When Everyone's a Researcher and it's a Good Thing

Be honest. Are you guilty of being a gatekeeper? 

‍

For years, UX teams have treated research as a specialized skill that requires extensive training, advanced degrees, and membership in the researcher club. We’re guilty of it too! We've insisted that only "real researchers" can talk to users, conduct studies, or generate insights.

‍

But the problem with this is, this gatekeeping is holding back product development, limiting insights, and ironically, making research less effective.  As a result,  product and design teams are starting to do their own research, bypassing UX because they want to just get things done. 

‍

This shift is happening, and while we could view this as the downfall of traditional UX, we see it more as an evolution. And when done right, with support from UX, this democratization actually leads to better products, more research-informed organizations, and yes, more valuable research roles.

‍

The Problem with Gatekeeping 

‍

Product teams need insights constantly, making decisions daily about features, designs, and priorities. Yet dedicated researchers are outnumbered, often supporting 15-20 product team members each. The math just doesn't work. No matter how talented or efficient researchers are, they can't be everywhere at once, answering every question in real-time. This mismatch between insight demand and research capacity forces teams into an impossible choice: wait for formal research and miss critical decision windows or move forward without insights and risk building the wrong thing.

‍

Since product teams often don’t have the time to wait, teams make decisions anyway, without research. A Forrester study found that 73% of product decisions happen without any user input, not because teams don't value research, but because they can't wait weeks for formal research cycles.

‍

In organizations where this is already happening (it’s most of them!) teams have two choices, accept that their research to insight to development workflow is broken, or accept that things need to change and embrace the new era of research democratization. 

‍

In Support of  Research Democratization

‍

The most research-informed organizations aren't those with the most researchers, they're those where research skills are distributed throughout the team. When Product Managers and Designers talk directly to users, with researchers providing frameworks and quality control they make more research-informed decisions which result in better product performance and lower business risk. 

‍

When PMs and designers conduct their own research, context doesn't get lost in translation. They hear the user's words, see their frustrations, and understand nuances that don't survive summarization. But there is a right way to democratize, which not all organizations are doing. 

‍

Democratization as a consequence instead of as an intentional strategy, is chaos. Without frameworks and support from experienced researchers, it just won’t work. The goal isn't to turn everyone into researchers, it's to empower more teams to do their own research, while maintaining quality and rigor. In this model, the researcher becomes an advisor instead of a gatekeeper and the researcher's role evolves from conducting all studies to enabling teams to conduct their own. 

‍

Not all questions need expert researchers. Intercom uses a three-tier model:

‍

• Tier 1 (70% of questions): Teams handle with proven templates
• Tier 2 (20% of questions): Researcher-supported team execution
• Tier 3 (10% of questions): Researcher-led complex studies

‍

This model increased research output by 300% while improving quality scores by 25%.

‍

In a model like this, the researcher becomes more important than ever because democratization needs quality assurance. 

‍

Elevating the Role of Researchers 

‍

Democratization requires researchers to shift from "protectors of methodology" to "enablers of insight." It means:

‍

• Not seeking perfection because an imperfect study done today beats a perfect study done never.
• Acknowledging that 80% confidence on 100% of decisions beats 100% confidence on 20% of decisions.
• Measuring success by the "number of research-informed decisions made” instea dof the "number of studies conducted" 
• Deciding that more research happening is good, even if researchers aren't doing it all.

‍

By enabling teams to handle routine research, professional researchers focus on:

‍

• Complex, strategic research that requires deep expertise
• Building research capabilities across the organization
• Ensuring research quality and methodology standards
• Connecting insights across teams and products
• Driving research-informed culture change

‍

In truly research-informed organizations, everyone has user conversations. PMs do quick validation calls. Designers run lightweight usability tests. Engineers observe user sessions. Customer success shares user feedback.

‍

And researchers? They design the systems, ensure quality, tackle complex questions, and turn this distributed insight into strategic direction.

‍

Research democratization isn't about devaluing research expertise, it's about scaling research impact. It's recognizing that in today's product development pace, the choice isn't between formal research and democratized research. It's between democratized research and no research at all.

‍

Done right, democratization isn't the end of UX research as a profession. It's the beginning of research as a competitive advantage.

‍

‍Was human-centered design all wrong?

‍

No, but it fails to capture the whole picture.

‍

Don Norman introduced human-centered design (HCD), the approach of putting people and their needs at the center of the design process, in the 1980s and almost 50 years later at DDX San Diego 2026, he challenged the philosophy.

‍

Human-centered design gave the industry an important shift in perspective, paying close attention to the people who would actually live with what we create, but there’s tension. Design can create tremendous value for people while also creating unintended consequences for cultures, communities, organizations, and the world around them.

‍

If we optimize for the person in front of us, what happens to everyone and everything outside the frame?

‍

And as technology becomes increasingly agentic, that broader view becomes even more relevant. We're no longer designing only things that people interact with. We're increasingly designing systems that can act, make decisions, and influence what happens next.

‍

‍

‍

Summary

‍

‍

‍

‍

Emerging design philosophies

‍

‍

Life-Centered Design (LCD) expands the focus from the individual user to the larger living system that the product or service exists within. It seeks to align design decisions with global goals such as the United Nation’s Sustainable Development Goals.

‍

Intelligence-Centered Design (ICD) and Human-Agent Centered Design (H-ACD) are extensions of HCD where AI plays an active role in the design process alongside human intelligence, each learning from, making decisions, and mediating the process.

‍

Agent-Centered Design (ACD) puts AI agents at the center as the primary participant in the process, agents building for agents.

‍

‍

‍

Key elements of design and innovation in an agentic world

‍

‍

Start with the people, not the product

‍

‍

Good experiences have always started with deep understanding of the people who use the product, service, or system. Needs, expectations, and priorities are shaped by environment, so what works in one market, organization, or culture can't automatically be treated as a universal truth for all.

‍

That's why ethnography and other forms of contextual research remain so valuable. Before asking what we should build, we need to understand the world people are navigating.

‍

Questions to ask:

‍

• Who are we really designing for?
• What assumptions are we bringing from our own environment?
• What would happen if this design enters a different context?
• What are users' stated choices and how does that differ from their actual behavior?

‍

The discipline of understanding people hasn't gone anywhere. It's just being asked to stretch further, to the systems, risks, and impacts beyond individuals.

‍

‍

‍

Design for the system around the human

‍

‍

Behavior is shaped by systems, organizational structures, constraints, and the decisions made around you, so those factors are part of what we are designing for. Agentic AI makes this deep understanding of context even more important.

‍

When AI can act, make decisions, and influence what happens next, it becomes a key part of the system. Therefore, design has to account for the relationship between humans and their agents including how they work together, what artifacts are created along the way, how do they communicate, what decisions does AI make and what are its responsibilities.

‍

Questions to ask:

‍

• What happens outside the immediate experience?
• How might the system change over time?
• How do decisions get made? What's the logic behind them?

‍

One practical takeaway is to spend more time understanding what people actually do, not just what they say they do. Diary studies, observation, prototype testing, and live site testing can all help surface what people may not be able to articulate themselves. Rather than simply asking “why,” ask, “What drove you to make that decision?” This is an open question to prompt an honest explanation of what influenced their thinking and led them to take a certain action.

‍

‍

‍

Do more "design doing"

‍

‍

Design has no shortage of frameworks, methodologies, books, and white papers. While these give us useful ways to think, real change only happens when teams transform what they’ve learned into something tangible. In the end, impact isn't measured by the depth of your knowledge, but by what you actually create with it.

‍

Question to ask: What's the smallest thing I could do to build or test this idea this week?

‍

A simple prototype will reveal insights that hours of discussion never could. A real interaction exposes the hidden gaps between what people say and what they actually do. A quick experiment tells a team far more than another round of debate.

‍

At its best, good design thinking must always lead to good "design doing."

‍

‍

‍

Earn influence by translating value into the language of the business

‍

‍

Research and design often sits in the middle of an organization. Leadership sets direction, research gathers insight, design shapes the experience, then implementation follows. When each function speaks a different language, intent can get lost along the way.

‍

Designers and researchers are inherently connectors. We bring together different perspectives, make sense of complexity, and help people see how decisions affect one another. But we aren't often in the room when the decisions themselves are being made. Policy, process, business models, and organizational structures are all forms of design, yet designers are often brought in after those choices have already been made.

‍

There is an opportunity and a need for design thinking and "design doing" to move further upstream, and that starts with speaking the language of the people making those decisions: finances and margins.

‍

Questions to ask:

‍

• What does this insight or design mean for productivity, adoption, risk, growth, for the people working inside the system and for the people affected by it?
• What would this save, generate, or protect, in dollars, time, or risk?
• Can we connect the human story to a number?
• How can I socialize this upwards in my organization?

‍

Business decisions are design decisions.

‍

‍

‍

Designing for what comes next

‍

‍

The next era of design is expanding what we consider when we design.

‍

As systems become more autonomous, designers have an opportunity to shape not only experiences, but the policies, services, and the systems around them.

‍

The future of design is less about finding the right framework and more about what we choose to do with what we know.

‍

Our monthly newsletter, User Tested is for product, design, and research teams who want to make UX research a strategic accelerator.  Subscribe to User Tested.

‍

The Growing Challenge of AI Bias in Digital Products

‍

AI is rapidly reshaping our digital landscape, powering everything from recommendation engines to automated customer service and content creation tools. But as these technologies become more widespread, we're facing a significant challenge: AI bias. When AI systems are trained on biased data, they end up reinforcing stereotypes, excluding marginalized groups, and creating inequitable digital experiences that harm both users and businesses.

‍

This isn't just theoretical, we're seeing real-world consequences. Biased AI has led to resume screening tools that favor male candidates, facial recognition systems that perform poorly on darker skin tones, and language models that perpetuate harmful stereotypes. As AI becomes more deeply integrated into our digital experiences, addressing these biases isn't just an ethical imperative t's essential for creating products that truly work for everyone.

‍

‍

‍

Why Does AI Bias Matter for UX?

‍

For those of us in UX and product teams, AI bias isn't just an ethical issue it directly impacts usability, adoption, and trust. Research has shown that biased AI can result in discriminatory hiring algorithms, skewed facial recognition software, and search engines that reinforce societal prejudices (Buolamwini & Gebru, 2018).

‍

When AI is applied to UX, these biases show up in several ways:

‍

• Navigation structures that favor certain user behaviors
• Chatbots that struggle to recognize diverse dialects or cultural expressions
• Recommendation engines that create "filter bubbles" 
• Personalization algorithms that make incorrect assumptions 

‍

These biases create real barriers that exclude users, diminish trust, and ultimately limit how effective our products can be. A 2022 study by the Pew Research Center found that 63% of Americans are concerned about algorithmic decision-making, with those concerns highest among groups that have historically faced discrimination.

‍

‍

‍

The Root Causes of AI Bias

‍

To tackle AI bias effectively, we need to understand where it comes from:

‍

1. Biased Training Data

‍

AI models learn from the data we feed them. If that data reflects historical inequities or lacks diversity, the AI will inevitably perpetuate these patterns. Think about a language model trained primarily on text written by and about men,  it's going to struggle to represent women's experiences accurately.

‍

2. Lack of Diversity in Development Teams

‍

When our AI and product teams lack diversity, blind spots naturally emerge. Teams that are homogeneous in background, experience, and perspective are simply less likely to spot potential biases or consider the needs of users unlike themselves.

‍

3. Insufficient Testing Across Diverse User Groups

‍

Without thorough testing across diverse populations, biases often go undetected until after launch when the damage to trust and user experience has already occurred.

‍

‍

‍

How UX Research Can Mitigate AI Bias

‍

At Optimal, we believe that continuous, human-centered research is key to designing fair and inclusive AI-driven experiences. Good UX research helps ensure AI-driven products remain unbiased and effective by:

‍

Ensuring Diverse Representation

‍

Conducting usability tests with participants from varied backgrounds helps prevent exclusionary patterns. This means:

‍

• Recruiting research participants who truly reflect the full diversity of your user base
• Paying special attention to traditionally underrepresented groups
• Creating safe spaces where participants feel comfortable sharing their authentic experiences
• Analyzing results with an intersectional lens, looking at how different aspects of identity affect user experiences
  ‍

‍

Establishing Bias Monitoring Systems

‍

Product owners can create ongoing monitoring systems to detect bias:

‍

• Develop dashboards that track key metrics broken down by user demographics
• Schedule regular bias audits of AI-powered features
• Set clear thresholds for when disparities require intervention
• Make it easy for users to report perceived bias through simple feedback mechanisms
  ‍

‍

Advocating for Ethical AI Practices

‍

Product owners are in a unique position to advocate for ethical AI development:

‍

• Push for transparency in how AI makes decisions that affect users
• Champion features that help users understand AI recommendations
• Work with data scientists to develop success metrics that consider equity, not just efficiency
• Promote inclusive design principles throughout the entire product development lifecycle
  ‍

‍

‍

The Future of AI and Inclusive UX

‍

As AI becomes more sophisticated and pervasive, the role of customer insight and UX in ensuring fairness will only grow in importance. By combining AI's efficiency with human insight, we can ensure that AI-driven products are not just smart but also fair, accessible, and truly user-friendly for everyone. The question isn't whether we can afford to invest in this work, it's whether we can afford not to.

‍