Accessibility, Bias Mitigation, and AI in UX Research

Transcript

Sarita Saffon:

Welcome back to the UX Whisperers podcast.

We are here to dive into the world of UX research and usability testing.

Today, we are excited to have a special guest with us, Eniola Abioye.

Born and raised in Oakland, California.

Eniola Abioye is a lead UX researcher at Meta and the founder of UX outloud, a career coaching and consulting business for user experience research.

At Meta she conducts research and build strategies in business messaging. Outside of work, Eniola is a thought leader, content creator and subject matter expert in user experience research and career development.

I’m Sarita Saffon, principal UX researcher at Userlyrics, and I’m thrilled to have Eniola on the show

to share her insights and experiences. So welcome Eniola!

Eniola Abioye: 

Thank you. So excited to be here

Sarita Saffon:

Eniola, to start, we have talked a little bit about what you do currently, but tell us about your background and how you got into UX research, what it’s like working for a global tech company like Meta.

Eniola Abioye: 

Sure, sure.

So I have been in UX research about ten years now.

My background is in integrative biology, and that’s what I studied in undergrad.

And so, like growing up and throughout college, I knew I was really, really interested in science and systems and kind of like figuring out puzzles and problem solving. And I’ve always been, like, big on people. I always liked talking to people, working with people.

And so I just thought that the only way to merge those things was to go into medicine, because that’s what I heard growing up.

And in college, I figured out I didn’t want to be a doctor. I was pre-med throughout college.

But then I figured out after, like, shadowing and talking to doctors about what I wanted to do.

And so after school, I was just looking for a job, and I have the spinal degree and didn’t necessarily know what I wanted to do with it. And my first job was, at a market research and user research firm that worked specifically in biotech.

So that was the connection for me. I didn’t really know user research was a thing in college. I didn’t know, I didn’t picture myself in a design career at all. I’m not really design oriented. And I didn’t understand how much attention went into building out products.

growing up in the Bay area, I’ve been around Silicon Valley and around tech, and so I understood, that it was a big deal.

I just didn’t know that there was a place for what I wanted to do within it. And so, I landed my first job.

I, you know, was doing so many projects as the agency side. And I was working with all the big biotech firms to really understand, like, how they were serving their, patients.

And I was talking to patients and doctors and understanding how different therapy area or different disease areas and different therapies, were received, by patients.

So, I feel like once I learned how to moderate and started conducting my first studies and leading my first studies, I just knew that this was, it for me.

Sarita Saffon:

Yeah. You just feel it.

Eniola Abioye:

Yeah, it was like, oh, this is it. I could do this.

And so that was, how I got started. From there I moved on to, working in human centered design at Kaiser, which was really fun.

From there, I shifted things a bit and moved on to Silicon Valley Bank. Went into fintech.

And now, like you mentioned, I’m at Meta and I work in business messaging.

So the conversations that happen between consumers and businesses on platforms: Messenger, Instagram, WhatsApp, and that’s been a blast.

I think over the course of my career, I’ve had, like one of my favorite things has been having conversations with, consumers and with, advertisers. But across the board, I’ve had really, intimate conversations. And just enjoyed holding space for people to share what their experiences have been and where they’ve fallen short and what they’ve really enjoyed.

When it came to talking to founders at Silicon Valley Bank, you know, the conversations around money in a banking relationship, especially as you’re starting growing a company. It was really, really intimate, similar to like, the conversations I was having at Branding Science, where I started out about different, disease areas and how, you know, having a disease and navigating life of the disease had impact. So that’s kind of been a trend, across my career.

Sarita Saffon:

So you as a UX researcher, what would you say is the biggest challenge that we face?

Eniola Abioye:

I think right now, the challenge that’s most top of mind for me and my work is collaboration, right?

Which also happens to be one of the most important things.

And I work as UX researchers because there are so many, different elements of building product, and there are so many people in the kitchen.

Which is a great thing because product teams should have a bunch of different toolboxes and a bunch of different skill sets in the mix.

But when you are working with multiple people, you’re working with different objectives, right?

And so as a product team, our job is really to align on what objectives are we focusing on now

and like what is our concerted effort in order to get there. And also within that you have people

who are wearing different hats, right?

You have your data scientist, you have your product managers, you have your user researchers, you have your content designers, you have your product designers. And so within that, with so many cooks in the kitchen, everyone has a job to do right.

And everyone has a focus and a toolbox that they’re, that their frame of reference is within.

And so pulling so many people, onto the same line of fire, onto the same objective over and over again or like consistently rather, is a challenge, but it’s also one of the beautiful things about working in product.

But as you know, working as a UX researcher, it just takes a lot of effort. I think collaboration is one of them.

The other biggest challenge, which is also one of the most important things that I’m seeing as a researcher is, making sure that we’re aligned with the business, at all times. And that means understanding the business of what we do.

Like I tell researchers and designers, like, it’s important to understand how the product that you’re working on makes money. Because that’s not kind of like antithetical to to the researcher to building user centered, product. But it’s really important to understand how the company objectives align with our objectives and how company prioritize priorities, reflect in our priorities.

And so those are the two top, top of mind things that I’m sharing with a lot of like user researchers in the field right now. Because there’s a lot of focus on them.

Sarita Saffon:

So while you talk about this, it comes to my mind, the self-referential design and how personal preferences, my impact to UX outcomes. What is your view on this?

Eniola Abioye:

Yeah. So I think as a UX researcher, one of the things that that really drew me to, working in the space, especially with consumers, is that I can really, identify with the consumers that I’m serving. When when I moved to Meta, I was thinking, well, there are so many people who use these platforms and wear different hats on the platforms.

And so it’s really easy to kind of identify and have empathy with the jobs to be done that people have to do on the platform.

But also, like you mentioned, with, with, design, it can be really easy to think about, you know, your use cases or your jobs to be done on a platform and think, well, what would I want to do?

And and this might be what other users want to do, but there are just so many different, cultural context. There are so many different, tasks or activities that people have on the platform. There are so many different types of people who are using it.

And like personalities and like how people want to connect and how people prefer, to access their information. And so it’s just so important to not assume that people think like me or move like me. And I think working, on a platform that is used by so many people like it really does cause you to step back and say, okay, I can’t make assumptions about what people would want.

I have to base the decisions that, in the recommendations that I’m giving to my product team on evidence. 

So what is the evidence that I am presenting or that I can pull? So that’s work. Working with my content person in my data, scientists like to produce that evidence so that I even hold myself accountable to the recommendations that I make or the decisions that I make. As a researcher need to be based on evidence, not evidence needs to show up quantitatively and qualitatively. So I think the way to mitigate the bias, my own bias and the bias of the people on my team is to pull data and and pull evidence and then base our decisions in that.

Sarita Saffon:

Would you have any other recommendation of strategies that you use to ensure that the designs reflect really the needs of the user, rather than not only the researcher preferences, but also the designers references?

Eniola Abioye:

We I’ve been in a lot of like working environments where, we’re an agile framework. So being able to, to test and bring users in and get user opinions, as early and often as possible is really has been like my anchor point to avoid kind of the bias of designing for myself rather than designing for the users that we’re targeting. So being able to do a lot of iterative research, doing a lot of like testing the platform with different members of the team has been really helpful.

But getting people’s eyes on it and people like touching and feeling and like playing around with the thing as often as possible has been really helpful.

Sarita Saffon:

Okay. And precisely, you were mentioning that you work for product, that it’s aimed to global audiences, including cultures, different cultures and different people from different cultures.

Why is it important to consider cultural diversity in UX research and design?

Eniola Abioye:

Yeah, so when we’re conducting research and designing products for people to use, people are just so different, right? People are coming in from different contexts. And when we look at the core values of human centered design or user centricity, one of those elements is, think of everything as a system, right? So no matter what it is that you’re designing, people are not using the product in a silo, right?

It’s not the only part of their day. It’s not the only part of their work. Typically they’re using your product. You know, in conjunction with something else, or, you know, they have things to think about or considerations or they have an entire experience.

So when we think about the products that we’re designing, we’re not just thinking about this one interaction or thinking about how it’s interconnected to all the other interactions that they’re having throughout their day. So there’s so much to take into account. And the more that we talk to people and understand, like the context that they’re bringing to using the product and like what it looks like or what their overall kind of like jobs to be done looks like the more we see, how interconnected everything is. So I think as we as designers and researchers, when we’re looking at, product development or when we’re looking at, building experiences out for people, we have to understand as holistically as possible what their experience looks like.

And so that’s where journey mapping comes into account and understanding step by step. 

What are they doing and what are the opportunities for delight in their day? How do we compare to other, you know, elements of their work or other tools that they’re using? And how can we make things as seamless as possible? Experience, experience, even though we’re only one part of it?

Sarita Saffon:

And could you share maybe an instance in your experience where a cultural misunderstanding like led to a product redesign, or where biases were successfully mitigated?

Eniola Abioye:

Yeah, okay. So I’ll shout out a couple. One, back when I was working at Branding Science.

I did a bit of device testing. And so we were testing devices in multiple markets. I was in APAC, I was in the US, and so I was trying to understand, how people felt about using a device, and it wasn’t intuitive and wasn’t clear across multiple cultural context.

So when I say culture, I’m thinking not just how people approach a different like therapy area, but also things like stigma, and what that looks like, and also like what people are familiar with as far as mental model. So like other tools that are similar, do that, do the colors resonate, do where the button is resonate, do like the instructions and things in the copy that are in the instructions resonate. And is it familiar according to people’s mental model and trying to build, a consistent product across multiple mental models is really difficult.

And so that took a lot of testing and a lot of really close collaboration with our content designer to make sure things were as transferrable as possible. So that’s one example.

Another example of a cultural context would be so like right now I work in business messaging. And so how people communicate, is super important and it differs everywhere. And it differs by, you know, type of user to different market. And so taking that into account, when we look at platforms or how they work, are the features that are built out on platforms and where they’re enabled, comes to play.

Sarita Saffon:

Okay. And we have talked about different forms of biases in this conversation. And perhaps it’s a good reminder for our audiences that the European Accessibility Act will come in effect next year, in 2025. In simple terms, the EAA goal is to make sure that companies that sell products

or services in the European market consider accessibility in their offerings.

How do you go about making sure that accessibility bias gets mitigated as much as possible?

Eniola Abioye:

When it comes to accessibility, like you said, it comes down a lot to bias, right?

When, the people who are designing, the product and in charge of like, shipping the product aren’t privy to the different ways that people navigate, accessibility needs when it comes

to, managing the product, it’s really easy to build it within, just the frame of reference of, okay, most people will interact with this product how I do.

So when we think about user research and the strategy behind it, as far as user segmentation, 

not talking to just the people who are kind of at the center, of how we expect people to use it and like the type of user segment we expect to see mostly on the platform, but also incorporating the people, in user segments or in groups that we see less on the platform,

but that we see. Or that we want to target or want to make, the tool inclusive for is super important.

So when we think about, recruiting for a study, for example, I think about, okay, who are we targeting or who who is most often, using this or who is most often having the experience where this tool would be valuable. And talk in recruiting them, but also making sure that I’m recruiting segments, that are differentiated. So if I, see most people using this are in a certain age group, I’m going to recruit on both sides of the age group as well.

If I see that, typically people have a certain type of job or level of tech savviness, I’m going to recruit on either side of that to make sure or in different job, categories as well, to make sure that I’m hearing from people who are not just, in the center, of the, the users that I’m thinking about. So I’m not just designing for users who are at the center.

So as many kind of like I break down the elements of the experience or the element of the demographics and infographics that I incorporate into the sample, and then making sure that I’m

differentiating at every characteristic.

Sarita Saffon:

Oh, that’s an interesting strategy. I actually haven’t heard that before.

And actually now that you mentioned that, I recently read in a study survey that highlighted the reasons why UX research studies failed. And one of the main reasons was that participant recruitment, meaning that the insights generated from the study came from the wrong people.

But in your experience, what has been the main roadblocks to designing great products for large audiences other than all of the aspects that we’ve discussed before?

Eniola Abioye:

Yeah. So, something that I’ve seen, in my past ten years in user research is the delineation between product strategy conversations and product design conversations.

When we think about product design and capd we think about conversations that are related to the design, to the research, or to the copy, of a product?

And then when we think product strategy, we typically think, you know, product managers are running conversations with engineers, with, data scientists, and the like.

And so, like having those as siloed conversations, I’ve seen that kind of slow down product design or make, product design a bit more difficult. When really when we’re having the high level product conversations as far as strategy and objectives and setting priorities, those are conversations that designers, researchers, content designers have to participate, participate in.

Because at the end of the day, we’re all product strategists, across the board, no matter what kind of, you know, role we’re in or what title we have, we’re product strategists, and we all have

the same goal of moving product forward, whether, you know, what if by whatever metrics you can think of, right, by revenue, by usage, by value, we’re delivering to our users.

And so rather than kind of delineate the like, you know, product design from our core strategy and like, you know, raising, you know, revenue and they and what we’re targeting and things like that being separate conversations, they’re all the same conversation.

And I think design like capti design, you know, researchers, content strategists not being a part of some of the high level product strategy conversations really sets us back as far as time and cohesiveness that we can have as product teams.

And so I would like to see that more. And I advise, researchers that I work with. All of those conversations are a space for you to be in and to bring in your product strategy, expertise, the thing that makes us researchers versus designers versus data scientists are what’s in our toolbox, right? Or what we specialize in. But at the end of the day, we all have the same goal of adding to the product strategy conversation. And so participating in those conversations and at the beginning of my career, you know, when a conversation was really around, you know, objectives and didn’t really mention the design or didn’t mention any, like, types of products or opportunities for research, I kind of checked it out.

And I’m realizing now that that’s one of the most important spaces for me to be checked in and for me to share my recommendations around product strategy.

Sarita Saffon:

And once you have the results in how do you ensure you gather the right insights from the right people before rolling out features or products?

Eniola Abioye:

Okay, so when you say the right people, do you mean my sample or do you mean product team?

Sarita Saffon:

Yeah, the sample, the recruitment that you do for for the study.

Eniola Abioye:

So I specialize in qualitative research. And so typically my sample sizes, are smaller than you would expect for like a quantitative research project. But I’m looking at multiple segments when I do. So beforehand before I do any research project, one, I’m looking at secondary research first.

So I’m doing my literature review and understanding okay. What do we know already and what can I take into this project, because it’s not about just learning as much as possible or like diving into as much content as possible, but like making it a streamlined as possible for me. So I’m doing literature review first, and then I’m always meeting with my data scientists to understand

what story are the numbers telling us? And what am I looking specifically to kind of validate, or what am I looking to dive deeper into based on what we know already, numbers wise? And then from there, I can kind of piece into the user types or the kind of like segments or like demographic information that’s playing into, where I’m trying to find more information.

And then from there, like I said earlier, I’m looking at the different demographics that we, that point to the people who are most likely to use the product, but also the different experiences.

So when I’m screening, I’m not just looking at like, hey, you know, where is this person based and kind of age and things like that. I’m also asking them, what’s your comfort with technology and what are some of your core jobs to be done and things like that, to understand not just their demographics, but their behaviors that’s indicating what information I’m trying to get.

And then looking, choosing the different kind of spectrum of activity that I’m most interested in and making sure that I’m talking to people at different points in that spectrum.

So I’m trying to get as much knowledge as I can in these, usually deep engagement. Because I’m qualitative. So ideas, usability tests, things like that. And making sure that I am talking to a breadth of people so that I can get a breadth of perspective, because at the end of the day,

I have to come and bring those perspectives and turn them into insights. And then from there, I’m working with my product team to really dive into, okay, what are the opportunities based on this information that we know, and really co-creating. Then like next steps based on my recommendations and insights. 

Sarita Saffon:

And I wanted to dig a little bit deeper in that piece where you actually gather this qualitative information, because I would guess that even in a small sample, you get a lot of information.

How do you work, to mitigate the different biases that we have talked about during that analysis and results and reporting part.

Eniola Abioye:

So typically at the end of the study, when I have gathered all of my information, I look at that and then I go into my data analysis. And different types of projects call for different data analysis methods, but typically I’ll start with like somatic data analysis.

So bucketing the data that comes back and like revisiting the core questions, and intended impact that I had at the beginning of the study.

And then looking at what, the answers and receiving to those questions. In that process for me, it’s not just me who is hearing the feedback.

So like, this comes back down to like how UX research is a team sport. I lead projects and I also have a team of researchers that we’re constantly talking about and kind of brainstorming and thinking through, what insights we’re getting back and how it relates to not just our team, but the teams that are around us in our organization.

And it’s been this way kind of throughout my career where I’m not just the only researcher on the team. And so being able to, like, thought partner with other researchers, and other designers and kind of think through, okay, what did we hear and what came through, and what are some like areas that we want to, like dive deeper into based on what we’re hearing?

Because there’s always surprises or there’s always really interesting tidbits that come through. And so being able to sit down and thought partner with other people who understand the goals of the project and what our objectives were and understand the information that came through.

It’s super helpful for me because I always say I think best out loud. I love just being able to throw ideas on a whiteboard and like, throw different buckets and understand what it is that we’re hearing and brainstorm with people about what we can do to turn the insights that we’re getting into impact.

So I would say, a great way to mitigate bias in data analysis is to not do it by yourself and to not keep that, brainstorming process in your head.

Sarita Saffon:

Okay. And once you present these insights to the rest of the team, how do you see the influence in the design decisions that are taking after that?

Eniola Abioye:

So that process of seeing your impact play out, can vary based on the product and based on the the process that you go through based on the methods that you use. But as researchers, it really is our job to track the progress of the insights, even if it takes six months or a year or two years. And so I look back to, the metrics that I, that we aligned on for the first, and at the beginning of any product.

When I’m scoping a project before building out a research plan or choosing a method, in that scoping process is understanding kind of what our core questions are, what’s our timeline? What are our objectives? What are our hypotheses that are already on the team? And so I always have that documentation to true back to as far as, okay, based on the questions that we set forth to answer, what are we hearing so far, but also what metrics are…what are the metrics that matter? What are the metrics that we are aligned on? Being our priority to move or shift? And so based on those metrics, I can team up with my data scientists to kind of check in on what the metrics look like, because those are the ones that the team has already identified that matter the most, to shift in this moment.

And so in order to track the impact over time and see how designs are, affecting the product or moving it forward, I look back to those same metrics. And how have you ever experienced a situation where you have to handle a project that involves, certain biases, like, for example, accessibility in specific, where it is deeply embedded in the user data that you collect.

In the beginning of my career, when I was working in biotech and working with vulnerable populations across different disease areas, it was all about accessibility.

So like my orientation to UX research and my frame of reference is all around having intimate conversations, being super intentional about the conversations I’m having. Because they were, monitored and tracked and I was ready for things that come up in conversation that needed to be that potentially need to be reported and things like that.

So accessibility and understanding how people behave and how different behaviors made space for, certain, therapies. Because it wasn’t just about, you know, designing. Good thing that worked. It was designing a thing that was intuitive and valuable enough and easy enough to use, so that it would be used.

Because when something is, I think at the core of like my research practice, like if something isn’t being used, then there’s an issue in the design, right?

Not with a user. We can’t say like, hey, we built this really good thing, it worked so well. You should be using it. Or you should change your behavior to around this thing that we’ve built.

It’s really, really, really hard to change behavior. It’s it’s extremely hard to, like, tend to approach design with the mindset of “I’m going to build this thing, and then I’m going to make people understand how great it is, and that they should use it”. But instead understanding what people’s journeys look like. And how people behave. And then building something that’s in alignment with that, is going to be much, much easier on the back end.

Sarita Saffon:

And on a personal level, what has been your biggest challenge when designing for accessibility, and how did you work on it?

Eniola Abioye:

I think the the challenge for me, in kind of contributing to design around accessibility is always, always checking, my biases because we all have them, right.

And especially around accessibility, our frame of reference, is our own abilities and how we lead our own lives and like our personal preferences and things like that.

So, like you said, kind of checking that, at the door, and being able to, like, incorporate kind of your own experience and your own perspective, but not leave it there, and take the step forward of like, or and take it to the next level of incorporating other people’s perspectives into what you’re building and building alongside other people so that there if you miss something or if, you know, a point of bias shows up for you, hopefully, because you’re working on a team of other folks and product folks that other people catch it, or other people call out, based on their perspectives, some opportunities, to be more inclusive and more accessible.

Sarita Saffon:

And we’ve talked about the past and the present, but I wanted to focus also a little bit on the future. How do you think AI is changing the UX research and design industries?

Eniola Abioye:

I’m a researcher that’s really excited about AI. When we think about AI, I’ve heard people kind of talk about it as this very like far away futuristic thing. But in reality, AI and machine learning has been around for a really long time. And so the ways that we leverage it to make user research more accessible and easier to do and easier to do on a consistent basis are really exciting.

When it comes to data analysis, especially like, aggregating insights from really large datasets, or looking through like really long transcripts has been really helpful for me. Because that part of, you know, conducting all the research, once the research is conducted and fieldwork is done, people want insights right away, if possible.

So like not having to do it all manually, because I don’t take notes during my sessions, I’m only focused on moderating. And so I depend on AI tools to go back and read and track through the things that I heard really quickly and be able to, to surface the insights as quickly as possible.

Because in this market, too, we know that a lot of UX researchers are spread really thin. So these AI tools that allow us to do things more efficiently or more quickly, have like made a really big difference in the amount of projects I’m able to take on or the amount of teams I’m able to support.

I think there’s been a lot of conversation around like, can AI do what user research does? And is this field going to slowly disappear as AI is here? And I have used and seen a lot of AI tools.

I haven’t seen any that even come close to being able to participate in product strategy in the ways that user researchers do.

I think when people bring up the conversation of, is AI going to replace user research? I think they’re thinking about a very small piece of what user researchers do and not thinking about us in the totality of being a product strategy partner, and bringing in user evidence and bringing in what we know, in order to apply to product.

I think they think about, okay, well, facilitating a study and sharing the insights.

But that’s really like 20% at most of what it is that we do.

And so as far as like the AI conversation, I think the developing tools to be able to do what we do more efficiently and like save as much of our energy for strategy rather than kind of like the logistics and like operations around your research is really exciting.

Sarita Saffon:

Definitely. I completely agree with you that we have to see AI more as a research partner than as our researchers substitute. But what could you see as maybe disadvantages of using AI inside UX research?

Eniola Abioye:

So the one thing I will say is that as more research tools have come on the market and made research more accessible, which I think is fantastic. We’re seeing more and more people who are not researchers doing research. And I am a big fan of democratizing user centricity. I’m a big fan of, you know, designing for the user is not only the researchers and only the product designers responsibility and also, user research is a skill, right? There’s a lot of intentionality that goes into good research. There’s a lot of rigor that goes into good research and good sampling and responsible insights and recommendations that take into account, both what the team is trying to achieve and what the user experience we’re trying to build is. There’s a lot of focus and intentionality that goes into user research. And so as I see more tools coming onto the market, some leverage AI, most leverage AI in some way, some don’t. But as I see more tools, I’m seeing more people, conducting research based on what they see.

Right. I need some questions, I need to have conversations with someone who uses the product or who might use the product, and that’s about it, and that’s not it.

And so what I’ll say is that there is still a responsibility to conduct really good research, especially when you’re conducting research that is, client or customer facing, and the rigor with which you use and research design directly reflects on the quality of the insights fthat you’re going to get back.

I’m seeing a lot of people based on what they see, think research is pretty simple and straightforward. And it can be, but I will say there’s a lot of rigor,intentionality that goes into UX research to get good results that you can use one, use efficiently and also, have high confidence in.

So I think as we kind of democratize people doing research, we need to help them really understand what goes into research and, and how we do it.

Sarita Saffon:

What would you recommend these teams that do not have a research team or do not have researchers at hand, but use this AI tools in order to at least approach UX research.

Eniola Abioye:

So I totally understand that some teams or organizations aren’t ready to invest in a research team or even their first researcher just yet. And so I’m not, saying that they shouldn’t utilize these tools.

I’m just saying that along with the access to the tools needs to come education. Even if it’s just the basics. Even if it’s free courses on LinkedIn or on YouTube. But like understanding the basics of human centered design and like the elements that we’re looking to drive and like how to create personas and how to understand the user journeys and the basics of service design. And like how we approach sampling, whether, you know, qualitative or quantitatively, like there’s just so much content, there’s so much to learn around how to do research, and how to do it efficiently, before kind of just diving into running up some questions and asking some folks, what their experience is like.

So I would recommend, investing if you’re going to invest in the tool, invest a time and resources into building up your knowledge base around how to do user research well.

Sarita Saffon:

And going back to that subject that we started talking about bias. What are the biases that will be introduced by using this type of AI tools?

Eniola Abioye:

I don’t think there are like an inherent set of biases that come with using certain AI tools. I think, there are like a plethora of biases that exist. I think using AI tools can be…it can make it easier to be less intentional or kind of like put the thought that you need into, you know, your scoping. And why are we designing certain questions and conversations in the way that we are designing them?

I’ve even seen tools that moderate for you and kind of run the conversation. So with the tools that we’ve seen on the market, I think the idea, is to be more efficient and to save time. And so with the use of AI tools, we’re seeing people take a step back from conducting the research and designing the research and strategizing around how research is used in order to save time and be able to scale. Which I completely understand.

And also research is still a high tech sport. So when you talk about bias and you talk about different opportunities or different risks that come with using AI, I think that’s one of the biggest ones is, research is designed to be pretty hands on, strategizing around the insights that come through and even formulating the insights based on the opportunities that we have as an organization. Very high touch.

And so, asking AI to lead that process, I think is really dangerous. But I also just think it’s not as you’re not going to get as much value out of it if you’re kind of replacing the wrong things with machine learning. 

Sarita Saffon:

So we have been talking about how AI brings new biases, but also how it can mitigate other biases such as, self-referential design that we’ve talked at the beginning.

How do you feel that this AI can actually mitigate that personal preferences, that we put in the

analysis of UX research?

Eniola Abioye:

So, so like I mentioned before, I think AI can be really helpful in examining patterns and surfacing them. And so when you look at a research study, when you’re moderating interviews or looking across multiple or you’re moderating, usability tests or surveys or things like that, I can be really helpful in surfacing patterns that maybe we’re missing and maybe we’re not seeing.

Also, when we talk about user segmentation, if there are patterns or trends that we’re seeing specifically within a segment, I think it can be really helpful to pick apart that nuance in those pieces for us. Especially as people are spread really thin.

And so if we’re balancing multiple projects and like, there are things that there’s higher potential to miss, maybe it’s because it’s within a segment that’s not our main one or within a segment, or insights that we’re less familiar with.

I think AI can be really helpful in kind of minimizing the bias around blind spots, or tunnel vision.

Sarita Saffon:

Okay. So we have established that definitely AI is part of our present in your research. But looking forward, what are some areas within the research, that you believe that will evolve significantly in the next few years?

Eniola Abioye:

Yeah. So my hope, is that in the next few years we see a lot of evolution around research repository technology. When it comes to research insights, I find that at a lot of organizations, learnings and insights move through people, right? People will ask, do you know anything about this? And, and do you have any evidence to kind of support, where we’re taking this and the direction that we’re taking design in. Or is there anything that can help steer us in to where we’re going? And as researchers, we don’t want to be bottlenecks ever. We don’t want to slow down the movement of information. And so what I’m hoping to see in the next couple of years is to have repositories that people feel comfortable in are intuitive for people to interact with.

And so if there was a bot or AI tool around surfacing insights that were there or navigating our existing knowledge base of research in a really easy way, I think that would be a game changer.

Sarita Saffon:

Interesting. Well, Eniola, that would be all for today. Thank you so much for sharing your thoughts with us and sharing this time with Userlytics.

I hope that everyone has learned a lot as much as I did, because I am a UX researcher and I have learned so much in this brief conversation that we had. Thanks again for for everything that you’ve shared with us today.

Eniola Abioye:

Thank you so much for having me. This was a blast! Thank you.