosystem. 🎯
Empathy as a superpower in the AI era
Sajani highlights a point that tends to be underestimated in discussions about tech careers: empathy. For her, this is one of the most important transferable skills a UX designer carries.
In the day-to-day of design, professionals observe users struggling and learn to make decisions based on what’s best for that person. That constant exercise of putting yourself in someone else’s shoes is exactly what supports the work of explaining AI to people who don’t speak the technical language.
Job titles may be changing, as Sajani notes, but the underlying work and skills of a UX designer aren’t going anywhere. Instead of designing screens and interfaces, she now designs how people understand a new category of products. The principles remain the same.
This perspective is especially relevant at a time when a lot of people in the design market feel threatened by artificial intelligence. The message here isn’t that design is going to disappear. It’s that the competencies developed in this field have a much broader application than most people realize.
The skills nobody realizes they already have
One of the biggest barriers for anyone thinking about making a career transition is believing they need to start from absolute zero. That they’ll have to study everything all over again, build a new portfolio, earn a new certification, and convince everyone that they’re now a different person with a completely new skill set.
But what Sajani’s journey shows is that transferable skills tend to be far more powerful than any new certification you can add to your resume. She didn’t need to reinvent herself. What she needed was to see more clearly what she had already built over years of working in user experience.
In the context of UX design, there are competencies that translate directly to working with artificial intelligence content:
- User journey mapping — it’s the same competency that allows you to structure a content strategy that accompanies the user from their first encounter with an AI concept all the way to the moment they feel confident enough to use a tool or make a decision based on that technology.
- User research — a central part of any UX designer’s work, it connects directly with the ability to understand who the audience of AI content is, what they know, what they don’t know, and what they need to know.
- Prototyping — which seems so specific to design, has a clear equivalent in content production: it’s the process of testing formats, language, and approaches to discover what truly communicates.
- Stakeholder communication — designers already do this all the time when justifying design decisions to product teams and leadership. In AI content, the process is very similar.
What Sajani did, in a sense, was audit her own transferable skills before anything else. She looked at what she was already doing well, mapped where those skills could be applied within the artificial intelligence space, and found an entry point that made sense both for her and for the company. That’s not luck. It’s a very strategic way of thinking about your career in a sector that’s still organizing itself and, for that very reason, highly values anyone who can connect dots that seem far apart. 💡
Learning by doing: the first months in the new role
An important detail of Sajani’s story is that she didn’t wait until she was fully prepared to start. Building her presence on LinkedIn gave her visibility, but she still needed to develop deeper technical fluency about the company’s AI products.
So how did she do it? By diving into the work.
In the first three months of the new role, Sajani embedded herself in the AI product teams. She attended the daily stand-up meetings, asked questions that probably seemed basic at first but became increasingly precise and relevant over time. The learning didn’t come before the work. It came alongside it.
This approach contrasts with what a lot of people do when thinking about switching fields: waiting until they feel completely ready, stacking up courses and certifications, and only then taking the first step. Sajani did the opposite. She accepted the opportunity and used the work itself as a learning platform. It’s a mindset that makes a lot of sense in a field like artificial intelligence, where knowledge evolves so quickly that waiting to be fully prepared could mean never starting at all.
As she puts it: most of her upskilling was structured around the work itself. A lot of people wait to learn and then do. In her experience, the work and the learning happened at the same time. 📚
Content strategy in the AI space: why it’s so different
Working with content strategy around artificial intelligence involves a level of complexity that goes beyond what most people imagine when they hear the term. It’s not just about writing well or knowing how to organize information. It’s about navigating a field where knowledge evolves rapidly, where terminology still isn’t fully standardized, and where the audience is extremely diverse — mixing people with deep technical backgrounds with others who are trying to understand the basics for the very first time.
Anyone coming from UX design is already accustomed to this type of challenge, because user experience also deals with very different audiences and needs to create solutions that work for all of them without oversimplifying for some or overcomplicating things for others.
Another aspect that makes AI communication a particularly interesting field for UX professionals is the need to work with uncertainty. In user experience design, you rarely have all the answers before you start. You form hypotheses, test, learn, adjust, and move forward. That iterative mindset is exactly what AI content demands, because the field itself is in constant transformation. What was true about a language model six months ago might not be true today, and anyone producing content in this space needs to be comfortable with the idea that knowledge is provisional and that updating your perspectives is part of the job, not a failure.
Content strategy within the AI ecosystem also requires a skill that UX designers develop almost without realizing it: the ability to tell stories with data. In design, you use research data and usability metrics to justify decisions and communicate results to teams and stakeholders. In AI content, you do something very similar: you take complex information, research findings, and system behaviors and build narratives that help people understand what’s happening and why it matters.
Sajani came into this role with that skill already developed, and it made all the difference in how quickly she was able to establish herself in the new position. 🚀
The role LinkedIn played in the transition
One element that can’t be overlooked in this story is the role that a public presence on LinkedIn played. Sajani didn’t land the AI Content Manager position by submitting a resume. She got it because, even before the role formally existed, she was already publishing content about design and artificial intelligence on a consistent basis.
When company leadership started shaping this new position, they already had a clear sense of how Sajani thought, what kind of work she produced, and what her strengths were. That goes far beyond a traditional hiring process. It’s professional reputation building in real time.
And here’s a piece of advice from her that’s worth highlighting: instead of treating your portfolio as a static folder of past projects, think of it as a living body of public work. Write openly. Show how you think about AI before anyone even calls you for an interview. That kind of positioning is what makes the difference in a market where many people have similar technical skills, but few can publicly demonstrate how they apply those skills to solve real problems.
Another important point: Sajani doesn’t recommend the strategy of getting an AI certification and hoping someone hires you. The path that worked for her was producing concrete work that proved her ability to explain artificial intelligence to an audience. It’s that practical evidence that catches attention, not a badge on a profile. 🎯
What this story reveals about the AI market
Sajani Lokuge’s trajectory isn’t an isolated case, and it’s important to understand that. The artificial intelligence market is growing at a speed that the tech sector itself is still processing, and one of the consequences of that accelerated growth is the creation of professional roles that didn’t exist a few years ago and, for that reason, still don’t have a well-defined academic path associated with them.
The AI Content Manager role is a perfect example. There’s no specific degree for this position. There’s no standard curriculum. What exists is a combination of skills that, when brought together in the same person, create a highly valuable profile for companies that need to communicate what they’re doing with AI in a clear, honest, and relevant way.
This opens a very real window of opportunity for UX design professionals, but also for other fields that develop competencies related to user empathy, clear communication, and strategic organization of information. Writers, product management professionals, UX researchers, and even teachers have transferable skills that connect with what the AI market is asking for right now. The question isn’t whether the skills exist. The question is whether the professional can identify them and position them in a way that makes sense to whoever is hiring.
Sajani’s case also raises an important reflection about how companies are thinking about internal talent. Instead of looking for someone from outside with a very specific technical profile, the company where she worked recognized that it already had, within its own team, a person with the right competencies to step into a new and strategic role. This is a sign that the AI market, despite all its technical complexity, is realizing it needs people who know how to bridge the gap between technology and humans, and that this type of profile can come from unexpected places. ✨
The mindset that makes the difference
Sajani admits she was comfortable and happy in her position as lead UX designer. She wasn’t looking to leave. But when the opportunity showed up, she didn’t let it pass.
That mindset of not waiting to feel completely ready is one of the strongest threads running through her story. The people already inside AI organizations are also figuring things out in real time. Nobody has all the answers. And if you’re a designer, you already have most of what this kind of role requires. The AI part is learnable.
That’s perhaps the most relevant takeaway for anyone reading this and relating to it: the feeling of not being ready isn’t a sign that you’re unqualified. It’s just the normal feeling of standing in front of something new. And as Sajani’s experience shows, the best way to get past that feeling is by starting, not by waiting.
The combination of UX design, AI communication, and content strategy is becoming one of the most sought-after skill sets in the tech market, and stories like Sajani’s show that this combination already exists in many professionals who haven’t yet realized the potential of what they have in their hands.
The field of artificial intelligence needs more than engineers and data scientists. It needs people who know how to translate what’s being built for the rest of the world. And those coming from the UX design universe have an advantage that shouldn’t be underestimated: years of practice doing exactly that. 🌍
