If you’re on a product team amid the current artificial intelligence boom, you’ve probably realized the age of steady and incremental product iterations with sturdy six-month roadmaps feels, in many ways, as though it’s in decline. 

Whether this is true and whether it’ll last is widely debated—but for those of us doing user research in the current environment, this feeling presents some unique challenges. 

At this point, we’ve probably all been in several meetings where we were notified by the visionaries of our companies that AI was in the process of changing our industry for good and that we ought not to be left behind. 

But what exactly is different when it comes to UX research in the age of AI? How do we adapt to meet what are undeniably new organizational needs?

You can wake up tomorrow with unprecedented functionality, making it seem like today’s iteration is irrelevant. This matters to researchers because we work with stakeholders to meet user needs. Features and flows can be rendered irrelevant with the introduction of something new. We must consider if tomorrow’s developments present a better path to meet user needs and if we have to pivot to innovate rather than catch up.

The research around each new product initiative must be thought about in the context of technology that’s developing faster than you can finish your PRD—and that’s a real challenge. 

Furthermore, AI-powered features often have a long way to go before they reach an optimal place. They generally get better and better as they evolve, but everyone in product knows a bad first experience often kills a user’s interest. 

The difference now is that “bad experiences” for users are sometimes a result of the technology not being quite there yet. Optimizing user experience isn’t like asking a developer to fix a bug or a designer to change the UX—it’s not within our control. But nobody wants to wait it out while we’re chasing our KPIs.

Finally, many of us would agree we have never seen shiny object syndrome in our industry like we do today. Simply put, we know as researchers that just because something cool is possible doesn’t mean it solves an actual user need and will help us reach our goals. But how do we know when all of this new functionality is unprecedented? 

Lightricks has been delivering innovative content creation experiences for more than a decade. Over the past year or so, we’ve transformed into an AI-first company innovating even more rapidly thanks to generative AI. 

As the user research lead, I know my team has had some wins and losses trying to navigate the shift. Through hard-earned lessons, I’ve found these to be the core principles at the heart of our best work during this unique moment in time.

If you’re like me, you became a researcher or designer because you never tire of trying to understand human behavior. It’s unlikely that what brought you to your field is that you were super duper excited about getting into the nitty gritty details of technology as it developed. Perhaps you’ve always been more or less content consulting with tech stakeholders as needed and thinking more about the user experience.

That’s all well and good, but on a personal note, at some point in the AI journey at Lightricks, I realized my failure to engage with the technical aspects of integrating AI into our products was quickly becoming my most significant liability. 

If you can relate, it’s important to acknowledge that many of us have self-limiting beliefs that prevent us from engaging with technical discourse. For example, I’ve often said, “I work in tech, but I’m not techy.”  

Here is what I’ve learned to be true: Artificial intelligence is as much about possibility, innovation, and people as it is about complex data sets and code. 

The more I read about generative AI as it relates to what we do at Lightricks, the more I find myself making better product recommendations and generating more relevant ideas. 

As soon as you decide and believe you can stay on top of AI as it relates to your product landscape, you’ll be giving user-informed guidance to your product team that fuels the innovation your company is undoubtedly going for.

Though most UXRs and designers consult with developers and technical researchers regularly, the bulk of that type of product work is left to product managers in many organizations. 

In all likelihood, you have people on your tech team consuming all the available content, trying everything new, and forging an understanding of where AI is and where it’s going. 

On your end, you’re constantly gaining a new understanding of the needs and behaviors of your target audience. When you’re in regular conversation with tech stakeholders, the technical vision and the deep knowledge of user insights can really come together.

This may require rethinking how and when you incorporate tech stakeholders into your flow. You’re off to a good start if you’re used to inviting tech leads when presenting research results. But maybe you want to consult with your dev or research team even when planning a research project to check and see if the technical landscape lends itself to questions about users that you may not have thought of on your own.

The artificial intelligence boom requires a new level of tech and human understanding that is simultaneous and elegant. Building better relationships with technical stakeholders is a good way for UXRs to rise to the occasion. 

Earlier, I mentioned shiny object syndrome: AI can do so many cool things. Some organizations struggle with being purposeful with what they integrate and why. 

As people doing research, if we can avoid the syndrome and give fast, rigorous insights regarding user needs—we can be a guiding force in ensuring that product iterations are purposeful and give users real value. 

What might this look like practically? It’s a combination of doing what we’ve always done—asking and answering the right questions about our users and avoiding the trap of only doing evaluative or usability-focused research.

When AI allows your team to release new things fast and furiously, it’s easy to get into a way of working where you’re testing the flows with users, giving quick UX insights and recommendations, and then moving on to the next thing. 

Though this type of research is important for the user experience, it’s crucial to stay on top of what’s happening even at the idea stage so you can do more exploratory and behavioral research to understand whether rapidly proposed solutions actually meet user needs.

This has always been true about the role of UXR—but the unprecedented pace and unpredictable direction of AI make it easier to do quick, evaluative research and miss the opportunity to help product stakeholders prioritize what actually gives tangible value to users.

Artificial intelligence poses challenges for user research, but if we adapt to what it asks of us, it’s an exciting time to be a part of designing unprecedented experiences. We can do that by engaging with the discourse, decreasing the distance between us and our technical teams, and never losing sight of our role in guiding product roadmaps toward meeting real user needs.