UX Design and artificial intelligence: a career shift that makes more sense than you think
UX Design and artificial intelligence might look like completely different worlds at first glance. But for Sajani Lokuge, 26, that distance never really existed.
A former UX Designer and now an AI Content Manager at an industrial AI software company, she made a career transition that a lot of people would consider unlikely: she moved from a technical role focused on building products straight into the heart of AI communications.
And what made it possible? The exact same skills she used to design interfaces and solve usability problems.
Sajani’s story, published by Business Insider, is a real-world example of how transferable skills between fields can happen naturally, without that feeling of jumping into the unknown. The AI market is growing fast 🚀, but there is still a massive gap between the people building the products and the people who can actually explain those products to real humans. That is exactly where professionals with a background in UX Design have an edge that very few people recognize.
Here is how she made it happen 👇
From Lead UX Designer to AI Content Manager: how it all started
Sajani is from Sri Lanka, lives in Colombo, and studied software engineering before getting into design. She entered the workforce as a UX Designer and worked her way up to Lead UX Designer at the same company where she works today. During that time, her day-to-day was deeply technical: she was directly involved in building the product, translating complex problems into visual and functional solutions for users.
About 10 months ago, an internal opportunity came up. The company needed someone who could explain what was being built, both to internal teams and external audiences. And it could not be just anyone. It had to be someone who already understood the product from the inside out, who had hands-on technical experience, but who also knew how to communicate in a way that made sense to people who are not engineers.
Sajani was a perfect fit for that profile. She had already spent years translating technical complexity into understandable experiences for users. On top of that, she had built a public presence on LinkedIn talking about careers in design and artificial intelligence, growing an audience of around 26,000 followers. By the time the AI Content Manager role started taking shape internally, the company’s leadership already knew her work, the way she thought, and the kind of content she produced.
The transition was not a rupture. It was a natural evolution of something she had already been doing organically.
What she actually does day to day as an AI Content Manager
Sajani’s scope of work is a lot broader than most people imagine when they hear the title content manager. She leads the company’s AI communication and content strategy, which means she is involved in multiple workstreams at the same time.
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Her main activities include:
- Developing AI adoption messaging for internal and external audiences
- Producing and moderating the AI Global Town Hall, a monthly executive broadcast that reaches every employee across the company worldwide
- Hosting and producing a video podcast series called Voices of Industry, where she interviews senior leaders from different departments about how they are adopting industrial AI
- Working on sales enablement, helping commercial teams communicate the value of AI products
- Planning and executing strategic communications, adapting the message for different audiences and contexts
Some weeks lean more toward audiovisual production, with a good amount of travel involved. Others are more focused on strategic planning and internal communications. That variety is actually one of the things that makes the role so interesting and so connected to a design background: the ability to move between different formats and different audiences without losing clarity in the message.
What does UX Design have to do with AI communication?
More than you might think. When you work in UX Design, a big chunk of your day is spent understanding how people think, what they need, and how to make an experience clearer, more intuitive, and less frustrating. You map out journeys, create personas, test hypotheses, and at the end of the day, you translate technical complexity into something any user can understand and use without needing a manual. That process, which seems specific to product design, is exactly what the artificial intelligence market desperately needs right now.
The biggest challenge for companies building AI products is no longer just technical. Engineers and data scientists can build sophisticated models, train LLMs, and deliver systems that work beautifully under the hood. The problem is communicating what those systems do, why people should trust them, and how to use them effectively to an audience that has no obligation to understand machine learning. That communication gap is real, it costs companies real money, and it opens a huge window for anyone who knows how to turn the technical into the accessible.
Sajani recognized this early on. During her years as a UX Designer, she developed a very specific sensitivity for the language that connects product and user. It was never just about pretty screens or well-designed flows. It was about understanding what the user feels when something does not work as expected, what they need to read in order to make a confident decision, and how the content inside an interface can be the difference between a good experience and a frustrating one.
In her own words, the job of a UX Designer is figuring out what someone is trying to do and why they are stuck. That skill transfers directly to AI communication. Whether she is planning a town hall or producing a podcast episode, the starting point is always the same: research first, understand who will consume that content, what that person already knows, and what questions they will walk away with.
The skill transfer nobody was expecting
Transferable skills are a concept that the job market still massively underuses. The natural tendency is to look at someone’s background and search for exact matches from title to title, tool to tool. But the most valuable skills rarely show up that way. They live in thinking patterns, in the way someone approaches problems, in the ability to ask the right questions before anything else. And that is where UX Design delivers far more than what appears on a resume.
In Sajani’s case, the skills that carried over were mainly three:
- Empathy as a working method: designers watch users struggle with interfaces and learn to make decisions that are better for the person on the other end. That is the bulk of the work when you are explaining AI to people who do not speak the technical language.
- Hierarchical information structuring: the skill of organizing content in a logical, progressive way, taking the audience from point A to point B without skipping steps.
- Content as a functional component: in UX Design, text is not decoration. Every word inside an interface has a purpose. That mindset is extremely valuable in AI product communications.
As Sajani herself noted, job titles are changing, but the fundamental work and skills of a UX Designer are not going away. Instead of designing screens and interfaces, she now designs how people understand an entirely new category of products. The principles are the same.
Learning by doing: the path that actually works
One of the most interesting parts of Sajani’s journey is how she built the technical fluency she needed for the new role. She did not take a six-month course before getting started. She did not study AI model programming. The learning happened inside the job, in an integrated and ongoing way.
During her first three months as AI Content Manager, Sajani embedded herself in the AI product teams. She joined the daily stand-ups, asked questions that probably seemed basic at first, until they no longer did. She learned how the products worked, how the technical teams made decisions, and what the specific vocabulary of that world looked like. All of this without needing to become an AI engineer.
That approach is very telling. As she put it, a lot of people wait to learn first and then do the work. But in her experience, the work and the learning happened at the same time. This is especially relevant in the current AI landscape, where things move so fast that waiting to feel fully prepared before taking action can mean missing the window of opportunity altogether.
The groundwork she laid before the role matters too. Her LinkedIn presence, where Sajani posted about careers in design and AI, was not just a hobby. It was the public lab where she practiced the skill of translating technical concepts into accessible content, received real-time feedback, and built visibility for the kind of work she knew how to do. By the time the role opened up, she already had months of practical evidence showing she could deliver exactly what the position required.
What the AI market is asking for right now
The rapid growth of the artificial intelligence market has created a demand that has not been fully met yet: professionals who understand communication and content within the context of AI products. Knowing how to write well is not enough. Having a surface-level understanding of technology is not enough. What companies need are people who can operate at the intersection of both, who can question a language model with the same rigor they would use to test a prototype, and who understand that the quality of an AI’s output depends heavily on the quality of what goes into the input.
This is the territory where professionals with a UX Design background stand out naturally. The logic of thinking before building, which is central to design culture, applies directly to working with generative AI. Before asking anything from a language model, you need to understand the goal, map out the context, define the tone, anticipate ambiguities, and structure the request clearly. In practice, that process is very similar to building a solid design brief or writing a script for a usability test. The tools change, but the logic stays the same.
On top of that, the field of AI Content demands a skill that UX Design develops really well: the ability to iterate without ego. In design, you learn early on that your work is going to be tested, questioned, and revised constantly, and that is just part of the process. With AI, the dynamic is similar. The content that gets produced needs to be evaluated, adjusted, refined, and continuously held up against real standards of quality and usefulness. Anyone who has already lived through that in design has an enormous competitive advantage in this new context, because they have already internalized that perfection is not the starting point — it is the destination of a continuous improvement process.
Advice from someone who already made the switch
Sajani left some pretty straightforward guidance for anyone thinking about making a similar move. And the most interesting part is that none of it follows the classic playbook of take a course, get a certificate, and update your resume.
Do not wait until you feel fully prepared
She shared that she was comfortable and happy in her Lead UX Designer role. She was not looking to leave. But when the opportunity showed up, she understood she could not pass on it. The people who are already inside AI organizations are also figuring things out in real time. Nobody has all the answers. If you are a designer, you already have most of what this type of role requires. The AI part is learnable.
Produce public work instead of collecting certificates
One of Sajani’s most impactful statements was about not just grabbing an AI certificate and hoping someone will hire you. Instead, it is far more effective to produce work that proves you can explain AI to an audience. That is how she got noticed. Before the role even formally existed, the company’s leadership already knew how she thought, what kind of work she produced, and what her strengths were.
Treat your portfolio like a living organism
For designers specifically, the advice is to stop treating the portfolio as a static folder of past projects. It should function as a living body of public work. Write publicly, show how you think about AI before any interviewer needs to ask. That kind of early visibility is what creates opportunities that never even show up on job boards.
How to organize this transition in practice
Looking at Sajani’s trajectory, a few clear patterns emerge that can help any UX Design professional thinking about making a similar move. The first is to map the skills you already have before going out and searching for what you are missing. User research, qualitative data synthesis, UX copywriting, information architecture, workshop facilitation, persona building — all of that is directly applicable to communication and content work in AI projects, and many times the professional does not realize it because they are used to seeing those skills within a very specific context.
The second pattern is to seek context before seeking certification. There are plenty of AI courses available today, and a good portion of them focus on the technical side of building models. For someone coming from UX Design who wants to work in AI communication and content, the most efficient path is to understand how language models work on a conceptual level, learn to build effective prompts, study best practices in AI writing, and explore how companies are using generative AI in their content workflows. That contextual knowledge goes a lot further than a technical certification disconnected from real-world practice.
The third pattern, and maybe the most important one, is to build a portfolio that shows skill transfer in action. Listing skills on a resume is not enough. You need to demonstrate how UX Design thinking was applied to solve an AI communication problem, how a user journey became a content flow, how a research methodology became a process for evaluating outputs. That kind of concrete evidence is what convinces a recruiter or a hiring manager that the transition makes sense, because it is not just stated — it is demonstrated.
In the artificial intelligence market, which is still learning how to hire hybrid profiles, that clarity makes all the difference. 🎯
The landscape for designers who want to break into AI
Sajani Lokuge’s story is a very specific snapshot, but it reveals a bigger trend. The artificial intelligence job market is not made up exclusively of data scientists and machine learning engineers. There is an entire layer of roles tied to communication, content strategy, internal education, and technology adoption that needs professionals with human sensitivity, the ability to synthesize information, and the skill to translate between different worlds.
User experience designers carry exactly those competencies. The question was never whether those skills are relevant to the AI space. The question has always been whether professionals can see that relevance and communicate it convincingly to the market. Those who manage to do that, as Sajani did, will find a field of opportunities that is only going to keep growing in the years ahead.
And the best part: you do not need to wait for permission to start. Public work, an active presence on platforms like LinkedIn, and the willingness to learn alongside the market are already, on their own, the first step in that transition.
