Empathy maps and user personas
Empathy maps and user personas are used everywhere in product development. They promise quick clarity about target users, making teams feel aligned. But popularity doesn’t mean reliability. If you use them straight out of the box, you won’t get meaningful insight, you’ll likely get noise.
These tools are often taught in UX bootcamps and pushed in startup environments as “must-haves” for developing user-centered products. But too often, they turn into abstractions that don’t reflect real users. Templates force teams to make assumptions about users’ motivations, thoughts, and behaviors. This can lead people into believing they understand the user when they’re actually seeing a fictional ideal written to fit the framework.
I’ve seen this firsthand. Teams interpret these personas as proxies for user empathy, but they’re often just extrapolations based on limited or misunderstood data. They feel accurate precisely because they look polished. The danger is the illusion of certainty created when the tool becomes the substitute for actual thinking.
C-suite leaders need to recognize that these tools are not sources of insight on their own. They must be treated as lenses. If your product decisions are based on templates filled in by assumption, your product will lean toward guesswork.
Empathy maps and personas can unwittingly reinforce stereotypes and personal biases
Empathy maps and personas can reinforce your worst thinking, especially if no one pushes back. Biases show up in hiring or marketing and they quietly creep into product assumptions. Teams bring their own life experiences and value judgments to the interpretation of user research. That’s where breakdowns start.
A mindfulness app team collected user feedback across genders. Women described their goal as emotional balance. Men described theirs as mental clarity. Same core motivations, different language. But the product manager reporting the data interpreted it as: women seek emotional regulation, men seek cognitive performance. That was wrong, and sexist. The difference wasn’t psychological. It was social conditioning.
That sort of thinking leads to bad assumptions and it shifts how resources are allocated. It shapes decisions about features, positioning, even pricing. And unless something challenges the team’s narrative, the end product ends up reflecting those internal misconceptions, not user reality.
Irrelevant or misleading data that can distract from what truly matters
One of the recurring issues with personas and empathy maps is the amount of irrelevant information they encourage teams to include. Templates often ask for things like a user’s age, job title, marital status, or even favorite brands, regardless of whether these details affect how or why someone uses your product. People add this information because the format invites it, not because it delivers value.
This kind of detail might build the illusion of completeness, but it doesn’t improve product decisions. Age, for example, doesn’t consistently predict behavior. A 60-year-old and a 25-year-old could both be heavy users of the same mobile fitness app, for completely aligned reasons. If your team is prioritizing a feature set based on assumed generational preferences instead of actual behavior patterns, then the product will underperform.
For leaders, the message is simple: stop prioritizing superficial data. Recenter your teams on actual usage behaviors, friction points, and goals. If a data point doesn’t change what you build, don’t waste attention on it. The role of frameworks is to reduce complexity, not to fill in space. If templates distract teams from identifying the mechanics of user engagement and decision-making, they slow innovation. Remove what doesn’t help you move faster, or build better.
Empathy maps and personas tend to generalize user behavior too broadly, often neglecting crucial edge cases
Even teams with the right research intentions often fall into the trap of generalizing users into vague, average profiles. When you layer multiple user types into one document and dilute specifics into general trends, what you get isn’t clarity. And when real-world complexities disappear from your models, the opportunity to serve more nuanced user segments disappears with it.
Products don’t scale efficiently by only optimizing for the average case. Market leaders break away by identifying unmet needs hiding in less visible use cases, what most teams overlook when relying too much on persona generalizations. If your team considers only a few blended personas and treats them as definitive representations, they’re likely ignoring outlier behavior that could be a source of growth or retention.
Executives should ask one question when reviewing frameworks like personas or empathy maps: what important behavior or need is missing from this summary? Encourage deeper segmentation. Encourage doubt. Ask what the data doesn’t show. Diverse user behavior is an indicator of broader opportunity. The teams that look beyond generalized representations are the ones that find it first.
Standard templates for empathy maps and user personas should be changed
Empathy maps and personas were created to organize user research, not to dictate how it should look. Yet in many product teams, these templates are applied without context. Teams fill in every field because the format implies it should be complete. That thinking slows down real insight.
Rigid templates often include assumptions about what users think or feel, even when there’s no research to support it. You don’t know what someone feels unless they tell you. Guessing undermines the entire point of user research. Templates also sometimes introduce irrelevant categories, like asking how users appear to others. If that’s not connected to the usage behavior you’re designing for, it adds noise to the process.
The smarter approach is to modify the structure to serve your product’s actual needs. Remove sections that don’t apply. Add in context that does. Use the format as a loose framework, not a fixed checklist. This is how you shift from assumption-based profiling to evidence-based strategy.
For executives, the expectation needs to be clear: don’t reward teams for aesthetic completeness. Reward them for strategic clarity. Show support for adaptation over perfection. If your teams are customizing their tools, they’re thinking, if they aren’t, they’re following. Following doesn’t invent anything. It copies.
Behavioral segmentation offers a more effective alternative to traditional demographic-based personas
Demographics are often easy to gather, but they don’t carry much predictive value. Age, gender, and job title might tell you something about who users are on the surface, but not why they take specific actions or what decisions they’ll make in your product.
What matters more is how users behave, what they’re trying to accomplish, the context in which they act, and what consistently triggers engagement or friction. Segmenting users by behavior aligns product strategy with real-world use cases.
The text offers a practical example: designing an app for pet owners. Instead of grouping users by age or pet type, the product team classified them as anxious pet owners, frequent travelers, and busy professionals. These categories directly informed what features needed to be prioritized and helped align product experience with emotional and logistical needs.
For executives, investing in behavior-driven segmentation is more strategic. It moves your product toward value faster. It gives marketing teams clearer messaging. And it supplies the kind of clarity that traditional demographic personas rarely provide. The closer you are to understanding actual behavior, the faster you can build products that match user intention. And intention drives growth.
Every data point used in constructing user personas should be critically examined
Most product teams collect user data with good intentions. But the mistake comes later, when they start filling in gaps without realizing they’re guessing. Personas and empathy maps often get populated with assumptions presented as facts. It looks polished from the outside, but underneath, the thinking isn’t grounded in truth.
If a user didn’t say it, or your research didn’t capture it, then it doesn’t belong in your model. Teams often feel a need to “complete the picture,” so they invent details. That approach breaks the connection between your product and what people actually want. It’s better to be accurate and incomplete than to present a comprehensive fiction.
From a leadership perspective, this is about defining standards. Product decisions should be based only on verified insight. Encourage teams to acknowledge ambiguity. If something is unclear about the user, call it out as a research gap, not a creative opportunity. Clear thinking beats complete-looking documents. Overconfident assumptions cost far more than admitting what you don’t know.
Set the rule internally: if the answer is not sourced directly from user input, label it clearly or leave it out. This makes sure that downstream decisions, whether on marketing, development, or UX, sit on a stable foundation.
Alternative frameworks can yield more reliable user insights
Jobs to Be Done (JTBD) focuses on what users are trying to accomplish in a specific situation. It’s less concerned with personality traits or fictional personas, and more focused on actions, motivations, and friction points. That shift in emphasis matters if you want your product to solve real problems.
Unlike traditional personas filled with speculative character traits, JTBD asks clearer questions: What are your users trying to get done? What’s stopping them? What triggers their search for a better solution? These things don’t require guessing. They can be observed, and they’re repeatable across segments.
This approach leads to better alignment between product decisions and user intent. It helps teams prioritize features based on outcomes, not on imagined backstories. It also shortens feedback loops because it takes assumptions out of the conversation and centers on measurable progress.
Executives should prioritize frameworks that increase product velocity and confidence in decisions. JTBD does that by cutting out noise and targeting problem resolution. It supports faster iteration cycles, clearer positioning, and closer alignment between what your teams are building and what customers actually need. If you care about solving problems at scale, JTBD provides the clarity to do it.
AI can improve user research by identifying recurring patterns in qualitative data
AI can process large volumes of qualitative data fast. For product teams analyzing dozens of user interviews or survey transcripts, that’s useful. AI can recognize common themes, patterns, and language clusters that might get overlooked by humans working through material piece by piece.
But AI doesn’t understand intent. It doesn’t know when a user expresses frustration subtly. It doesn’t filter results based on what’s strategically relevant unless it’s told where the focus is. So while AI can scan and suggest, it can’t be the final filter. Product leaders still need to interpret the results, test against real-world inputs, and decide what really drives action.
This augmentation, AI plus human review, is where the tool becomes effective. AI helps find patterns faster. It doesn’t validate conclusions. It doesn’t weigh strategic trade-offs. The senior team still has to guide interpretation and execution.
For C-suite decision-makers, the takeaway is simple: don’t delegate understanding to a machine. Use AI where it accelerates insight, but keep ownership of the narrative and direction. AI scales pattern recognition, not judgment. That judgment comes from leadership.
Despite their limitations, personas and empathy maps can still bring strategic value
Personas and empathy maps don’t need to be discarded. They’re still valuable when used as communication tools or frameworks for creative thinking. When you need to align different internal teams, or move quickly in a brainstorming session, having a shared reference point helps. It gives people something to react to and build on.
These tools also serve a purpose in stakeholder communication. They simplify research outputs in a way that’s easier for non-researchers to understand. When used clearly and sparingly, they reinforce alignment and help keep teams grounded in user-centric language.
What matters is how they’re built and why they’re used. If they come from real data and are used to start a conversation, not to close one, they bring value. They help connect insights to ideas.
Executives should signal clearly: treat tools like personas and empathy maps as flexible aids. Don’t let them become documentation exercises or product direction substitutes. Used at the right time, with the right intent, they provide structure that supports speed and focus. Ignore the idea of “best practice” templates. Adopt what adds clarity. Drop what doesn’t. That’s how you keep momentum and build smarter.
In conclusion
If you’re leading product, design, or strategy, the takeaway is clear, stop mistaking polished frameworks for actual insight. Personas and empathy maps aren’t the problem. Misuse is. When teams rely on these tools without questioning the inputs, they build strategy on assumptions, not data.
Demographics don’t tell you what people truly want. Templates aren’t a substitute for thinking. And no framework is complete without challenge and validation. The most effective teams know when to adapt, when to question, and when to simplify.
Invest in behavioral data. Prioritize clarity over completeness. Use AI to speed up discovery, but keep strategic judgment in human hands. Keep the signal, drop the noise.
