In this episode of Product Momentum, we’re joined by Bansi Mehta, founder and CEO of Koru UX Design, an enterprise healthcare UX agency supporting some of the US’s largest healthcare technology companies. We discussed the busy intersection of artificial intelligence, product management, and UX Design. Bansi’s Sense – Shape – Steer framework helps guide UX design teams as they integrate AI into their products – and avoid the trap of AI’s drive toward mediocrity that limits individual creativity and expertise.

Here’s what we learned:

Avoiding the Trap: AI Solutions’ Race to Mediocrity

AI’s ability to rapidly generate hi-fi prototypes and voluminous content brings great benefit, but also significant risk. The risk manifests in mediocrity – i.e., solutions that drive to the mean. This sense of “good enough” stifles designer creativity and diminishes the quality – the Delight – of the final product.

“The speed of AI makes it easier than ever to churn screens,” Bansi says. “But it’s designed to deliver to that average mean that allows us to say, ‘that works, that makes sense.’ And that’s really the trap….these days, there’s less patience in the industry for discovery and research.”

Introducing the Sense – Shape – Steer Framework

To combat this new reality, Bansi developed the Sense – Shape – Steer framework to help teams navigate the complexity of building AI-powered products.

• Sense. Understanding the Problem/Opportunity.
  “Sense is where you’re really creating that sense of what is worth solving,” Bansi explains. “It’s the intersection of what the user needs, what insights we have in terms of their challenges, and the opportunities that are present. But we mustn’t stop there. We then look to see what AI can do for us. And where we see the intersection, that’s the sweet spot.”
• Shape. Designing the AI-Enhanced User Experience.
  We emerge from the Sense step with rich insights into our user’s desired experience, Bansi continues. “And as we approach Shape, we do so with an emphasis on the kind of UX challenge that we are trying to solve – from the user’s perspective. Using a storyboard, we proceed frame by frame to define the user’s journey, the problem that we are trying to accomplish.”
• Steer. Implementing, Evaluating, and Iterating.
  The Steer step comes once you have built something and you launched, Bansi says. “This is where we define and clearly articulate our AI eval criteria that we’ve said are critical for product success,” Bansi adds. “I’ve seen products make it or break it depending on whether they got their AI evals right. It’s one thing to hypothesize that your solution will work. But it’s a completely different thing when you actually try to build sophisticated agentic AI layers where there’s multiple configurations and prompts.”  

Broader Insights, Future Outlook

The conversation underscores the notion that while AI accelerates development and content generation, it also requires subject matter experts in UX and Product to demonstrate greater vigilance than ever to maintain quality and relevance. The Sense – Shape – Steer framework calls on product teams to think first about user needs before considering whether and how to integrate AI.

Our episode with Bansi Mehta feels like the capstone conversation to recent episodes with Nesrine Changuel, Teresa Torres, and Oji Udezue, where we examined bringing Delight to the user experience, re-engaging Discovery in the development process, and adjusting to the Speed of today’s AI-driven development.

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Sean Murray [00:00:00] We’re a little contracted to to talk about AI these days. It seems like that is always a topic that pops up. We actually make a joke normally about, hey, we have to bring this up once per podcast. I think that’s going to be a little bit of our focus today. But I want to know from you, based on where we are now with the technology with new workflows, what is this new intersection of product in UX? And what are maybe what do you what are your what’s your

Bansi Mehta [00:00:25] But I have to be honest, Sean, that the boundaries are actually blurring for sure. And it’s less about who does what and more about really teams coming together and figuring it out in the sense what needs to be done and that what needs to be done often irrespective of the role really, be it the researcher, be the designer, be at the product manager, the conversation often needs to start from what are we calling and why?

Dan Sharp [00:00:54] You said something really interesting to me that goes to something I was actually reading last night around the trap, right? And you said how AI solutions kind of drive to the middle, right. They’re driving to the mean. So would you agree then that, you know, in this world where it’s so easy to have an AI tool come up with a pretty high fidelity prototype that like, we really need designers to be thinking about like, Hey, what’s actually going to delight users?

Bansi Mehta [00:01:17] Yes, more than ever. Actually, Daniel, only the challenge is that because building has become so far so easy, I actually find researchers and designers struggle in terms of simply keeping up. And that’s the reality. Research, there was less patience in the industry for discovery, for the research. And now that we can actually act on any sort of insight or build something so fast, that tolerance level has gone even further now.

Dan Sharp [00:02:01] Sean, we just had a great conversation with Bansi Mehta. She’s developed a framework for analyzing how to use AI to solve product and design problems.

Sean Murray [00:02:12] The big thing for me, why this episode is important for everybody to listen to, is we’re all looking for frameworks and different ways to solve problems when we think about how can we incorporate AI into our products, and Bonzi’s got a framework for that. So regardless of whether exactly speaks to the situation that your product is in, it’s a new way to think about this specific problem space, and there’s a lot of different takeaways just for product management and kind of like the future of working with our stakeholders in general. Love it. Let’s have a listen.

Dan Sharp [00:02:48] Joining us on the pod today is Bansi Mehta, the founder and CEO of Quoro UX Design, an enterprise healthcare UX agency supporting some of the US’s largest healthcare technology companies. Her recent UX framework for AI is called Sense Shape Steer, which went viral with over 7,200 product managers and designers downloading in the first two weeks of release. I checked it out, it’s pretty awesome. She’s drawn to the elegance of a beautiful, simple interface and is endlessly curious about AI and the future of human-computer interaction. Which I think is true for Sean and I as well. Welcome to the pod.

Bansi Mehta [00:03:22] Thank you. Thanks, Daniel. Now, very nice to be here. Nice to meet you, Sean.

Sean Murray [00:03:29] It’s we’re excited to have you because we are framework nerds so we’re pretty excited anything that we can take and apply that day Like that’s that’s always a huge win for us. I want to start though with something that Dan kind of alluded to We’re a little contracted to to talk about AI these days It seems like that is always a topic that pops up We actually make a joke normally about hey we have to bring this up once per podcast But I think that’s gonna be a little bit of our focus today, but I want know from you based on where we are now with the technology, with new workflows. What is this new intersection of product and UX? What’s your take on the future of those roles?

Bansi Mehta [00:04:06] Well, I have to be honest, Sean, that the boundaries are actually blurring, for sure. And it’s less about who does what and more about really teams coming together and figuring it out in the sense what needs to be done and that what needs to be often irrespective of the role, really, be it the researcher, be the designer, be the product manager. The conversation often needs to start from what are we solving and why. Like how does it help anyone? And then from there really move forward. So I think that has not changed. If at all, it has become actually more important than ever, especially where creating a prototype or even a quick experimentation. I have actually seen that. I was part of actually a stand up today itself, we call it like one person stand up where every single day we identify that these are the enhancements or improvements we are going to make. And literally next day, the code is checked in. So that’s the speed in terms of how fast you can really go from quickly discussing idea to actually having the product or the code that actually can be shipped. So really what’s important here is to one, consistency, two, to really keep our eyes on who are we solving for, and three, really raising the bar as we do it, because I think creating average mediocre products and solution has become easier and faster than ever.

Sean Murray [00:05:45] I like that the focus is on what needs to be done, right? What problem are we solving? I think we kind of find ourselves circling back to that. We just had Teresa Torres on, she talked about how we’ve done so well, working discovery into our product processes and now that we can ship at the speed of light, we’ve kind of forgotten that we actually need to check back and make sure that we are actually delivering something that people want. I think one thing that I want to clarify is, from a roles perspective, I know you said that the focused needs to be on what needed to be. Do you see like some sort of future hybrid role where the actual the expectation is that you’re able to do that your skill set is rounded enough where you can actually do both parts of the job or do you think that they’ll still be distinct disciplines moving forward?

Bansi Mehta [00:06:28] I see that there is some merit, some ground where anyone can do the basic prototyping in the sense really using cloud, using cloud code. You create, you at least go from idea to having something that you can tow and then have a conversation around. That I think is becoming more table sticks than we would like. In terms of really polished craft, where you’re thinking on the edges, you’re really pushing the envelope in terms of solving this problem creatively and better than the most, that’s where, yes, the craft is still required irrespective of what tool that you require, that you use. For designers, really, hence, it’s less about really just being pixel perfect or… The speed in terms of, oh, this is how fast I can create screens and mock-ups and more about really, and that’s why I precisely use that word raising the bar because that’s the conversation I find myself having again and again with my own team, as well as the client teams. It’s easier than ever to churn screens. You can have full workflow and the way AI creates stuff, it is designed to be right, right? It’s designed to that average mean, which means you will never look at it and think that, oh my God, this is so bad or no, this doesn’t work. You will mostly actually nod your head and say that, okay, yeah, that makes sense, that works. And that’s really the trap. In this sense, if you use that as your starting point, Before you realize that quality really diminishes in the sense everyone is creating similar solutions. It is really your average middle as opposed to really thinking out of the box, pushing the envelope. And that’s where I feel that designers specifically really need to raise the bar in terms of what, how we solve the problems and how we, even if you are using AI, how we really push the limits.

Sean Murray [00:08:31] I love where you said talking about diminishing quality. So somebody came to me, we are always evaluating new AI tools. And he came to be like, check this one out, check this model out. Look, I asked it to create this user story and look, I’ve got seven pages of content. My first question was, is it good? Right? All we knew was that there was a lot of it, right? Quality is not volume.

Dan Sharp [00:08:51] So I love that answer. Thanks. Yeah. You said something really interesting to me that goes to something I was actually reading last night around the trap, right? And you said how right AI solutions kind of drive to the middle, right. They’re driving to the mean, which it seems like, right, that there’s a trap there where you get this pro to be say, this is awesome. And then your creativity diminishes. Cause now you’re looking at a thing and like you, you’ve kind of built in like, Oh, this how it should be. Um, on the pod, we’re big friends of, uh, Nezme and shangal and your product delight. Um, so would you agree then that in this world where it’s so easy to have an AI tool come up with a pretty high fidelity prototype that like, we really need designers to be thinking about like, Hey, what’s actually gonna delight users.

Bansi Mehta [00:09:35] Yeah, yes, more than ever. Actually, Daniel, only the challenge is that because building has become so fast, so easy, I actually find researchers and designers struggle in terms of simply keeping up. And that’s the reality. Research, there was less patience in the industry for discovery for the research. And now that we can actually act on any sort of insight or made something so fast, that tolerance level has gone even further down.

Dan Sharp [00:10:10] Yeah, it’s interesting. So Sean and I were texting before the pod. Um, there is a tweet that came out recently by a guy named Dax Reed, who is a developer working basically for kind of a no code AI solution. Uh, we had this quote, you know, your org really has good ideas. Ideas being expensive to implement was actually helping, right? Like that was a feature and now we’ve removed this. So I would talk a little bit about your framework, right. The sense shape steer model. And, you know, how your ideas are coming up to kind of make sure that the ideas are actually good before, right, before we actually go and build them quickly and find out that they weren’t so good in the market.

Bansi Mehta [00:10:45] Right, right. Well, actually, you won’t believe the framework came out of came more out of necessity than really where we say that, oh, we have figured it out. And this is the way to go. And why I say that is that I keep coming across teams and problems and the way we saw and and everyone seems to be struggling how to how to approach it. In this sense, there is a variety of starting points when we think about solving for AI. And when I say solving for, AI, it means really creating either features, which are AI-based, or reimagining the whole product, or creating a brand new product idea, which is AI-native product. And both ways, I have seen the struggle in this sense. Oftentimes, it will start with some sort of a POC. That an engineering team or somebody who could prototype and show that we could build something with AI will build it. People will get excited that, oh, wow, we can do it. And then the whole conversation starts in terms of retrofitting it, that, okay, now what problem it solves, how well it solves. I was actually surprised that how early in the stage, I was already having conversations where somebody would come to me in terms a prospect or a client or the team, And they would say that we actually built this. And it works. The problem is that either there is no adoption or people are not able to figure out because there is a mental model shift, right? There is a complete paradigm shift. To Sean, the point that you made earlier, where it’s seven page of document, it’s a real thing. With AI, generating more content is easier than ever before. And I mean, we work in healthcare, so AIScribe and visit notes. Translating it in terms of the SOAP notes, that’s a very, very common scenario nowadays, right? And it’s actually well adopted as well. The problem is that it’s, when we think that, yes, AI can actually listen to the whole conversation ambient listening, right, and then create the SOP node or visit node. The user experience in the quality of the note itself and in the nuance, right? How well it captured versus not captured and it doesn’t end there. It is, there is a serious conversation in the sense that what we talked about earlier, that if the answer is your average mean. Even doctors will nod their head and they say that yeah this looks good as opposed to being vigilant which they need to be and on the other end with more content being generated is it really reducing the work for the providers or they have to now go through it line by line because ultimately it’s still their license on the line because that’s our way of really offloading the liability to human. In other words, also human and loop, you can look at it both ways. So, I mean, and these are the points where you really want to question, right? That what is the user experience? Is this actually helping the user too? Users perceive that this feature or this capability is actually going to improve their life. And that requires going deeper, looking under the hood in terms of those responses itself and what will give accurate. Response versus not accurate, right? Where it will hallucinate, where it will actually create unnecessary amount or versions of content as opposed to really sticking to the point, so on and so forth. So yes, there was more out of the necessity that we need some sort of a structure in terms of how do we approach different kinds of AI problems that come our way.

Dan Sharp [00:14:37] Yeah, I definitely want my doctor to check the notes and make sure she’s prescribing me like 500 milligrams instead of 500 grams of amoxicillin for an ear infection or a wheelbarrow full of pills.

Bansi Mehta [00:14:48] Yes.

Sean Murray [00:14:51] Can you walk us through, kind of step-by-step, through your framework, through each of the three steps, walk us though that and talk to us about what questions we’re answering, why it’s important, maybe some tips on how to execute the step, could be a longer answer, but I’m really excited to hear about your framework.

Bansi Mehta [00:15:07] Of course, of course. So the framework is actually, as I said, divided into three parts, what we call sense, shape and steer. So sense is where you are really creating that sense of what is possible, what is worth solving. And before you actually go about saying that, okay, we are going to solve it. And these are the ways in which we can solve it, right? Which is really the Shape part of it. Now, if we talk about the sense part, and if I have to really explain it very simply, it is really the intersection of what is the user needs, what insights we have in terms of the challenges or the opportunities that are present. So insights are still the drop of descent in terms of that’s our starting point. But we don’t stop there, unlike how we used to do before, or how we would for non-AI product, right? With the discovery and then you directly start with the ideation. But here we say that, okay, these are the insights or these are problems to be solved. Now, let’s look at the AI side of things in terms of what are the possibilities and what AI can do for us. And where we see the intersection, that’s the sweet spot. And how do we make sure that there is some scaffolding or structure around it in terms of how do we really evaluate what are the AI opportunities, right? That’s where things get interesting actually. That was really the purpose behind creating the framework to create that scaffolding. So it allows you to think step by step. Like for example, one thing that we really think about is that one, what do we think that AI has access to versus not have access to? Now with AI advancing, with agent AI, with NCP, all of that, you actually really want to get to the depth of it in terms of what data set or ground truth that we have that would actually be used to train the AI. That’s one, right? Another thing that we really think about is what is the experience mode? In the sense, again, I see when it comes to AI ideas, the conversation often start with we need a co-pilot or we are going to build agents. And we can build this kind of an agent in this application that can do this, this, right? What we want to do is really start with that, what is the opportunity, AI opportunity and user opportunity, and then really say that, okay, if we say AI, what experience mode it is, right. Is it going to be ambient? Is it going to be generative AI? Is it gonna be agent AI? And agent AI also have multiple layers, right? It is not really just one task oriented. So how sophisticated we are talking about. Is it really one individual microagent? Which I also find, like we have also built experiences for very simple agent where all it would do is really, when you are creating, let’s say a new workspace, All it will do is just. Help you with documentation in terms of what are you building and hence what should be the description. As well as, on the other end, really the whole orchestration, right? And both needs clarity in terms what kind of AI mode it’s going to be and then what role AI will play in this. In the sense, yes, and this is where we are really building the hypothesis and hence The stent, right? This is where we’re really building the hypothesis that, okay, we think that AI can do this, this, this, and that’s how we actually build further understanding in terms of we are expecting that AI can help in terms of taking the notes and then creating follow-up tasks. AI can really, for example, understand that who this task would be best assigned to. Recommendation that, oh, these are the follow-ups tasks for let’s say, for example a provider. And this is who it should be assigned to and how it’s going to do that. And third and very important thing is where we also talk about the risk and guard rate. And this is where we actually talk about that what could go wrong is AI makes a mistake. Because this is where we already start thinking about that how much on us we want to place on AI in terms of it being really, really right. Versus how much autonomy that we want to really keep in terms of with the user, right? So all of these steps that we go through that allows us to really wrap. And this is not for the designers alone. The beauty and the right way of approaching this is that this is a combined conversation between engineering product and design where they are actually going through this together and trying to figure out.

Dan Sharp [00:19:52] Right. So a lot, a lot to unpack there. Um, so it seems like, right. The sense framework is very much your problem space identification, right? Yeah. Identify, you know, an opportunity either from you, the product design side and determine, okay, where might AI fit into this? So I’m curious, you’re at, you kind of talk about, you know, the, the model where, Hey, what are the risks of using AI and like what’s acceptable? Um, does your framework kind of have a go, no go for using AI to solve that problem?

Bansi Mehta [00:20:22] It is contextual. It’s framework-wise, yes. We should agree on the success criteria. And that success criteria is where we have this discussion. For example, we know that, how you said, that when your doctor is actually going creating the identified nodes and there is prescription, you want them to really look at it. So depending on where we are applying AI, how high-stake it is, how easy or hard it is to revert. Or reverse that step or action, all of that really depends in terms of what are the critical success factors and how accurate we want the AI to be. And it goes both ways. One is really, we keep human in the loop, but there is also a cost in terms of whether that human in loop will lean in, in terms on trusting AI versus not. And I’ll give you an example. Again, in healthcare space, there’s something called RCM. So the revenue cycle management, which is where after the visit and everything, how the billers and coders will kind of prepare and submit for the claim and how they’ll get payment. So this is the conversation that we need to have in the beginning to really say that, yes, we want actually 80 to 90% of recommendations to be accepted by users for this product to be successful, right? Or for example, in terms of follow-up or even prescription, we would probably need it to be 99% accurate for it to. For users to really feel that yes, I can actually trust and I want to lean in as opposed to every time being skeptical and unsure that is it the full list? Am I missing something? Do I really need to go through everything and have the sense of actually the mind, my work has increased as opposed to decreased, so. Having going through this exercise really allows to surface these kind of problems early on and then building for that because there are ways right in terms of how you can circumvent all these situations, as long as there is a conversation around it, and we surface it.

Sean Murray [00:22:36] To take us through the shape step. What are the problems that we’re solving and give us some execution tips.

Bansi Mehta [00:22:41] So towards the end of Sense, what you want to walk away with is some sort of an opportunity statement. And I call it opportunity statement, as opposed to the traditional discovery where you will have a problem statement. Because oftentimes, it’s an AI opportunity that we are talking about, right? We walk in with that. And in shape, there are a few things which are really, really important to start with. First and foremost, because we are talking about AI-based features and experiences, there may or may not be interface involved. And that’s why the emphasis is that we don’t start with really Figma because when we start with Figma, we tend to think interfaces. What we really emphasize on is that based on what we think is the opportunity statement and what we have seen in terms of the kind of UX challenge that UX problem that we are trying to solve, let’s think from user’s perspective and create a storyboard. And that storyboard, again, has a certain structure where what we are doing is literally the storyboard style. Frame by frame, we are thinking from user’s perspective and saying that at every frame, what the user is trying to do as part of the journey, the problem that we are trying to accomplish, right? What is their goal and what is the action? At one, on one swim lane, this is what we’re talking about. And on another swim lane what we were talking about that So what is the context? Why are they trying to do it? And what is a design decision in the sense should AI interfere here? Should AI here be more of silent, let user do it versus this is where AI can actually nudge or this is actually AI can already generate some sort of an insight and give user that boost depending on what they are trying to accomplish, right?

Sean Murray [00:24:33] I like where you get with model selection and token costs there. And I think the general population of AI users are starting to become more conscious of that, um, use the models for specific tasks and then just kind of like what the cost of tokens actually is. Yeah. Let’s get to, let’s get this steer. Tell us about, okay, we have our journey map. We have our AI touch point map. What’s the steer step.

Bansi Mehta [00:24:55] So steer step is an implementation, steer comes once you have built something and you launched. However, it is worth going through it while you are doing this exercise in terms of at least having some sense of definition around steer part, because steer is where you really define your AI evals is one very important part of steer. And I have seen actually product making it or breaking it in terms of make or break kind of a situation for the products in terms of if they have got their AI evals right or not. It’s one thing to in hypothesis saying that this will work and this is the kind of accuracy level we want to achieve in isolated use case. And it’s a completely different thing when you actually try to build sophisticated, let’s say agent AI layer, right? Where there’s multiple configuration for the agents and in terms of the prompts. So there was this one solution or product that we were building, which was about the compliance in the sense that for every call that was made, how compliant it was on certain predefined guideline. And, um, there… It had to be accurate. It cannot be where AI actually goes through the whole call conversation and then comes up with evaluation and says that and then we write about let’s say 80% of things but not write about 20% because that means a human will still have to go through the whole thing to determine how accurate it was right and at the same time it was not a orchestration in the sense where it’s not a simple yes or no it will depend on what is the protocol it is testing against, how sophisticated it is, and which certain scenarios it will face. And that’s the level we want to get to in STEER in terms of saying that what will be the AI-eval criteria? How are we going to test it? Because it’s not a QA in isolation who can do the AI eval, who sometimes need subject matter That’s for trie- to really say that this was the right medication that was detected or not, or this was the right kind of a follow-up and the location that was suggested by AI or not. That’s not something that a designer or a developer or even QA can do. Maybe they can do it for a certain set number of data set use cases. But when we really want to pressure test, stress test, we want a subject matter expert alongside. So this is the conversation that we have where we actually talk about different metrics that we are going to judge the success of the whole AI-based opportunity that we are exploring. So one is really accuracy. And you don’t always need 100% accuracy. It’s subjective, right? But we really need to understand that there is a fallback option, there is reversibility, or there is human in the loop. This much is acceptable versus below this level is not acceptable for XYZ reason. The important thing is that we actually are able to test it and prove it, that it actually helps them, right? Trust metrics to say that what will make users adopt this versus not adopt it, and how are we going to define that? Like, how would we say that, yes, this is the adoption, it is acceptable versus not, and what could actually affect it? So again, Defining this early on allows our teams to be vigilant about it, already prepared for it as we build the AI solution and we are ready to deploy because a lot of AI experiments remain experiments and they don’t get actually deployed purely because the thoroughness is missing.

Sean Murray [00:28:55] The thing that I like about sense-shaped steer is that while the framework was designed out of necessity for understanding the best way to build AI products, I think that there’s a lot about just building products in general and the way that you need to work with teams and subject matter experts throughout your entire process in order to ship something relevant and then how you learn once you have at least your first draft of your finished product. That’s awesome. I have some takeaways from everything you’ve given us today. My first one is the one that I really liked about it’s less about who needs to do what and more about what needs to be done. I really like that. I really that take part of expertise is recognizing diminishing quality. We’ve talked about, you know, how people present us with something that’s not ready, but they think that it looks cool. Right. And we’re able to recognize that it’s a poor quality because of our back, because of your expertise. I really like the sense shape steer framework. And then also something that you said that has resonated with the conversation that I had with a client last week was rethinking when an interface is appropriate. I can’t get too deep into what we talked about, but ultimately it came down to why do we actually force a person to log in to do this task? And that was because it’s the only way to get access today, right? And in the future we might want to have, you know, whether through MCP or through some new authorization protocol. We might just want to ask AI to do something, an AI model to do that we have access to, that we’ve authorized to go take action on behalf of ourself in another system that requires authentication. So the interface design is gonna be something that’s gonna be really interesting to watch in the future. Thank you so much for being here today. We love hearing about new frameworks and models because we like to be able to take away something we can tactically use today, this week. Something that we can share with our teams to get people thinking differently and changing their way of working.

Bansi Mehta [00:30:53] It’s been, it has been absolutely a pleasure, Sean. And I personally feel that I know teams are really struggling and people are really trying to wrap their head around it. So if I, if this framework can help teams in terms of making sense out of it, still and still move in the pace that they are required to move in this time and era. I mean, I think that’s the biggest gratification for me.

Sean Murray [00:31:20] What’s the best way for people to reach out and see what you’re working on now?

Bansi Mehta [00:31:23] I would say LinkedIn message would be the best way.

Sean Murray [00:31:27] All right. Well, hopefully you’re ready for a flood of, of LinkedIn messages. Thank you so much for being here with us today. Yeah. Thanks for joining us. Thank you.

Bansi Mehta [00:31:36] Thanks, Daniel. Thanks, Sean.