C (4:56)

I like that a lot. You know, there, there's some stuff I, I don't want to characterize it as alarmist because that's like, I'm trying to be dismissive, but you get, you get a dialogue of, oh, AI is going to take away all of our jobs. And what you're describing sounds more like all of us are going to get an assistant who makes our jobs a lot more fulfilling and easier to deal with. The bland stuff. Am I reading you right? I mean, that's feeling like a much more hopeful scenario.

D (5:27)

Absolutely. And Mike, this has played out multiple times in technology. If you think about, even if you just dial back, I don't know, 50 years, 45 years when computers were just coming into the enterprise. There was a time when maybe some select group in an enterprise had computers, not like everybody had a desktop or something. And eventually you join a job, the first thing that you get is a laptop, right? But there was thinking, oh, these computers are going to take away all jobs, right? Because we do so much with paper and pen and all that. That has not happened. If anything, the whole kind of computing industry has created more jobs and Internet came up, same thing. Or Internet actually created even more jobs, even more opportunities. Same is likely to happen sometimes. It's harder to imagine. It's like if I go back in history even further, think about Industrial Revolution, pre industrial revolution, over 80%, maybe 90% of humanity is just engaged in feeding ourselves, engaged in agriculture, working in fields. And you would imagine, you would be wondering, okay, what if some technology comes in and takes it all over? What will all these humans do? Well, they do what they do what we see all around us today. Right. Only 1% or less of humans are actually doing farming today, you know, but that's enough to feed everybody on, on earth at least, you know, in terms of production. Maybe don't distribution and other things have challenges but, but rest of humanity still found meaningful things to do and, and, and more meaningful things to do as, as technology advances. So I think what will happen is yes, there will be leaner companies, companies will have less number of people, but there will be more companies because there are more interesting things to do. Right. There'll be more important things to do, things that we as, as a civilization always wanted to solve. You know, critical disease, you know, climate issues. Right. You know, if you think of any such like global challenging thing for humanity, maybe space travel and other things, I think there will be a lot more things to do. But it's always hard to imagine right at that moment because real, it's your personal, maybe livelihood or job and so forth. But I think this change is coming and whether you like it or not, it will happen. And I think end of it, we'll enter into an era of higher prosperity. So I have a very optimistic view of the change. Not a pessimistic view at all.

C (8:37)

Yep, I love it and I, you know, I tend to resonate to that, to be really candid with everybody. But I think it's great to talk through it and you know, kind of help to explain it. So one of the things that to me is really distinctive about what we're doing with generative AI now is in the past. So often I've thought of technology as a tool, you know, so I was at Apple for a long time and even though people said they loved their Macintosh, they loved it as a tool, that, that helped them out in some ways it feels like with generative AI we're making it more like a colleague or an assistant or something like that. Talk to me about that. What's that like? How's that different and how are you driving toward that?

D (9:24)

Yeah, I think it's, think about it this way again, it's like progression of technology as a tool, meaning it can help you do something like physically like a car or whatever to a computer, which is, it can do something like little bit more, you know, where the human intellect was Needed before. Okay. It can calculate large things, it can store things, you know, project numbers, etc. Now we are entering an era where technology can be a thought partner. It can, you can bounce ideas off against each other. You could be calling some colleague, like, you know, just this week I had to research on something that I had no idea about. Right. In the past, I would be doing some searches, etc. Maybe I'll be calling someone who knows that industry. Right. And getting that information. Now I could just go to one of the, you know, LLMs and interfaces and ask a deep research question and get a very meaningful, substantial answer, well researched answer. It's almost like. And I can also kind of go back and forth and get almost like a thought partner, right. That doesn't mean humans have no role. It just means like, you know, I still needed to do this, take the initiative and think about and use my discretion. Okay. What it's saying, should I accept it, should I not accept it? Right. How do I apply whatever it's saying into, into real life? But it just accelerated my research hundredfold. I think increasingly that will happen. Technology as a thought partner, technology as an advisor, technology as a, as a guide. Right. You know, my son was going through his surgery recently and he broke his ankle and so on. But all his lab results came and I was like, okay, I'm going to ask, you know, Gemini or something. Yes. What does it mean? And it's, it was pretty stunning. It would tell me exactly what the doctor said. It's almost like I got a second opinion. I'm like, okay, you know, I am still going to go to the doctor, but I feel, you know, they are working off of the similar knowledge base.

C (12:09)

So, Yep. I, you know, I've had a similar experience of, you know, tell me in English what this actually means. And it's nice, it does a nice job of translating that stuff through. And it's, it's not a replacement for the expert, but it gives you a much higher feeling of confidence and comfort being able to move forward like an advisor would be. I really like that metaphor to it. So in prep for this discussion today, we were reading some of the stuff you've written about online and stuff like that. And two things that stood out were talking about deep customer empathy and rapid experimentation. Talk to us how you're applying those to what you're doing at Emma.

D (12:58)

Yeah, look, a key insight that we have built our company on is in the past, software enterprises companies had to morph their processes to suit the software because the software was Static. For example, if you buy your HR software, any RCRM software, it basically does what it does. So if your company does something completely differently, it doesn't work. So you change your processes to suit the software, or you put patchwork or use multiple different tools and then you have a lot of complexity. Every large enterprise you walk into, you see there is so much complexity because this non ideal, many pieces of software and many humans in the middle trying to make it work. Ideal would have been every company could on their wish generate software or generate automation workflows that would just work for them. I think we now have that opportunity and this is where listening to customers or becoming customer first is so important for this generation of companies. We believe we are writing software that actually adapts to how the enterprise works, not the other way around. The customer doesn't need to adapt to your software. Your software is malleable. It can take feedback and change behavior, which was not possible before. Now it's possible. The software adapts malleable. It adapts in its look and feel, in its behavior, what it will output. Also its ui. We are building things where it will adapt the UI dynamically, not just generating code, but it's actually generating, compiling, running that code in real time and so forth. So that's the new era and it needs new ways of building software companies. You know, I've worked in software all my life. I think in some way has been sort of, hey, this is how it works, you know, my way are right, and this is how you pay. You pay per seat, right? Again, you pay a whole load of money. Sometimes you see, oh, you, you are not using it, your team are not really using it, but you still continue to pay. All that is changing. You pay based on usage. The software listens to you and adapts its behavior, adapts how it looks and feels and what it does. And that's quite revolutionary in a way. And that's why we call our software kind of an AI employee. It's almost like you hired someone and you give that person feedback, that person changes behavior. That's what our software does.

C (16:09)

I love the vision of it. And it also is making me think about impact on existing tech companies. So if I'm a big running a large tech company and I've got a big legacy piece of software that's built on, I've developed a certain workflow. This is how you interact with my product. You're supposed to do this and this and this. It sounds to me like my product is at risk of not being able to Accommodate this new world where the software adjusts to the user and to the employees rather than the other way around. I mean, do they need to be all of them redesigning and refactoring their stuff?

D (16:52)

Absolutely. I think it's a time of change, of massive change. Not that we have not seen, maybe not in this scale, but we have not seen this kind of change. For example, when software moved from shrink wrap, you wrote software, it got into a cd. You shipped the CD to the era of cloud where the software could change behavior every day and you don't need a new CD or anything. So that was a big change and we saw the deck got shuffled, that the top companies in 90s were no longer top companies in early 2000s or 2010s in any area. If you look at enterprise software in any function, all changed because there was a change of paradigm. I think that paradigm shift is happening if I built our CEO of a large legacy tech company today. And I think every company that started before 2020 is a legacy now. Unfortunately, the time frame has squeezed, so I'll be worried and I need to adapt very quickly. Some of them are adapting and some foundation faster than others. A lot of this software that exists today will be stale, will not exist. A lot of SaaS companies will be dead.

C (18:24)

Yeah, yeah, it's, you know, it, I'm going to date myself. But it also, I, I think your example of the cloud software is a really good one. The other one that comes to mind is the graphic interface and you know, lotus 1, 2, 3, word perfect graphics, if you've even heard of those things, you know that they're not around anymore. But they used to be kings. They used to be the deities of the software industry. And so this is one of those generational turnovers that's going to affect everybody. It's exciting. It's either incredibly exhilarating about the opportunities or it should be incredibly terrifying about what could happen. But either way, it's not something to ignore. Help me picture. All right, so good. So we've established that number one, all the companies need to be thinking about it. Let's talk about the customers who are using your software. I know it's hard to think out into the future with anything related to AI because it's changing so freaking fast. But do you have any thoughts on, like how human teams and company organization, how's that going to look like in a few years as your collaborative model of software assistance takes off?

D (19:39)

Yeah, absolutely. I think every company has to go through what we call an agentic business transformation in the next few years. ABT agentic business transformation which is piece by piece. They have to look at every function, let's say take anything. Sales, customer support, hr, marketing, finance. Every function needs to rethink how they organize themselves, organize their core processes and how much is done by humans, how much is done by AI and how the AI and human collaboration happens. It's not going to be all human, not going to be all AI. Right. It's it also at what point AI needs to, you know, bring the human in the loop. All that they, they need to think through or they need to get advice on how to think through that. I think what will happen is the future. You asked me what will the future look like. My vision for the future is it'll be very natural. Look, let me. Sorry for the digression.

C (20:53)

Oh, go ahead.

D (20:54)

When I was in studying computer science in undergraduate, right. And then graduate school and I went to mit. Even in those days, something like a self driving car will read about. Like you know, CMU is experimenting, etc. Was like a pipe dream. And okay, this is science fiction. This will happen maybe in 200 years. But it's great to read about and think through. I mean on my way to work every day like five self driving cars pass me like I'm in Mountain View, California. Like there's way more cars all around. And my own car is self driving too. Like I have a Tesla, of course it asks me to put my hand, but I don't do anything. It actually drives on its own completely. So it's very normalized. My point is, you know, imagine even five years back or thinking, oh, I'll be driving on a road where, you know, I may be surrounded by a few cars where there is no human driving. Right. The seat driver seat is empty. It sounds like eerie. Similarly and that's normalized. What we normalized is you'll be working with a bunch of AI colleagues. You may have a AI manager, you may be managing AI agents or AI employees. So it will be a hybrid workplace. It may sound like a science fiction just like in 10 years back, imagining you are driving in a road where you are the only human driver. Which has happened to me now a few times. So I'm the only human driver in that some stretch of road and there are like three other way more around me. It will happen.

C (22:48)

Yep, yep. So. So I definitely buy into this idea of you got to transform the way you organize work. That's going to feel really daunting to somebody in a legacy company. I mean even in the. For a tech company, that feels daunting. It's going to feel really daunting to a company that's not as heavily on tech. Any thoughts on like where they should start? What's the first step? How do they make this into a digestible thing as opposed to oh my goodness, I have to blow up everything all at once?

D (23:24)

Yeah. I think the first thing is getting started early because you learn a lot. Your first experiment may or may not be successful, but you have to get started to learn. Second is making sure your employees understand this transformation will happen and reassuring them that look, it's for the right reason, reskilling them because they need to have a critical skill which is how to manage AI employees, how to work with this technology. I think it's like a critical skill today to be able to go and write the right prompts on ChatGPT or Gemini even though it's natural language and so on. It's supposed to be easy, but it's not completely trivial. You still need to learn. It's as interesting or as complex as working with another human or managing a set of employees. Right. When I became a first time manager, I thought how hard would it be? And I quickly realized, oh, this is a whole set of skills. When you are delegating something, coaching something, giving somebody feedback, very different than doing it yourself. Like how you frame that feedback, what you say it matters. Right. What do you say when you say all that? How much you say all that matters? So same thing. So everybody needs to be reskilled. So companies have to start that process today. The last thing I would say is get out of the fear of. I would say both fear, like under expectation. Over expectation and fear. Yeah, right. Over expectation. I've met some executives, they're like, oh, you know, tomorrow I'll have AI and it'll replace everything. Like I'll just press a button. Yeah, that's probably true. It's going to take a little bit of time. And second is, oh, it will never work. That's also not true. Right. The truth is somewhere in the middle and then the fear is, oh, I don't know, I will, everything will break or it will be bad. Like you know, you could have thought, oh, self driving car, they'll just run over humans all the time. Yeah, that's not true. They're more careful than the humans. Do they not going to have accidents? I think they will just like humans have, but at a much lower percentage. Right. Same thing you can expect from this technology in Some way. You have to consider the possibility that what we are building, it's a complex system and it's maybe, you know, at some level it is conscious in some way. And so we are working with other conscious beings.

C (26:36)

Yep, yep. And even if we're not sure about whether they're conscious, it turns out that writing prompts and interacting that way ends up being a pretty good way to instruct them anyway. So what the heck, you might as well. Yep, yep. I like those guidelines. I like that idea of how to, how to get started on it and staying balanced. So as you're looking at the things that, that Emma's doing are there, I'm wondering, are there some things that have you especially excited either particular uses of it or particular ways that they're changing work? Is there some stuff early on that's really getting traction that stands out to you?

D (27:18)

Yeah, sure. Look, first, we are on a mission to transform every enterprise. We think every enterprise can be hundred x thousands x more productive in the future. So we are horizontal. We are building a platform that can create any type of AI employee to automate any complex workflow. However, to start with, we have seen there are more willingness to try some things than others. Customer support has been the first thing that a lot of customers have tried with AI. We are seeing hr, like internal employee kind of experience. Automating everything related to employee experience is something that we are seeing a lot of customers want to try out and make sense. Like internally at ema, we have no hr. We only use Emma.

C (28:12)

Wow.

D (28:14)

Yeah, we can, I mean a lot of new companies like ours could be a blueprint for the future because we only hire humans when AI can absolutely not do something by principle. I think finance, like we are seeing a lot of finance use cases complex like data processing, matching documents, accounts payable, procure to pay, order to cash kind of processes. Lot of work going on in transforming and making them more efficient. Think of even sales and marketing. We have a lot of customers using our tools for writing business proposals, responding to RFPs, building slide decks, analyzing contracts in the sales process, commercial contracts, and just speeding up the entire process. I think in the future there'll be no part of an enterprise that will not be touched by AI employees. It's natural, it will be part of, as I was saying, it will be so normalized. In fact you would be like just like mobile phone or anything else is so normalized. Right. It's part of our life, our day to day completely almost like extension of our physical existence. Now I think that's what is going to happen in next five to 10 years?

C (29:49)

Yeah, got it. Nice. So there's this huge debate online about whether AI is actually increasing the productivity of companies right now. And you've got some people who say it's making huge improvements and they cite, you know, specific examples of particular job categories or departments. But you get some other people, including like there's some Nobel prize winning economists who are just saying it is having no impact on productivity and it's not going to for the next 10 years. And that is very confusing to companies trying to figure out how, how heavily should they invest, should they push on AI projects, should they not push on AI projects, et cetera, et cetera. What do you say to an E suite person who's trying to think about AI related to productivity? And do you have any thoughts on how we reconcile these completely different perspectives that we're hearing?

D (30:48)

Yeah, I think it's possible to reconcile. And we work with large enterprises all day long and we see it also. See there is this aspect, as I was saying, some enterprises are just thinking, oh, I have this business process, let me put some AI here and there, see what happens. Likely what will happen is not much productivity gain. I'll give you an example. It's a very large company, large enterprise. We spoke to them and they're like, okay, can you help us automate all our travel and expense policy related processes? What's the process? Or you're a very large company, 200,000 people across the world, they send their expense reports and travel whatever receipts to nine different mailboxes. And we have humans just watch those mailboxes and then they process and so on. So what we want from you, this is the customer saying, create nine different agents that just man those mailboxes.

C (32:10)

Yep.

D (32:12)

So I'm like, but why nine mailboxes? Why not have one, one window for everything and then, oh, we don't know, some legacy reason and so on. My point is, look, you have to think of the, your current processes entirely differently. Not just inject AI in some places, but think of like this whole agentic transformation. You have to reimagine in a new world AI and human, how they will work together, not just mimic. So if you just create these nine different agents and then pretty much it's the same thing happening, you'll say, okay, I got like maybe 5% productivity improvement, which maybe doesn't even show up overall. Right. But if you transform the process itself, you will see 50%, 60%, 80% improvement. So I think the it will require Large scale re architecture of the organization. Yeah, right. And some organizations are have done it faster than others. Like some parts for example customer support because they had already outsourced a lot of customers to third parties. So it was easy for them to think oh I outsourced to other humans. So I re architected my business processes around that framework that some rules. So now it's just outsourced to AI. So it's kind of the same thing. Just instead of other humans, AI is doing it. So my processes are kind of fine tuned to that concept. But other parts of the organization have not done that for good reason because cannot outsource like critical business things through to, to other to outside companies. But now with AI they need to rethink a lot of this. So it will require so net net AI is having huge impact and I have so many case studies and examples in my head and we have published bunch of them with many analysts etc but it has to be done the right way. Right. And, and you have to think holistically about your company and, and how you organize going forward. It's not one off. Okay, I put I I of often see these news reports or these quotes, oh, I have employed 5,000 AI agents and I'm not seeing anything like first that's a wrong metric. What does it mean? I have 5,000 AI agents. I would think, oh I had 50 critical business processes, maybe hire to retire, order to cash, whatever. Right. And I have automated x percent of them with AI or re engineered with agentic transformation. Then I know they're doing the right thing. And then you, you will see the numbers on productivity actually go up. My prediction is there'll be a lot of confusion, lot of learning and you will think okay, nothing is happening for some time. And then suddenly you will see, oh, bunch of companies are have run out of the pack. So there like every, every industry, there'll be two, three companies who have really embraced AI and reimagined how they can operate their entire operating structure, et cetera. And they will move so far ahead of rest of the pack they will be untouchable. And maybe new entrants will come in who have embraced AI from the very beginning. And that's happening. You will see AI first companies in every industry who have no legacy. So then you don't have to re engineer, you just engineer it. Correct. Like a company like ours, like in a tech company. As I said, we didn't even think of hey, how do I set up a HR team? We said okay, how do I use AI do hr?

C (36:30)

Yep. What do I need from hr? How can I do it? Do I need to insert human beings? So it's kind of flipping the whole normal thought process on its head or maybe the old school thought process. Another subject you've raised a lot in your posts has been transparency. Talk to me about how that helps build trust because sounds like a lot of what we need to do is establish trust in where this technology is going. And are there particular issues that we need to be especially transparent about related to AI?

D (37:04)

Absolutely. Look, the sooner we start, start thinking of AI close to like how humans work, the better decisions we will make. How do humans work? When we hire someone new, we have a sort of a, you know, we give some work and we see how they are working. We see can we trust this person? If we trust, we give them more and so on. Right. It's not like you hire someone new and say, okay, go stand in front of the CEO and present. You will set that person for failure, set yourself up for failure. So this building of trust comes from consistency and transparency. Can this person do something consistently and is this person very transparent on what they are doing, how they are doing and so on? Right. Same principles apply with AI, right. So AI has to be transparent in terms of like explainability, you know, observability or whatever term you use. For example, at ema, everything we do we explain to our customers, our enterprise customers. This is why we made this decision, this is how we did it. Right. So you know why? You know, it's not arbitrary. There was some, some reasoning and then can it take feedback and adapt its behavior? Right. So same with humans, right. We'll trust more if they took feedback and adapt and then can it behave consistently like I gave you feedback and then it did it once and then didn't do it the same way. Right. I will lose trust.

C (38:54)

Right.

D (38:54)

So can it perform consistently? That's what we embrace, which is can it take feedback? Is there opportunity for human to take give feedback? And based on feedback, based on learning, can it perform consistently on the job?

C (39:12)

Nice. And that's part of the whole adapting to you rather than you adapting to the software that you were talking about earlier. I love that. Okay man. I feel like we've gotten some great action items for companies here. So I'm just looking at my notes. So there's a big one about current tech companies, pre 2020ish, something like that, that are tool oriented software companies or tech companies, they need to really be seriously, deeply thinking about do they need to refactor and restructure the stuff that they're offering because they're at risk of being obsolete. So that was one. Another one was for companies that are the users of this, getting ready for that agentic transformation. And I think you had a couple of nice ideas on how to approach that. Of do some experiments with maybe some low hanging fruit. Some areas where it's straightforward, could be customer support, could be hr, could be something else. But then also being very open to rethinking work processes over time and bringing the employees along, reskilling them and preparing them to be taking advantage of it. So maybe we're not huge fans of the CEOs who are going around saying, well, I'm going to fire half of you because of AI, but I don't know which half. We're going to figure that out. That does not sound like the right approach.

D (40:39)

I think that's absolutely the wrong approach. I think that kind of bravado may please the street.

C (40:46)

Yeah.

D (40:48)

Maybe short term stock price bump or whatever. But that's not the effective approach. Look, your employees still are your best asset. Today you're human employees, tomorrow you're human. Plus AI employees are your best asset.

C (41:05)

Yep. And the AI is not so much replacing your employees as making your employee investments more productive, more valuable. Yeah.

D (41:14)

I mean imagine even every. This is the bizarre part, right. Every company will say, oh, you know, I need to get AI like you know, I need to, I'll get AI and let go of my engineers. But on the other side, I need to also hire engineers. Right? Yeah. Hiring has not stopped. So. Which is good thing. My point is your current employees can be twice more effective.

C (41:49)

Yeah.

D (41:50)

Thrice more effective if they use AI the right way. And it's almost like you have an infinite supply of h new employees without hiring a. A single one. Right. Because your current employees have the most context. Unless they are not performing or whatever. But let's say you're good employees. Yeah, Good employees. They have the more context. They don't need any more onboarding. They already know what they're doing. If you made them more efficient, you could grow your business much faster than waiting and hiring and onboarding a new employee. It takes six months, Right. It takes few months to hire someone, it takes another few months to train that person. It's at least six months or nine months in any role to start, to finish, to get like someone new, get him, get someone up to speed. Yeah. So that's a way to think about it. You want to grow your business 10%, 20% right. It's the like, AI will actually help you. Not just cutting cost, but growing your business because your most high performing employees, by the way, that's another thing we look at.

C (43:07)

Yeah.

D (43:08)

We look at in our company how much AI is being used by our employees and who is using. And this is like we just collected, started collecting the data and guess what? Our most highest performing employees also use AI. The highest.

C (43:28)

Okay, interesting.

D (43:30)

And we published that internally. Look, you want to get promoted? You want to ask, oh, how can I be higher performing? Use more AI we have, given the tools, we're not using them.

C (43:41)

Yep. Wow. So even within your company that's AI centric, you're still working on getting them to really adopt it thoroughly?

D (43:49)

Absolutely. I think it. Look, as humans, we always have some kind of. How do I put it? We have some hubris or some notion, oh, we are better than others. So sometimes I'll see. I can write code. I don't need help. And I'm like, no, you don't need help. I know that. But you can write like 10x more code. Why do I want to write code? That is mundane. Right? The most innovative, most sophisticated code. Right, Same thing. Oh, I can write like a blog on my own. I'm sure. But you could write five blogs and make it your own with help of AI. Why would you not do that?

C (44:35)

Yep, yep. So the biggest change we need to make is in ourselves and our own expectations.

D (44:41)

I mean, our own expectations of what we can do and also being humble, that, look, we have a partner now. We have. I mean, I remember like growing up. I'll see. And this may sound like, you know, ancient time. Some people will be like, oh, I don't need a calculator. I can calculate. Yes, but why?

C (45:07)

Yeah, yep, Right.

D (45:10)

Same thing. Oh, I don't need a computer because I can write really fast. But why?

C (45:17)

Yeah, yep, yep. I'm with you. Okay. This is, man, what a fun time to be in the tech industry. Or actually, what? It's a fun time for everybody because it's not just the tech industry that gets to play with this. So great stuff. Thank you very much. Is there anything I didn't ask you that you want to be sure? Like a message you want to deliver or a question I should have asked you or something like that.

D (45:40)

Yeah. I think maybe going back to the question on what enterprises should do today, I think, look, all said and done, they need to be aware that there are risks involved. So choosing the right AI is very important. Is it responsible? Do you have the right safeguards? Do you have the right security? Where is your data going? Is the tool you are using enterprise ready? Very important to look at. But those are part of the par for the course, part of the it should not stop you from using AI. You just need the right guardrails instituted. But get in there, get started. Because if you are not getting started, your competitor is. Yeah, what we are seeing is in every industry, as I said, few companies will just ramp up so fast because they're using AI and because they're thinking about it holistically that others will not be able to catch up.

C (46:57)

So one last thing. How does somebody learn more about you, your thought leadership, your work and Emma?

D (47:05)

Just go to Ema Co Ema Co and learn all about Emma and we have lots of good resources for everyone to think about how they can transform their business with AI agents.

C (47:19)

Excellent. Well, thank you so much. Really appreciate your time. Sergeant, Great talking with you and good luck with your adventure in the future.

D (47:28)

Thank you and really enjoyed the conversation.

C (47:31)

Likewise. Take care.

B (47:33)

Want to keep the conversation going? You can find the show notes@usertesting.com podcast if you haven't already. Don't forget to follow us on Apple Podcasts, Spotify, Overcast, or Google Play so you never miss an episode. And if you enjoyed today's show, please share it with a friend or leave us a rating and review on Apple Podcasts. And until next time, this is Insights Unlocked, an original podcast from User Testing.