“I feel the concept of authenticity has only become more important in our decision making.”
– Michelle Niedziela, PhD
Welcome to the second Humanity in Branding interview, where I explore the growing influence of AI and automation on society through conversations with people of differing perspectives. This interview is with Michelle Niedziela, PhD, who specializes in applying neuroscience, psychology, and behavioral science to product development and consumer research.
Throughout the following questions and answers, you’ll explore Michelle’s observations and insights about the growing impact of AI—its impact on both conducting consumer research and building customer relationships, as gleaned from research insights.
Here’s what that means for brand leaders, according to Michelle:
Brandingmag: What does “humanity in branding” mean for you given the work that you do?
Michelle Niedziela: When I think about humanity and people in general—especially as they relate to consumer and branding work—I think of authentic connections, of recognizing humans for all their complexities, emotions, and experiences. And I frequently contemplate how we are always trying to simplify people, boil them down to one-note labels or over-generalized measurements.
From the perspective of a behavioral neuroscientist, this involves using science to understand not just what people like and how they think, but also why they feel the way they do. It’s about integrating empathy, cultural relevance, and emotional intelligence into our product development and brand strategies.
Humanity in consumer work isn’t just about data or trends. It’s about storytelling and creating meaningful experiences that resonate with consumers on a deeper and often more complicated level.
Bm: You talk a lot about strategic foresight. Can you explain what that means, and how business leaders can achieve it?
MN: Strategic foresight is a method, a process of thinking that leads teams and leaders to anticipate and prepare for future trends and shifts in the world. It involves understanding the signals of change and creating adaptable strategies. It’s more commonly practiced with business leaders, where “futurist” consultants help them move beyond reacting to market trends to developing strategies that actively explore “what’s next” instead. (Examples include anticipating and planning for global disasters or possible technological breakthroughs.)
This involves tools such as scenario planning, consumer behavior modeling, and innovation workshops. But instead of keeping it at the C-suite level, I prefer collaborating with the day-to-day research teams and getting them into a workshop where we learn the concepts and mindset necessary to better introduce research insights into an agile action plan.
A lot of consumer research is done and forgotten or (in better cases) the insights are applied to a very specific business decision. But those insights can often be used in a broader way. For example, they can help you respond when products don’t perform as well as you’d hoped in the market.
A vast majority of product introductions fail when introduced to the market. Foresight strategies can help. They help you perceive impacts beyond your typical insights, engage cross-functional teams to create potential scenarios, and develop those action plans into realities. And it’s also a lot of fun. It fosters a culture of curiosity in which research results are explored collaboratively, and it saves valuable project data from gathering dust when it could very well provide teams with better ways of responding to the market.
Bm: You focus heavily on flavor, fragrance, and sensory technologies, but how does technology blend with such human-specific aspects?
MN: Technology and human-specific sensory experiences work well together when used to enhance—not replace—the nuances of perception and emotion. Sensory technologies like AI-assisted flavor optimization, virtual reality for experiential testing, AI-chatbots for qualitative research, or wearable devices measuring physiological responses help us quantify and predict what consumers might feel.
However, the human element remains crucial. As much as people try to skip it, you still have to ask real people real questions. And valuable insight still comes from human team members.
Technologies amplify our understanding but must be paired with a deep awareness of cultural context, emotional expectations, and individual preferences to deliver truly impactful sensory experiences. As you adopt new technologies, it’s important to ground them in the innately human work first, the complexity of human decision making, and the value of well-designed research. Because while new technologies are important and amazing, core competencies around the basics become even more important when we adopt them.
Our human perceptions of the nuances of sensory inputs, fragrances, flavors, textures, the multisensory experiences we have…these must remain appreciated and respected, never oversimplified. Humans are complex, and that’s what makes these challenges so interesting.
Bm: How is consumer research changing now that artificial intelligence is in full swing? Is this technology really helping us better understand the reasons behind people’s behaviors?
MN: For sure, it is. AI is revolutionizing consumer research by enabling faster data collection, real-time analysis, and uncovering hidden patterns in behavior. Tools like natural language processing for sentiment analysis or machine learning algorithms for predictive modeling have made it easier to analyze massive datasets and identify trends.
That said, while AI excels at “what” and “how,” it still struggles with “why.” That’s where behavioral frameworks and neuroscience come in—combining AI with human science allows us to move from understanding patterns to grasping the underlying motivations and emotions that drive consumer behavior.
This is all improving at an exponential rate. A study just came out in Nature Human Behaviour testing the capability of large language models (LLMs) to predict human decision-making behaviors. The research team conducted a series of experiments comparing the predictive accuracy of LLMs against human experts, using various decision-making scenarios to assess performance. Their findings suggest that LLMs not only match but often exceed human experts in forecasting human decisions. That’s amazing and impressive.
Bm: Given your research, how are AI and algorithmic marketing impacting the business-to-consumer relationship?
MN: It’s a lot to keep up with, but I recently heard something from Stefano Puntoni, a behavior scientist focusing on AI and professor at the Wharton School at the University of Pennsylvania, about this stress. “Everybody is one year behind,” he says. So, while you might feel anxiety, FOMO, and over-hyped expectations, he suggests doing your best, moving fast, and never forgetting that all of your competitors are also one year behind.
AI and algorithmic marketing are transforming how brands interact with consumers, making personalization more precise and real-time. It’s a shift that can feel both empowering and problematic.
On the one hand, algorithms allow brands to deliver hyper-relevant content, creating seamless, customized experiences. On the other hand, over-reliance on algorithms risks losing the human touch, leading to a lack of authenticity or ethical issues like manipulation. The challenge lies in maintaining transparency and trust while leveraging AI’s capabilities. I feel the concept of authenticity has only become more important in our decision making.
Bm: Which brands are successfully incorporating AI and automation into their brand experiences?
MN: Brands like Spotify, Netflix, and Sephora are pretty easy to spot as leading the charge. Spotify uses AI for hyper-personalized music recommendations, finding new and interesting ways to create unique emotional connections with users. Netflix’s algorithm predicts not just preferences but also moods, delivering tailored content that aligns with what users might feel like watching. Sephora has used AI to personalize beauty consultations, offering virtual try-ons and product recommendations. These brands succeed because they balance AI’s efficiency with an understanding of human behavior and emotion.
The Toyota Research Institute has been doing some amazing work incorporating decision and behavioral sciences into their product development and research programs. I also saw a keynote talk at the Society for Sensory Professionals from Lanette Shaffer Werner, Chief Innovation, Technical and Quality Officer at General Mills. She talked about how General Mills has made it a priority to encourage and help employees incorporate new technology tools into their work. But while they have a very tech-supportive environment, they also stress the importance of sensory science in the process—a very human-centered way of thinking. And I think that’s the key. Many of these tech-forward companies have learned they must incorporate human behavioral understanding.
Bm: What do these brands understand about adopting AI and automation that most marketers don’t realize yet?
MN: Successful brands recognize that AI is not a one-size-fits-all solution. Instead of using it solely for efficiency, they focus on blending automation with personalization, emotional resonance, and trust. Again, very human-centric. They understand that the consumer experience isn’t just about convenience but also about making people feel seen and understood.
These brands also invest in transparency—educating consumers on how their data is being used and ensuring that the benefits of AI are mutual. It’s authentic and honest.
Bm: It seems the biggest reason for leveraging AI and automation is because of the efficiency they provide, but what should brand leaders watch out for if they want their customer experiences to also improve in the process?
MN: Efficiency is undoubtedly a key driver, particularly because many companies are now focusing on employee productivity, but brand leaders must be cautious about losing sight of authenticity. Over-automating or relying too heavily on algorithms can strip away humanity in what are often quite intimate interactions.
Leaders should watch out for consumer fatigue from overly robotic or impersonal experiences. Ensuring a balance between automation and the human touch, addressing ethical concerns about privacy, and building flexibility into AI systems to adapt to consumer feedback are crucial for improving customer experiences alongside efficiency.
Bm: If you could debunk one myth about consumer research right here and now, which one would it be, and why?
MN: This is really from my own perspective as opposed to the overall branding community, but the biggest myth that I encounter over and over again is that consumer research tools from neuroscience or any advanced technology (like AI) can somehow “read minds.” Many people in the consumer research community believe that these tools reveal hidden truths consumers can’t articulate, but that’s not accurate. And I feel like it’s all part of a sales gimmick.
They say that consumers lie or can’t be trusted. And people often feel that advanced or new technology is always somehow better or more accurate. Instead, while tools like electroencephalograms (EEGs), eye tracking, and sentiment analysis can help us uncover patterns and correlations in how people feel and behave, they’re limited.
True consumer insights still require context, critical thinking, and an empathetic approach to interpreting the data. There’s no magic formula—just good science paired with human intuition.
Bm: If you could leave brand leaders with one piece of advice when it comes to bringing AI into their work, what would it be?
MN: Don’t forget the basics. Clients are surprised to find that my intro lectures often focus on the theory of emotion—the “basics”—but to use these new tools smartly, we have to first understand how humans work.
When we put too much trust in the technology, we oversimplify the human. And we end up disappointed with the results.
Humans are complicated. Carl Sagan is quoted as saying, “If you wish to make an apple pie from scratch, you must first invent the universe”, essentially meaning that to truly create something, you need to consider the origins of all its components down to the fundamental building blocks.
Everything is interconnected, and even a simple act like baking a pie relies on complex natural processes and human knowledge accumulated over time.
Cover image: Carlo