What is your deepest, most interesting secret? You don’t have to tell me, but maybe you’ve told ChatGPT about it or confided in Pi AI. Or perhaps you’ve sought advice on meal choices or entertainment options or asked for brunch outfit suggestions (🙋🏽♀️) from another AI offering. It’s not about what you asked or shared; what truly matters is the leap of faith you took. By interacting with AI, you embraced something recently unfamiliar to the masses, integrating it seamlessly into your everyday existence for tasks both trivial and transformative – and therein lies the user experience shift.
AI is not just reshaping brands; it’s redrawing the map of how consumers interact with brands and the world entirely. This revolution is ushering in a new era of user-brand interaction, where AI’s capabilities enable people to craft unique experiences and expectations from the brands they engage with. The evidence is clear, from Forbes highlighting AI’s multi-faceted impact on future consumer behavior to the New York Times discussing the dawn of personalized AI agents. This transition encompasses more than just technological advancements; it signifies a fundamental shift in user behavior and, subsequently, the trajectory of user research.
As brands navigate this new territory, the traditional methodologies of user research and insights gathering simply won’t work anymore. It’s no longer enough to predict user behavior; brands must now, more than ever, adapt to the fluid expectations of their audience by focusing on underlying consumer emotions and worldviews. The dynamic nature of AI-driven consumer interactions demands a fresh approach to understanding and engaging with the market and your audience.
These new rules for user insights aren’t just guidelines; they’re essential for moving your brand forward, given how AI is already conditioning users.
1. No more conclusions.
In the traditional model of user research, the objective often culminates in a definitive conclusion about user behavior, preferences, or trends. But AI’s ability to offer personalized experiences at scale means that consumer preferences and needs are no longer static; they evolve in real time based on new data inputs and interactions. The dynamic nature of AI encourages users to seek new information and experiences with that information continuously. In an era defined by AI-driven dynamism, the journey matters more than the destination.
This fluidity demands user research that prioritizes ongoing exploration. What is most important to understand is if the emotional bedrock underlying these wants and needs is also shifting or if it is remaining static.
Evidence of this change can be seen in how brands like Netflix and Amazon use AI to continuously adapt their recommendations, encouraging users to explore new content rather than settling on a fixed set of preferences. This approach enhances user engagement and provides these companies with a wealth of data on evolving consumer tastes and behaviors. Exploration allows for a deeper understanding of the emotions and motivations driving consumer behavior beyond mere surface-level desires. Looking at the patterns in user exploration over singular conclusions unearths more resonant user insights.
2. Intuition is a stronger signal than reason.
In TikTok videos and on other social media platforms, users are sharing how AI tools have helped them make choices more confidently and swiftly. What we’re seeing here is a broader trend towards valuing intuitive responses, which can provide deeper insights into user preferences and decision-making processes.
When people use AI chatbots like ChatGPT, they receive fast, condensed answers, allowing them to quickly hone in on the information they need in a highly iterative way. This teaches users to make rapid decisions based on brief hits of information rather than needing the time to source and synthesize their own findings.
AI is training the user to ask questions quickly and directly and then move on. It’s a very different process that will ultimately hone users’ gut or intuitive thinking. This is a huge shift that changes everything we know about formulating research questions and a resonant overall brand strategy.
Brands must adapt their research methodologies to capture gut reactions, employing interviews, surveys and other research vehicles that capture instinctual responses over reasoned ones. That means asking questions that are both clear and specific but also open-ended enough to allow for personalization. This approach not only aligns with the changing user behavior but also offers a more direct window into less filtered preferences and biases of your audience.
@the.rachel.woods #chatgpt can help you make decisions, and this is one of the capabilities of #ai that gets me most excited for the future. #rachelwoods #promptengineering #chatgpthack #generativeai #greenscreen
TikToker @the.rachel.woods discussing how AI can help people make better decisions.
3. Embrace the raw, unfiltered essence of your user.
In an era where AI technologies foster personalization at an unprecedented scale, the capacity to understand and celebrate users’ real, multifaceted natures becomes critical. AI won’t judge or make demands of who they want the user to be, so why should your brand? Too often, when working with brands at Concept Bureau, we find a common user worldview that brands need to speak to, but for one reason or another, this does not line up with how brands view their users. User research must allow for the multitudes we all contain.
The more users interact with AI, the more they are conditioned to expect relationships with no judgments or expectations put on them. I know people who have confided in AI about relationships, parenting, mental health challenges and career progression, sometimes using AI to help launch conversations in their real life that have made a positive difference. In each of these scenarios, AI was an adaptive, nonjudgmental conversation partner to help work out all of the kinks. This sets a new standard for how brands should approach their audience and user research: with openness, flexibility and a genuine appreciation for individuality.
Applying this principle requires a departure from traditional marketing strategies that often segment consumers into broad, static categories or personas. Tools like user personas can be helpful when building brands and new products, but they can also box you in when the market and, ultimately, people are more dynamic than a persona allows. Time and time again in user research, I see brands and leadership that do not allow their users to change or be who they really are, which are two things I can guarantee your user will do.
Ryanair is one brand that illustrates the value of recognizing and immersing itself in the reality of its users. Their clear vision of their patrons — who place primary regard on budget-friendly cheap air travel — guides the company to tailor its public engagement. In one TikTok post, a Ryanair customer talks about how Ryanair is so cheap and doesn’t care about other parts of the travel experience so much so that they will probably make a meme out of this video of him complaining — and they do.
@ryanair The loss to tommy must’ve hurt your bank account #ryanair #ksi #sidemen
Ryanair memes a video on TikTok of their user discussing how Ryanair is cheap and does not care about customer service
Ryanair’s ability to lean into and celebrate the position that its riders are, above all, in search of cheap pricing has translated into the brand capitalizing on this often funny dynamic and creating a large social media following in the process. What Ryanair is doing here is recognizing and valuing the intrinsic diversity, complexity and authenticity of its users rather than adhering to rigid or idealized user narratives and placing judgments if users don’t fit their ideals of what a traveler should be. Meet users where they are rather than where your brand assumes or wishes them to be because AI already is.
4. Go macro, not micro with insights.
Unlike micro insights focused on optimization and incremental improvements or mid-level insights for broader general user understanding, macro insights forecast future consumer trends and behaviors. They unearth patterns and are anchored by users’ emotions. Macro user insights enable brands to anticipate changes in consumer emotions, guiding proactive strategic decisions. AI will become decently good at micro and mid-level insight generation, but it takes nuance and context to unearth macro user insights that set strategic direction for a brand. In an article titled The UX Research Reckoning is Here, Judd Antin proposes that UX researchers focus too much on what he calls middle-range research, which he defines as “a deadly combination of interesting to researchers and marginally useful for actual product and design work. It’s disproportionately responsible for the worst things people say and think about UXR. Doing so much of it just doesn’t deliver enough business value.”
Antin’s critique underscores the importance of macro insights in not only predicting future trends but also in aligning research efforts with the broader strategic goals of a brand. Emphasizing macro insights does not diminish the value of micro or mid-level research, but it places them within a strategic framework where their contributions to incremental improvements and understanding serve the higher goal of strategic brand direction and foresight. This approach helps ensure research not only informs design and product development in the present but also contributes to the long-term strategic positioning of a brand in a competitive and ever-changing market. By reorienting focus toward macro insights, your brand can forge deeper connections with your users, anticipate shifts in the market and innovate proactively, securing your place as a leader in the AI-enhanced future of the user brand experience.
Remember the leap of faith you took by telling your secrets to an AI chatbot? It was never just about help with a difficult relationship, choosing the next binge-worthy series or finding the right brunch outfit. It was about embracing the unknown, trusting the process and learning a bit about yourself along the way. AI anticipates our needs but also becomes a trusted confidant, guiding us through mundane and meaningful choices.
The real secret isn’t just in the questions we ask or the advice we seek but in the profound shift towards a future where our interactions with AI reflect a deeper understanding of ourselves. The most interesting secrets are those that lead us to discover more about what it means to be human.
