Are Your AI Tools Lying to You?

Welcome, dear listeners, to the Speak and Flow podcast, where we dive into unique experiences to help you and your team achieve maximum potential and flow, even when the stakes are high. Today, I have an exciting topic with an industry leader who's gonna help us guide,

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Melinda Lee: are insights, especially with a lot of data, scattered data everywhere, and so this is a fascinating topic, especially with AI on the forefront. He is a design leader, he's been in the industry for two decades or more. He's worked with Fortune 500 companies.

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Melinda Lee: large, and even startups, and what he does do well is also he has a people-centric approach, and so I'm so glad that he's here. He's founder of Sepian AI, Jan Greenstein.

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Melinda Lee: Jan! Hello!

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Yan Grinshtein: Thank you. Hi.

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Melinda Lee: Welcome!

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Yan Grinshtein: Thank you for having me.

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Melinda Lee: I'm excited about this topic. I'm… before we dive in, I'm actually just interested to learn, like, how did you even get into design in the first place?

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Melinda Lee: What do you love about it?

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Yan Grinshtein: As I mentioned to you earlier, I made a decision to… I fell in love with web design, as I said.

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Yan Grinshtein: Probably at the worst time you can fall in love with a technology or an industry that it seems to be collapsing, because it was during the dot-com collapse, late 90s, early 2000s.

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Yan Grinshtein: And for me, as somebody who came from arts, I used to be a painter many, many years ago, got my associate's degree in graphic design, again, many, many years ago.

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Yan Grinshtein: And…

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Yan Grinshtein: seeing how I could do this digitally now, not manually, just felt like a… like a revelation, if you will, to me. And then so I… you know, I didn't care if I'm gonna make money in this, I didn't care if I'll ever have a job in that, I just wanted to design websites, and so…

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Yan Grinshtein: I kind of went there, and eventually I dropped out of college, doing my BA in Architecture Interior Design, and pursued becoming a web designer, which most people told me that I was an idiot.

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Yan Grinshtein: But… I… I didn't care. Just loved it.

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Melinda Lee: I love it, and you're the type of person I admire, and I think we need more people like that to take a step out to just falling in love with what you do, and then I feel like the money comes, or the success comes, and the fulfillment comes.

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Melinda Lee: And so, how do you feel about now that you, because we've seen so much growth in terms of designing, and now you're taking designing even to another level with AI.

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Melinda Lee: Through Sepian. You know, what is that like for you?

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Yan Grinshtein: There are a lot of memes about people like myself, right? The ones who grew up with, no internet or no smartphones, and so it's almost like seeing this history kind of unveiling right in front of your eyes, right? Like, I… I started in the design world, as I said.

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Yan Grinshtein: My first job as a designer, I was essentially designing menus and buttons. That was literally my job day in and day out.

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Yan Grinshtein: And then, a couple years later, I started actually designing websites, I became an award-winning designer, and then…

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Yan Grinshtein: Build an incredible career across, 5 different countries and 3 different continents.

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Yan Grinshtein: in that industry, working at, the agencies, and then working in the tech industry. And so.

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Yan Grinshtein: For me, it's almost like a natural transition, where we go from designing things.

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Yan Grinshtein: To almost thinking about the arts and design, and that actually happens in front of our eyes.

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Yan Grinshtein: Now, I don't foresee that the industry, if you will, the designers will disappear, but will definitely evolve.

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Melinda Lee: Yeah.

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Melinda Lee: And that's great, because now you can be a true designer and save more time in doing the things that you love.

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Melinda Lee: Versus all the manual things that you don't, as designers, love as and enjoy as much.

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Melinda Lee: Hopefully, right?

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Yan Grinshtein: Yes, yes, there's a lot of… there's a lot of things now that you can do that you… you don't have to do the manual work or.

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Melinda Lee: Right.

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Yan Grinshtein: The tedious work that typically people don't like.

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Melinda Lee: But you have to… I mean, for you, I think that that gives you a good blend as a leader, because you have all that background, and you have all that knowledge of doing it, and you also have the design in your eye, but you also have a great communication, you know how to translate complex topics into someone like me that can understand.

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Melinda Lee: And so, kudos to you. And so now, what you're seeing in businesses and organizations,

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Melinda Lee: a lot of data, scattered data all over the place, and… and for me.

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Melinda Lee: As a leader, we're constantly needing to make decisions.

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Melinda Lee: important decisions. And, we might not have all the right information, data scattered everywhere, and for me, that's a high-stakes moment, and we have to continue the path of growth as a company leader. Like, how do we do all of that

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Melinda Lee: it feels like everything is so important and urgent, and how… and so people… I don't know, can you share a story about an organization who has made a decision without all the right information, or scattered data, and the consequences of that?

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Yan Grinshtein: Sure, before I do that, this year, Atlassian put out a report, I think they're called the State of Work Report 2025, and they mentioned something interesting when they said that

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Yan Grinshtein: Employees of organizations so they have more

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Yan Grinshtein: More data than they ever had before.

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Yan Grinshtein: But they are… Less informed than ever before.

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Melinda Lee: Yeah, wow.

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Yan Grinshtein: Now, the reasoning for that is…

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Yan Grinshtein: We do have ample amounts of data, information, data.

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Yan Grinshtein: Stories, documentation, you name it, everywhere. And there's mountains of it.

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Yan Grinshtein: The problem, typically, that happens is there's not enough time, there's not enough resources, there's not enough knowledge about all of the information that exists within the organization.

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Yan Grinshtein: In recent research that I did, I discovered that, on average, Fortune 500 organizations deploy anywhere between 270 to 300

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Yan Grinshtein: applications internally.

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Yan Grinshtein: So, each different division has its own set of tools that they have their own data that lives there.

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Yan Grinshtein: Other parts of the organization typically have no preview to that, or they don't know if this thing even exists.

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Yan Grinshtein: And so in a lot of cases, when top leadership makes decisions, when they make these decisions, they typically rely on information that they get from somebody lower than them.

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Yan Grinshtein: So, think about the CEO gets more information from their VPs. VPs get information from their directors, directors get information from their managers, and the list goes on. There's almost like a chain of

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Yan Grinshtein: Information flow, if you will, within the organizations.

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Yan Grinshtein: Now.

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Yan Grinshtein: The larger you become as an organization, the difficult it is to be connected to the lower level, or to the…

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Yan Grinshtein: raw data, if you will. By the time it gets to you, it got through probably a host of.

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Melinda Lee: Synthesis, and a host of…

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Yan Grinshtein: Cleaning, and a host of biases, and a host of…

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Yan Grinshtein: I want this feature to exist, so I'm gonna…

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Yan Grinshtein: tweak this data or this information to be a bit more towards what feature I want or think we should have. So by the time you get to a point of the CEO or somebody at the top making the decision of what

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Yan Grinshtein: They should do on the product.

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Yan Grinshtein: Oftentimes, they might be misled with information that is not fully accurate.

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Yan Grinshtein: fully, there. A case in point that you and I, touched upon.

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Yan Grinshtein: right before getting on this recording, was the Sonos case. The Sonos case is fascinating to me personally, because

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Yan Grinshtein: Everything seemed to be super amazing happening to the company.

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Yan Grinshtein: I love the brand, I love the company. Granted, I don't have their speakers, because I love Sony, and I love Pioneer, and I love other brands.

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Yan Grinshtein: But I still think they're an incredible brand, they have an incredible product.

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Yan Grinshtein: And they have a… almost like a cult following of people.

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Melinda Lee: Yup!

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Yan Grinshtein: off their products and buy their products, right?

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Yan Grinshtein: But something happened early this year when I saw on the news that Sonos is imploding. CEO stepped down, dozens of people are being fired.

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Yan Grinshtein: They're reporting they're going into $20, $30 million damage control, reporting that they're gonna miss their revenue by almost $200 million this year.

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Yan Grinshtein: I got fascinated. I'm like, why? What the hell happened?

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Melinda Lee: Right, so quickly.

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Yan Grinshtein: Yes, it just… it felt like it was an immediate thing that happened, but…

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Yan Grinshtein: doing more research and talking to some people that used to work at Sonos, I discovered that

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Yan Grinshtein: There is a very specific process of that chain of information, if you will, that flows all the way to the top.

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Yan Grinshtein: And…

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Yan Grinshtein: I am pretty sure, again, I never worked at Stonos, I don't know their internal processes, just from the conversations that I've had.

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Yan Grinshtein: But I'm pretty sure that by the time the information got to the top leadership, it was so distilled.

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Yan Grinshtein: That some of the flaws and issues with their mobile application they were about to relaunch might not have been on the top subject conversation.

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Melinda Lee: And so, they made a critical decision, as leadership typically does. Right.

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Yan Grinshtein: To launch the application, even though it had some issues.

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Yan Grinshtein: But because they did that, Their stock started to collapse.

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Yan Grinshtein: A rage among their customers online.

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Yan Grinshtein: CEO had to resign, and just… it just led to so much problems that, in my opinion.

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Yan Grinshtein: Could have been avoided.

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Yan Grinshtein: If the top leadership Or somebody in that entire chain of process.

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Yan Grinshtein: Had an option to have an entire full view

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Yan Grinshtein: Of what is happening across the entire organization, and all of their… and their entire product customer base.

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Melinda Lee: Wow.

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Melinda Lee: That is amazing. And that's, like, only… they're cheap… I don't know, they're probably getting information, like you said, down one chain of command?

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Melinda Lee: What if, like you mentioned, like, where there's more information across other verticals?

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Yan Grinshtein: Yes, if you think about data within the organization, when you make decisions, right?

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Yan Grinshtein: Typically, You make decisions on a very specific piece of information that you have at your quote-unquote fingertips.

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Yan Grinshtein: That information sometimes, or oftentimes, might not include other pieces of data that you have in your organization, just because…

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Yan Grinshtein: It's not even being considered.

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Yan Grinshtein: Such as, if I'm making a decision, and…

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Yan Grinshtein: I would really love if your audience, or whoever is watching this podcast, will reach out to me on LinkedIn or somewhere else and tell me I am absolutely wrong here.

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Yan Grinshtein: Please do that.

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Melinda Lee: Challenge.

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Yan Grinshtein: beg you.

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Yan Grinshtein: I challenge you.

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Yan Grinshtein: Here's… here's what I see it.

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Yan Grinshtein: If a design team is making a decision on the product, so product team, which consists to be of typically a designer or a design team, product managers, maybe customer experience, maybe engineering.

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Yan Grinshtein: When that product team is making a decision of which feature or function or anything to be done on their product.

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Yan Grinshtein: In the next iteration, or next…

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Yan Grinshtein: Release cycle or whatnot in their product.

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Yan Grinshtein: When they make the decision, I am pretty certain, almost to… I wouldn't say 100%, but 99.9%,

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Yan Grinshtein: That they do not include data that comes from their sales team.

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Yan Grinshtein: Data does not include that it's coming from their, maybe, customer support, but not customer support person.

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Yan Grinshtein: but actual tickets.

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Melinda Lee: Hmm.

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Yan Grinshtein: I'm pretty certain, I can bet that design teams do not read actual support tickets, or actual sales motions and their conversations with potential customers.

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Yan Grinshtein: And what is happening there. Or they do not have information from the top leadership, stakeholders, their visions, and whatnot that happens instantly, not when they care about it at all hands of the company once a quarter, or even once a month.

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Melinda Lee: Right. And so…

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Yan Grinshtein: A lot of these pieces of information are incredibly critical.

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Melinda Lee: They're so critical, but there's so much, there's so much of it, and it's scattered all over the place.

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Yan Grinshtein: Yes.

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Melinda Lee: Yeah.

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Yan Grinshtein: All the data is… data lives everywhere, and…

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Yan Grinshtein: when I used to work at different companies, I found that about almost 40-50% of data lives dormant somewhere in Google Docs, in Dropboxes.

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Yan Grinshtein: folders, or whatnot. And it's not something that's being flagged, read, understood, analyzed, or being part of the bigger picture analysis.

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Melinda Lee: Yeah, well, and then first of all, it's scattered all over the place, and then once you…

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Melinda Lee: if you do have the time to put it together, like, you have to create… it takes a long time to put the dashboard, you know, how do you synthesize it and put it all into information that is useful? And then it's constantly changing, because now that you put the data together for this week, it's gonna change next week, and then the next month, and it's like… and then… ugh, it's crazy.

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Yan Grinshtein: There's no end.

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Melinda Lee: There's no end.

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Yan Grinshtein: Data is a living organism.

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Melinda Lee: Yeah, yes, that's a good way to say it. That's a good way to say it.

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Yan Grinshtein: Yes.

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Melinda Lee: Yeah, it is.

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Melinda Lee: It is, and so no wonder why, when he made that decision, the… too bad… I feel… yeah, that's so sad that he has to step down.

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Melinda Lee: Because he didn't have all the right information.

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Yan Grinshtein: Yeah, there is so many different cases in the industry about companies making decisions with

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Yan Grinshtein: A lack of correct data, or correct insights, or, or,

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Yan Grinshtein: incomplete insights. That's one of the… one of the issues that a lot of companies do have, is

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Yan Grinshtein: They don't have… full insights. They have a piece, and they make a decision on it.

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Yan Grinshtein: Granted.

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Yan Grinshtein: Our tech world, the software industry.

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Yan Grinshtein: have got used to iterative process, which is great. It should be that way.

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Yan Grinshtein: But it doesn't mean you have to shoot out products that are… Half baked.

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Yan Grinshtein: unfunctional, buggy, or whatnot. And that's something that I think

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Yan Grinshtein: Can be remedied when you make a better decision with

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Yan Grinshtein: A much better view of what is actually happening.

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Melinda Lee: Yeah, yeah, yeah. Yeah, and this is, important for me to notice, too, when it comes to communication, because when I'm training the teams, the leadership, and their teams, we talk about how to have a cohesive message. We talk about how to have one voice.

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Melinda Lee: you know, one call to action. But at the same time, we have to have the right data to feed. I mean, imagine if that one voice is the wrong voice, because we don't have all the data.

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Melinda Lee: And we don't have the right information from all across the different organizations and teams and things, so this is really helpful for me to learn, and support, you know, clients in understanding.

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Melinda Lee: And so, can you share with us, like, what are you excited about when you work with clients now? You're solving these… you have a proprietary process to help them to solve this issue. Can you tell us more?

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Yan Grinshtein: Sure, I don't necessarily work on a day-to-day basis with our customers, but…

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Yan Grinshtein: The system that we built is essentially built on top of a decision-making framework that I developed years ago.

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Yan Grinshtein: What we do is we connect into most of the organization's tooling, things like intercoms and Zendesks and Slacks and Notions of the world, and…

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Yan Grinshtein: Google Docs, I don't know, Google Analytics, it doesn't really matter.

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Yan Grinshtein: We integrate all these applications, and we essentially ingest all this data on the fly. We structure everything, we synthesize everything.

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Yan Grinshtein: And we derive from all that scattered data what exactly is happening with their users, essentially finding the user issues based on things like behavioral tagging systems, emotional tagging systems, and technical. And we unify everything, and then we show them the bigger picture of insights and recommendations on their products.

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Yan Grinshtein: Of what needs to be done, and it's all…

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Yan Grinshtein: instant. You don't necessarily need to wait for…

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Yan Grinshtein: an insight a week now. Right. We're essentially solving a problem that existed for the last 30-some years.

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Yan Grinshtein: that… It's exactly the same or slightly changed process of going from raw data to inside.

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Yan Grinshtein: Typically takes, and have been for the last so many years.

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Yan Grinshtein: Anywhere between 9 to 10 to 12 steps takes… well, today takes a week.

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Yan Grinshtein: It used to take about 2-3 weeks, or more. Today, with a lot of co-pilots and AI assistants and whatnot, in our research, we found that it takes about a week for a team, instead of 2-3 weeks.

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Yan Grinshtein: What we found is…

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Yan Grinshtein: We delivering this to our customers within 10 minutes, rather than a week, so there's no more need for

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Yan Grinshtein: waiting for some sort of data to come up, or some sort of insight to come up. Everything is a living organism, as I mentioned. Data is a living organism. It's constantly flowing. So whatever you synthesize today is no longer valid in 3 or 4 days.

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Melinda Lee: Right.

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Yan Grinshtein: Because something changed with your customer. Something changed with your industry. Something changed with your product.

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Melinda Lee: Great.

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Yan Grinshtein: So forth.

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Melinda Lee: Right, speak in flow. Give me the right information so I can speak it in flow. We gotta get the right intelligence, the right, data intelligence as a human, like, it was evolving, and then so I could speak it in flow. I love that. I love that, and

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Melinda Lee: And do you… can you share a client story that you've been able to support? Oh, you don't… you said you don't work with the clients too much.

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Melinda Lee: But what is a result that you're excited about?

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Yan Grinshtein: Well, one of the most exciting things that… I'll… I have two examples, actually.

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Melinda Lee: Okay.

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Yan Grinshtein: So, one example is one of our customers, we managed to discover… we managed to turn their 19,000 intercom conversations into just 300 insights within 10 minutes. Essentially helping them understand what is actually happening with their customers on the customer success side.

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Melinda Lee: Yeah.

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Yan Grinshtein: And why some of the customers are asking to deactivate their accounts.

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Melinda Lee: Mmm.

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Yan Grinshtein: And we help them understand that the platform, not we. When I say we, I'm talking about a platform, because we're not

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Yan Grinshtein: Manually doing this.

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Melinda Lee: Yeah, you're right, got it, got it.

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Yan Grinshtein: Yes, but when our platform helped them to understand that some of their customers are asking to deactivate their accounts.

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Yan Grinshtein: They're not asking deactivated accounts because they don't like the product, but because they could not get to a specific piece of information within their product.

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Yan Grinshtein: very simple, easy fix. So our platform essentially recommended what needs to be addressed. And so right now, our customer is essentially resolving the issues that will reduce the turn of their customers.

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Yan Grinshtein: Another example, what blew my mind, our own system that we built kind of surprised me in a way.

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Yan Grinshtein: Where we have an e-commerce customer that sells online clothing.

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Yan Grinshtein: it's very easy for a lot of platforms, for a lot of tools, to just say, your best reviewed product is X.

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Melinda Lee: Put it above the fold.

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Yan Grinshtein: Yeah. On your website, right? So more people can see, more people can buy.

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Yan Grinshtein: In our case, in our platform, what our AI system discovered was, and recommended, was, here's your highly reviewed products.

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Yan Grinshtein: Instead of saying to put it above the fold so it's very easy for everyone to see.

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Yan Grinshtein: It starts recommending actual, specific placements across their websites.

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Yan Grinshtein: Based on the navigational pattern of their websites.

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Yan Grinshtein: Where to place it so it's more visible as people navigate and shop for other products.

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Melinda Lee: Right.

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Yan Grinshtein: In addition to that.

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Yan Grinshtein: It found that the company has a product that a lot of people complain about it being wrinkly.

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Yan Grinshtein: very easily.

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Yan Grinshtein: And so our system recommended Different materials to use in their garment to solve that problem.

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Melinda Lee: Wow.

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Yan Grinshtein: Which is already branching out into a physical world of products, not even digital.

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Melinda Lee: Nice.

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Yan Grinshtein: Blew my mind.

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Melinda Lee: Wow.

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Melinda Lee: I love that. I love that. I love it. That's great. That's so fascinating. I mean, it's just so fascinating. It's evolving.

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Melinda Lee: So quickly.

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Melinda Lee: And like you said, it helps us, yeah, it helps us in so many ways that we wouldn't… like, we're just still scratching the surface, but…

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Yan Grinshtein: You know, we… we're in a study.

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Yan Grinshtein: We were just curious, because our platform saves people dozens of hours per week.

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Yan Grinshtein: We've calculated that on an average team of about 20 people, we can save about 41,000 hours a year.

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Melinda Lee: Wow.

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Yan Grinshtein: In their day-to-day work.

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Melinda Lee: Yeah. That needs to be done on data synthesis, analysis… Right.

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Yan Grinshtein: Right, derivative of insights and so forth.

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Melinda Lee: Right.

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Yan Grinshtein: We sat back, and I'm like, hold on a second.

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Yan Grinshtein: We're saving so much time to people.

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Yan Grinshtein: We're curious what they're gonna do with that time.

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Yan Grinshtein: We ran a study.

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Yan Grinshtein: And we found that most people responded that they will finally get to do strategic work and tasks.

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Melinda Lee: They've been thinking.

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Yan Grinshtein: What to do, and they never had the time to do.

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Melinda Lee: Yes.

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Melinda Lee: Yes, I can see that. I cannot see that, I can see that. Yeah.

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Yan Grinshtein: I mean, granted, about 20 or so percent of people said they'll take longer lunches.

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Melinda Lee: Which is delicious.

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Yan Grinshtein: Which is great.

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Melinda Lee: What's a…

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Yan Grinshtein: That's their mental health.

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Melinda Lee: Yes.

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Yan Grinshtein: And they can work and be more productive.

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Melinda Lee: Yes.

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Melinda Lee: Yes, it's fascinating. I love it. I love what you do. And so, if, if, what would you like to share

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Melinda Lee: with the audience, if, like, we can remember any part of this, what is that one leadership golden takeaway? Because I'm ending with that, because you talked about the people, right? What are people going to do with this new technology, and what else can they do? So what would you like to share about leadership with them?

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Yan Grinshtein: You know, I've been asked a lot, being a mentor in my… in the design industry.

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Yan Grinshtein: If AI will replace people.

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Melinda Lee: Right.

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Yan Grinshtein: And one of the things that I like to say is that

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Yan Grinshtein: AI is a tool, just like when Figma came around and people are…

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Yan Grinshtein: against it, because they still loved Photoshop.

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Yan Grinshtein: And then so forth.

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Yan Grinshtein: I think…

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Yan Grinshtein: AI will replace people that did not adapt AI, did not know how to use it. And so I think that it can…

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Yan Grinshtein: It can make design, products, engineering, and other teams more productive.

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Yan Grinshtein: more… Effective.

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Yan Grinshtein: and more efficient.

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Yan Grinshtein: And to more… to more design function that constantly battles of how do I prove our ROI?

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Melinda Lee: Right, right.

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Yan Grinshtein: This is how.

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Yan Grinshtein: Because AI can actually track your specific ROI, and how it's being delivered, and then communicate it back to the leadership teams.

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Yan Grinshtein: Of how effective you are.

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Melinda Lee: That's true. So, so they're able to be more strategic in their initiatives, and then hopefully take some lunch.

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Yan Grinshtein: Yes, longer lunch.

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Melinda Lee: There you go, not replace you. More strategic, longer lunches, some work with AI. Like, it's your partner.

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Yan Grinshtein: Yes, we don't replace people, we supercharge them.

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Melinda Lee: Love it, love it. Thank you so much, Jan. I had a really great time. I learned so much, I really appreciate your insights, and how can, teams and companies, organizations get ahold of you, if they wanted to learn more?

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Yan Grinshtein: LinkedIn, probably the best place. Our website is very simple, it's sepian.ai, Sepian with a C, C-E-P…

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Yan Grinshtein: I-E-N dot AI, or just find me on LinkedIn.

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Melinda Lee: Awesome.

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Melinda Lee: Beautiful. Thank you so much, Jan. I had a great time, and reach out. Thank you, audience, for being here. I trust you got your golden takeaway, and remember, anytime you have a chance to have a conversation, high-stakes moments, get the right information and data, maybe through Sepian AI,

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Melinda Lee: And, and remember, to make a positive difference in the world.

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Melinda Lee: Take care. Always. Much love. Bye-bye.