[AI Series] How AI Is Transforming Wealth Management With Sindhu Joseph

Sindhu Joseph is the Founder and CEO of CogniCor, an AI-driven digital assistant platform transforming customer engagement in financial services. She holds a PhD in Artificial Intelligence and has co-authored six US patents. Recognized among WealthManagement.com's Ten to Watch in 2023, Sindhu is a member of the Forbes Technology Council. Her global background spans India, Europe, and the USA, and she is passionate about democratizing access to wealth through AI. ​

Here’s a glimpse of what you’ll learn:

• [2:14] Sindhu Joseph explains how CogniCor helps financial advisors with AI-powered copilots

• [5:02] How AI frees up advisors from mundane tasks so they can focus on clients

• [8:21] Personalization in wealth management: remembering client details like their pet’s name

• [10:41] Reducing financial advisors’ prep time with AI — saving up to 3.5 hours per day

• [12:29] Real-time AI assistance: how it works for home office teams versus advisors

• [14:38] Compliance concerns with AI-driven client interactions and transcription

• [22:34] The surprising impact of exponentially increasing AI training data

• [24:02] Why financial advisors are historically slow to adopt new technology

• [33:46] Sindhu’s leadership style and how her global experiences shaped her vision
  

In this episode…

Many financial advisors struggle with time-consuming administrative tasks, leaving them with less time to focus on their clients and provide high-value advice. With only 30% of US households receiving financial guidance, how can technology help advisors scale their services and reach more people? Could artificial intelligence be the key to personalizing financial advice while improving efficiency?

Sindhu Joseph, an artificial intelligence expert and founder of a leading AI-driven financial technology company, believes AI can revolutionize wealth management. She explains how AI copilots help advisors by automating meeting preparation, summarizing client interactions, and providing proactive recommendations. These tools save advisors an average of 3.5 hours daily, allowing them to focus on building relationships rather than administrative tasks. Sindhu also discusses how AI enhances personalization by recalling essential client details and delivering real-time market insights, helping advisors stay ahead of client needs.

In this episode of The Customer Wins, Richard Walker interviews Sindhu Joseph, Founder and CEO of CogniCor, about how AI is transforming financial advising. Sindhu shares how AI reduces advisors’ workload, improves client relationships, and enhances operational efficiency. She also discusses compliance considerations, the future of AI in wealth management, and how AI’s rapid advancements are reshaping the industry.

Resources Mentioned in this episode

• Quik!

• Richard Walker on LinkedIn

• Sindhu Joseph on LinkedIn

• CogniCor

• "[AI Series] Harnessing AI for Strategic Leadership With Geoff Woods" on The Customer Wins

• "[AI Series] Tailoring Client Acquisition in Finance With AI With Farbod Nowzad" on The Customer Wins

• "[AI Series] Empowering RIAs for Personalized Customer Wins With Cormac Murphy" on The Customer Wins

Quotable Moments:

• "Exposure and access to wealth management are critical in understanding how to put money to work and create lasting wealth."

• "AI can help financial advisors scale and reach more people, democratizing access to crucial financial advice."

• "I was always fascinated by how human intelligence works, and AI became an instant love affair for me."

• "We thought more data would linearly increase performance, but it brought exponential results with AI."

• "AI is going to liberate us from being a cog in the wheel, giving us time back to rethink our priorities."

Action Steps:

1. Leverage AI to automate repetitive tasks: AI can free up financial advisors from mundane administrative work, allowing them to focus on building client relationships. 

2. Use AI-driven insights for personalization: AI can analyze historical client data to deliver personalized recommendations and offer thoughtful, tailored advice that strengthens long-term relationships.

3. Prepare for meetings with AI-generated summaries: This reduces preparation time while ensuring advisors enter meetings fully informed, improving efficiency and client satisfaction.

4. Stay compliant with AI usage guidelines: Using AI solutions that align with industry regulations ensures firms remain secure and avoid potential legal risks.

5. Embrace AI as a strategic partner, not a replacement: Viewing AI as a supportive tool rather than a threat helps advisors adapt and leverage technology for long-term success.

Sponsor for this episode...

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Episode Transcript:

Intro: 00:02

Welcome to The Customer Wins podcast, where business leaders discuss their secrets and techniques for helping their customers succeed and in turn, grow their business.

Richard Walker: 00:16

Hi, I'm Rich Walker, the host of The Customer Wins, where I talk to business leaders about how they help their customers win and how their focus on customer experience leads to growth. Today is a special episode in my series on artificial intelligence and today's guest is Doctor Sindhu Joseph, founder of CogniCor. Some of the past guests in this series have included Geoff Woods of the AI Driven Leader, Farbod Nowzad of Cashmere AI, and Cormac Murphy of CacheTech Advisors. And today's episode is brought to you by Quik!, the leader in enterprise forms processing. When your business relies upon processing forms, don't waste your team's valuable time manually reviewing the forms.

Instead, get quick using our Form Xtract API. Simply submit your completed forms and get back clean, context-rich data that reduces manual reviews to only one out of a thousand submissions. Visit Quickforms.com to get started. All right, my guest today, Doctor Sindhu Joseph, CEO and founder of wealth management AI startup CogniCor.

CogniCor Copilots are indispensable allies of growth-minded advisors and RA firms. CogniCor Copilot enables advisors and firms to personalize and scale effortlessly, navigating client review meetings, client servicing workflows, and discovering actual insights from client and advisor interactions. Dr. Joseph has a PhD in Artificial Intelligence and has co-authored over six patents, and was chosen as a top ten to watch in 2023 by WealthManagement.com. Sindhu, welcome to The Customer Wins.

Sindhu Joseph: 01:48

Thank you so much, Rich. Very excited to be here. Hello everyone.

Richard Walker: 01:52

I'm excited for you to be here and for our guest to hear from somebody like you. PhD patents and everything. So for those who haven't heard this podcast before, I talked with business leaders about what they're doing to help their customers win, how they built and deliver a great customer experience, and the challenges to growing their own company. Sindhu, let's understand your business a little bit better. How does your company help people?

Sindhu Joseph: 02:14

Yeah, so CogniCor is a, you know, really custom-built for the wealth industry. It is a copilot solution helping financial advisors who are the primary, you know, distributors for managing your wealth in terms of understanding their customers, their clients really well insights and also managing all of the operational efficiency aspects like, you know, managing the account maintenance activities like onboarding customers, account opening and things like that. So that's what CogniCor is focused on. But let me give you a kind of 360 level view of like, you know, what we are trying to achieve as in terms of the vision. So CogniCor was started because.

As a background, I have traveled pretty much across three continents. Extensively. And one of the things that I have seen across this, and I live in San Francisco today. Is the wealth gap between the rich and the poor. And when I studied this, one of the things that I realized is it's not because we don't work hard enough.

Like, you know, everybody works in 2 or 3 jobs, especially in the US, and they make a lot of money. But the reverse is not true for everyone, which is making money, putting money to work for us. And that is the gap between the wealthy and the poor. So I feel like, you know, the exposure and access to wealth, how to manage it is one of the critical factors in trying to understand how you can, you know, put money to work for you and create lasting wealth. So cognition is an attempt to scale financial advisors to reach more households because they only cover around 30% of the US population.

And so how can we reach more households, and how can we deliver advice, the crucial financial advice that is needed for young people for like, you know, people who are, you know, below the mass affluent, which is the financial advisors, like, you know, the threshold for, you know, managing wealth. So how can we expand to those populations? So I think AI is the answer to it. And really, AI can help financial advisors scale and reach more people. So that's really the vision of CogniCor.

So how can we democratize and, you know, democratize the access to wealth?

Richard Walker: 04:47

I love it. So is it safe to say we share a similar vision? If you give more time back to the advisor away from the mundane, trivial, repetitive tasks, they can actually do their best work. Go out and spend time with their clients. Is that what you're thinking is as well?

Sindhu Joseph: 05:02

Yeah, that is one definitely one aspect of it. Financial advisors also focus a lot on, you know, providing and they pride themselves in providing personalized care, personalized attention and personalized advice. So I think I can really fundamentally support that process. Like imagine, you know, financial advisor, you have a conversation with, you know, one of your clients a few months back and you talked about the client's pet named, you know, Dixie. And so in a few months later, like, you know, you are having that conversation and you want to ask about how is Dixie doing, but you don't remember the name of the pet.

So, you know, I can actually bring not only like, you know, what is the what are their investment priorities long term for the client. But also these kind of like, you know, very simple but very important relationship enabling kind of information in in the context of that conversation to the forefront. So you can actually have all of this personalization like in a more bonding created using I imagine this as kind of somebody is a personal assistant looking over your shoulder and prompting you the right way to kind of do things.

Richard Walker: 06:26

So I kind of think of this as if you if you ever see images of the president of the United States walking with his team and getting notes passed. Now, this is this is who you're meeting right now, do you think the copilot is going to work that way? Is it going to intuit, oh, you're having a conversation with this phone number, which is this account in your CRM which has these things? Hey, don't forget to ask about the dog. Or is it more the advisor has to say, what's the dog's name?

Or should I ask about how is it going to work?

Sindhu Joseph: 06:52

Yeah, yeah, it is more proactive like, you know. So for me, personalization means like, you know, understanding the, you know, having the knowledge of the customer. So we actually pore through a lot of historic information, all of the interaction data like, you know, meeting notes, email communications, phone transcripts, plus the, you know, whatever information, they're in the CRM. So we pore through every single bit of it and, you know, have a summary of the client information, which might include that you know the dog's name and enable it for the advisor in a proactive manner so that they don't have to, you know, remember to ask you this question, but it is there when they need it, when they are talking to that particular client. So delivering that in the context and knowing the, you know, information about the customer and knowing also the, you know, regulations and things like that.

So all of these three parts are part of the personalization. And I think that's how we should kind of remember or, you know, deliver personalization to clients.

Richard Walker: 07:57

Okay. I want to ask how this works, but I want to ask. My mind is jumping ahead like I'm imagining I have an earbud and now the computer is telling me, hey, they mentioned a 529 plan last time you talked. Find out if they did it or not, or ask them if they're ready to move forward. Like actually talking to you.

Kind of like professionals on stage. Have somebody talking to them in their background. Do you think that will happen or is it really just screen prompts?

Sindhu Joseph: 08:21

Yeah, I think it is more like teleprompter screen prompts. The reason is like, you know, if somebody is really talking on your like, you know, ears, it becomes distracting because you're having an engaging conversation. So we do like, you know, advisors can choose to do this, you know, 30 minutes in advance or like, you know, a day in advance to prepare for the meetings. And it doesn't need to be like, you know, associated with a meeting, like, you know, as you the great thing about this is like, you know, as you go into your office in the morning, you get a list of proactive recommendations saying that, you know, this is the market insights for today or for this month, and some of your customers are impacted out proportion way because of this market insights. Let's say like you know there is some Chinese tariffs that has been introduced and some of this portfolio is impacted.

And these are the clients that might, you know, feel bad about it or you want to kind of, you know, rebalance. So you know take a look at this. Now you can choose to kind of reach out to this client or send an email or like, you know, rebalance or do whatever those things. So it is more of a proactive like, you know, I don't need to keep all of this in mind. Like, you know, it will tell me when things happen, when you know clients are ready for distribution.

It would tell me that, okay, these two clients are like, you know, coming up for R&D in the next three months, you better talk to them. So. So that's kind of the like, you know, way that it should work. And highlighting the opportunities like, you know, there is a pie 29 plan set in place, but there is also a tax optimization opportunity if you contribute 3000 more to that plan. So that is something that we would kind of propose to you.

So it need not be like, you know, in real time in that meeting. But we would also do that during that meeting.

Richard Walker: 10:20

Okay. Two things. So first of all, I've been to a couple of roundtables recently, and the topic of how long it takes an advisor to prepare for a meeting was a big one, like hours and hours and hours of their week going into just prep. So are you foreseeing this as just shrinking that prep time down to nothing, or just a reading a brief versus having to go do the research?

Sindhu Joseph: 10:41

I think very much so. On average, we save customers 3.5 hours on a daily basis to both prep for the meetings, but also to prioritize because prioritization is one of the key aspects, like if you have 100 customers like, you know, do you want to keep all of this like, you know, in, in, on top of your mind or like, you know, let I do that for you and make sure that, you know, you are also delighting your customers by knowing that, okay, I was supposed to do that. And, you know, I'm doing that proactively and delighting my customers.

Richard Walker: 11:18

So are you sure I can't use it in my fintech? I'm not a financial advisor anymore. I mean, I want this unfortunately.

Sindhu Joseph: 11:25

This is like, you know, we can use our platform, you know, our platform to pretty much all of the use cases. But this is a custom-built workflows designed for financial advisors. So like, you know, you might want to think about switching your job to financial advising.

Richard Walker: 11:44

I used to be one.

Sindhu Joseph: 11:45

Of the you know, this is very exciting because one of the partners, you know, when I was presenting this tool, they said, like, you know, I wish I had this in my industry. Financial advisors seem to be extremely spoiled.

Richard Walker: 12:01

Ha ha. I mean, nowadays, the 20 years ago when I was an advisor, I would I would give up a left arm for this. This is incredible stuff. My other question I wanted to ask was, you're thinking about it as preparing you ahead of time. Do you foresee it being a real time prompt?

In my zoom meeting when I'm on call, seeing things kind of fill in like a chat window and flow and oh, they just said this. Let's bring that up. Do you see that? Do you see a real time interaction as well?

Sindhu Joseph: 12:29

Yeah, we do have that real-time interaction, not for advisors but for the Home Office team. So if an advisor is calling in, then like, you know, we have a real time transcription and prompts prompting, like, you know, if advisor comes up with a question, we have the response prompted to the agent so that they can, you know, share it with the advisor and also real time analysis of sentiment. And then like, you know, if it is really going bad, like, you know, we would show the sentiment going bad, bad worse and press the panic button. So like, you know, this is where you need to bring in your manager and things like that. So there is that happening internally for, you know, home offices for advisors very much.

The technology is there. I'm not sure like, you know, advisors are, you know, ready enough to have this handled in real time because for contact center agents, it's they're like in a job to kind of handle calls and, you know, ten other things at the same time. So they are experts in doing that for an advisor. You know, they are you know, relationship people. They want to kind of, you know, fully be engaged in that conversation.

So while we and they also need to like, you know, make sure the recommendations that we are providing is like, you know, they are ultimately fiduciary responsibilities belonging to them. So instead of blurting out, blurting out everything that you know the AI is saying, they probably want to do a kind of thought through process around it. So because of those reasons, we are not yet putting this in front of, you know, real-time, you know, advisor meetings. But it is very much possible we are doing that for the internal teams.

Richard Walker: 14:14

Well, I suppose you also face a compliance challenge, because if you're having an AI interact within a phone conversation or a zoom call video, you may be considered recording and recording laws vary by state and compliance department. Right. That's not a trivial thing to overcome, I would imagine. Have you had pushback from compliance on any of this?

Sindhu Joseph: 14:38

Yeah. Usually there is like, you know, the interesting thing is nobody knows for sure. Like, you know, what is the regulation going to be around? I recording calls. And so people tend to kind of be in all extremes like, you know, starting with on one side I don't want any I to sit in my calls to, I am recording the video and I can even go back to every single bit of that video to kind of, you know, know that what I'm talking to in that particular instance.

So nobody has a clear answer to it. We on being a little bit more conservative on the like, you know, compliance to comply with all the regulations. We don't usually record the audio we transcribe in real time. So the transcription is the only document that is kind of produced as an after-effect. And this one we would, you know, have stored in warm compliant storages and things like that to make sure that, you know, it is recorded.

It is a trail. So some advisers have concerns about having that, you know, trail being left behind. So, you know, if there is questions they can always go back to this transcript. So there is but I think it is kind of coming anyway. So you can't get around the fact that there is not there is going to be note takers, there is going to be transcriptions done.

So it is going to be the future. So be prepared.

Richard Walker: 16:07

So let's talk a little bit about adoption. But I want to start backwards a little further. You have a PhD in this area. I don't know very many people who have that. In fact you may be the only one I know who has that.

You were probably doing this when it wasn't cool. And so you suffered through the challenges of is this going to manifest into something or not? And so take us through kind of how you stayed involved through the hard times of what I represented versus what it was versus now, and where you see it going and why people are or are not adopting it. Can you give us some of that background?

Sindhu Joseph: 16:41

Yeah, definitely. So it kind of goes back to my childhood. Like, you know, it's kind of surprising to say that. But as a child, one of the things that I was very, very curious was how human intelligence works and how, you know, of course, the big questions on, you know, where we where did we come from, how all of these things works, how does the nature around us work? And these questions were very, very like, kind of very fascinating for me.

And some of my relatives used to say that this kid is philosophizing life too much at this stage. So. So that was.

Richard Walker: 17:21

Fun. Sindhu, stop thinking so much.

Sindhu Joseph: 17:25

Yeah. So I was always fascinated by that. So. But I left it at that. And when I was in college, I was introduced to a topic of artificial intelligence where you could, you know, create intelligence artificially, which was like, you know, kind of, you know, the my mind meet the persona that I really want to kind.

So it became an instant love affair for me. So after that, I have never looked back. I knew that this is what I wanted to do because, you know, it gives me a facility to kind of experiment with intelligence and create intelligence, if possible, so that we can understand our own intelligence better. So that was that was kind of my academic curiosity. It had nothing to do with, you know, I can change the world with it, but it was more like, you know, I am extremely fascinated with this field.

So, so I also I was quite lucky in my first job. I didn't have any AI training as such, but curiously, they were like, you know, starting a research lab. And I was one of the first interns to be hired in that lab. And their focus was AI. And so I got, you know, almost willingly or unknowingly dipped into I and I started working on it for, for around five years.

And I realized, okay, this is the field that I want to kind of deep dive into and then started my PhD. So, you know, I never thought when I was doing my PhD that I would use this title so much as, as I use it today. It was it was kind of like, you know, I'm doing something that is exciting for me, but like, you know, now to say that I am a PhD in artificial intelligence, it seems to give me that differentiating edge, I guess. Yeah, yeah. But yeah, it did go through a lot of troubling times.

Like, you know, we were ashamed to say that, you know, we are working on AI. So we would like, you know, mumble and say, okay, we are working with AI and, and because, because everybody is, you know, in their mind was thinking that it's a failed technology and it doesn't work. I was more working on a concept called multi-agent systems, where like, you know, multiple autonomous agents can collaborate and accomplish a task. And there were, you know, groups and organizations and societies of norms that was constructed around it. So all that was very fascinating, but nobody adopted it in real life.

And now there is this agentic framework that is, you know, very like, you know, kind of a fancy topic. The, the next hyped thing. We worked on it I think ten years back. So, so I think it's all coming back. And it's very exciting to see that all coming around.

But the biggest surprise, even for me as a researcher, was when we were working on machine learning algorithms like convolutional neural networks and Bert algorithms and so on. We kind of never had a sense that it always had trouble in terms of like, you know, going beyond certain accuracies. And we never thought that if you exponentially increase the data that is, you know, used to train these algorithms, the results would be dramatically different. So.

Richard Walker: 20:59

So wait, wait, you're saying you didn't anticipate that more data would make it better?

Sindhu Joseph: 21:04

We did anticipate a linear increase in performance.

Richard Walker: 21:10

Like so but not exponential increase.

Sindhu Joseph: 21:12

Yeah. So it was like you know, it is it is natural to think that the more data the better the performance. So that is kind of a linear kind of increase. And so we thought okay we can increase by, you know, a billion parameters, a million parameters and things like that. But you know, we never thought that or focused on the fact that, okay, you add 1 billion or 1 trillion parameters, then the performance would be exponential that you would actually be able to understand, you know, natural language and predict it in a way that is LLMs are doing.

So that was a pleasant surprise and extremely, you know, exciting surprise for even personally for me, because I didn't I didn't think that, you know, that would happen. It was possible at that moment. But yeah.

Richard Walker: 22:02

It is remarkable. Just think about a Google search and how many websites and information has to go through to give you results in 0.13 seconds. And then you see these large language models come out and it gives you results super-fast. And it's incredible to me how much it can process. And I'm thinking I understand hardware and software to a degree.

It's super impressive that it works. And I have built my own software that I thought it's going to be too slow and it comes out and it works. It's fast enough. Keep going, keep going. This is fascinating.

Sindhu Joseph: 22:34

Yeah. So, like, you know, even as an engineer, like, you know, you would kind of open AI and, you know, I guess, you know, part of the credit goes to Bert, algorithms from Google. So they all like, you know, really anticipated that, you know, really exponential increase in data could really bring exponential results. So amazing that they had the capacity to do that. And you know, they went ahead and did.

And the results are, you know, just amazing from a predictability standpoint. Like, you know, large language models are language prediction models where you are actually predicting what the, you know, most probable next word or next sentence that is going to be. But the ability to do that is, is can really change industries the way we live. It's amazing to see that.

Richard Walker: 23:29

Yeah. So I have been at a roundtable about I, and I saw an experience that most of the people who went there, their leaders in our industry, didn't actually play with AI themselves. They weren't really personally adopting it. They wanted their companies to. They talked about how their companies were going to adopt it or make use of it.

What do you think that is? I mean, is that a human thing that we just don't have the time or the interest, or we don't think we should dabble with others? Should. Why is it taking a while for the adoption to happen?

Sindhu Joseph: 24:02

I think it is. Accessibility is one big, you know, parameter for adoption. But given the ChatGPT is available and accessible to pretty much everyone, I'm not seeing why adoption is a big issue. You should be adopting at least the ChatGPT version of it. You know, I was surprised to see that, like, you know, when I talked to broker dealers, RIAs, they used to tell me that financial advisers are the last people to adopt anything new.

And we did experience that personally as well when we were trying to sell our products, like, you know, nobody had heard of it. Like, you know. Oh, fascinating. Like tell us when it comes out, we would say, okay, this is here working for others like you want to use it. They wouldn't believe that it exists.

But when ChatGPT came out, there were a big section of the community that really went ahead and then, you know, started adopting AI and the trend reversed, like, you know, firms used to push this technology down the throat of financial advisors. Then they started adopting all kinds of things like, you know, ChatGPT for client insights and putting all of those, you know, sensitive information to ChatGPT and, you know, getting results. Then they had to in the firms have had to say like, wait, don't yet adopt this. Like, you know, we are, you know, going to put in place, you know, regulated proper technologies for you to use. So there were cry from the advisors saying that we want these like we want meeting note takers.

We are going to use zoom and things like that. So we saw a reversal of trend in one of the toughest adoption groups. But I do understand, like, you know, in a CEO's life or like, you know, a big organization, the CXO level people, they have their priorities set aside somewhere else. But even in their jobs in in my own job, like, you know, I would say I was able to increase my performance by 50% just using ChatGPT by my side. Like, you know, if.

Richard Walker: 26:16

I went through a challenge with e-signature, I started showing e-signature to customers and firms in 2003. Yeah. And to me it was like, this is so obvious, this is so necessary. And everybody's like, wow, that's so great. Not going to buy it.

Wait, Why? I credit the adoption of e-signature to grocery stores because when the signing tablets started showing up at the checkout stand and we all started signing for our own personal interest, our own personal consumer behavior and benefit, it became standard and adopted. And then UPS started showing up at our door here, sign our little tablet and you got more of that consumer engagement. And so what I'm saying is the leaders of these big companies are consumers just like you and me. And it's when they consume it personally.

And this is why ChatGPT made such an impact, because now it is accessible to consume. Personally, didn't have to have an API. You didn't have to think to go try it. You just everybody else is trying it, right. So I think the adoption curve is highly based on personal experience.

And consumerism makes that more easy to adopt. And that's why we're seeing more and more people come around to it. What do you think people should know about AI? That I mean, you're a PhD, like, you have studied this. You have lived breathed this for I don't know how many years.

A long time, I'm sure. Yeah. What do you think we should know that we don't know.

Sindhu Joseph: 27:38

That's a big, open-ended question.

Richard Walker: 27:40

Well, maybe. Maybe to the power of how we adapt it better maybe to that side of things. I don't know.

Sindhu Joseph: 27:47

Yeah. I would say like, in a couple of things. One is on the cautious side in terms of your data because, like, you know, AI is also capturing a lot of your data. So like, you know, how do you make sure that, you know, if you are using a free version of ChatGPT, then, like, you know, be sure that you know all of what you are typing into. ChatGPT will be used to train the ChatGPT algorithms, which means that in the next iteration, your data would be embedded into the ChatGPT knowledge or brain, and it will come out for all of the people to use.

So that is one thing that you know how when you interact with the different AI tools, you need to know, like you know how much you are in control of your data. Because if you have. If you want to have control over your data, you need to be conscious about what tools you are using. The second is when you are using AI that or consuming the information. You also should know that many of these are, you know, trained with the information that is available on the internet, which is, you know, biased sometimes, which is like so you would and you would see this trend more and more like, you know, there is a kind of AI generated junkyard that is going to be created and AI is going to learn from it.

And so it is going to be a, you know, loop that is getting repeated. So, so that is that is going to happen. But those cautious tails apart, I think there is an exciting time to be around. You know, as I was mentioning earlier, like, you know, I am I look at everything in life also from a like, you know, broader philosophical angle. So when I look at the human evolution, like, you know, we were foragers in the beginning, like, you know, kind of meant to walk around, hunt for food and have life in a very carefree style.

When the Industrial Revolution came around, we became a cog in the wheel. Like, you know, we thought that that's our identity. We have to go to work from 8 to 5 and kind of be that repetitive workforce mindlessly doing the same repetition of tasks. And majority of us are continuing to do that today. I feel like, you know, AI is going to liberate us from that, that cog in the wheel.

So we are going to get our time back. And this will go through a lot of social disruption in the sense of like, you know, we may not have enough work or job or tasks to occupy us throughout the day and we would have time back in our hands. What do we do with that? Time is up to us. Like, you know, we can do a lot of things, exciting things.

So I, I think that is going to be the biggest next exciting thing that is going to happen, not just the technological evolution of AI, but the societal, the social aspects of like, you know, how AI is going to influence the way we human race live. And I feel it's a liberation for all of us, and I'm excited to see what's going to happen on that.

Richard Walker: 31:13

So two things. One, I want to clarify, you have absolutely have to be careful with what data you feed to an LLM like ChatGPT. But ChatGPT, if you pay for it, says they will not train their model with your data. So there are ways to make sure that your data is secure. Right.

But the second thing this notion that we're going to give time back, I think is a fascinating one. And I would love to give people their time back. But as a capitalist society, we're going to say we're paying for somebody to work 40 hours a week. If they can get all the job done in two hours, we'll give them another 38 hours of work, won't we? So how do we change that mindset to say we've made your job so easy, you only have to work 20 hours a week, but we'll pay you for 40.

That's going to be an interesting challenge to.

Sindhu Joseph: 31:57

Yeah, I don't think the companies need to pay 20 hours. Like, you know, they only need to pay that two hours that the person worked. But as a society, we need to decide, like, you know, how everybody is fed or, you know, there is some universal basic income concepts that are floating around. So you should we should all really think about how we can actually make sure the society's still functions. And also maybe like, you know, if we have more time in our hands, we can also utilize for great things also for, you know, really we have humans as a tendency to create problems for ourselves.

So like, you know, we can handle it in different ways. So like, you know, we need to think about it. But I think it is a it's a huge opportunity for us to rethink and realign our priorities in a way that we haven't done in, you know, a huge number of years.

Richard Walker: 32:54

Yeah I agree. Wow. Okay. I have to wrap this up because I want to I want to keep talking to you about this. Maybe I'll have to come on the show again next year.

So before I get to my last question, what is the best way for people to find and connect with you?

Sindhu Joseph: 33:07

So I'm on the only social media that I use is LinkedIn. So you can find me there on LinkedIn. You can also reach to my company website CogniCor.com so all the information is available. How to reach out.

If you want to kind of set up a demo that's available there as well. So, you know, I would look forward to, you know, getting some comments on this as well as, you know, people reaching out to get demos of the product as well.

Richard Walker: 33:36

Yeah. Cool. Okay, so here's my last question. Who has had the biggest impact on your leadership style and how you approach your role today?

Sindhu Joseph: 33:46

That's a tough question to answer. Yeah. So as I was mentioning, like, you know, I have traveled a lot in my, you know, life here. I was born and brought up in India, traveled across India for quite some, you know, pretty much all of the areas in India, as well as lived around ten years in Europe, traveled across Europe before coming to the US. I'm based in San Francisco around seven years now.

So I have seen one of the things that motivates me a lot is the wealth disparity, as I was mentioning, like, you know, the between the poor and the rich. So the reason I started this company was to give back in some ways to the society, like, you know, where I was nurtured, was given an opportunity. How can we give back to nature? I'm also very, very focused on vision style, leadership person like you know how and I'm very connected to nature because I grew up on a farm, you know, very well, you know, embedded into the nature. So I want to give back to nature.

So my leadership style is like, you know, bring people around this vision, motivate them and give them facilities and ownership to perform. So I would say kind of symmetrically opposite to an Elon Musk kind of style. I know that he's very successful. Like, you know, there are a lot of successful leaders like that. My style is like, you know, a very vision-oriented style.

Yeah, I think I learned that from my parents. Like, you know, they have been very innovative, very entrepreneurial, moved across different places, started some things on their own, and kind of showed me that style. So I'm immensely grateful to for them to think, for me to think out of the box and, you know, care for the fellow human beings and nature around us. So that's one shout-out that I want to really make sure that I, I place there, but also like, you know, there are a lot of shortcomings in my leadership style and as well as like, you know, how I kind of approach the business world. I'm not a business person, per se.

Came, you know, squarely from an academic perspective with a, you know, kind of a very strong willpower to kind of achieve some Significant things. So that means that, you know, I, I was someone who didn't, you know, make sure that I keep fostering relationships. And in business relationships are, you know, very important. So one of my mentors, Atul Kamra, he kind of forced me to keep track of relationships and make sure that, you know, you are like fostering relationships. I still find it really hard to do that, but I know that it's extremely important.

And I also want to, you know, kind of give a big shout out to him to in for guiding me in terms of like, you know, these are some of the things that you should keep in top of your mind, even if that's not your nature.

Richard Walker: 37:09

Yeah. I you know, one of the things I really appreciate about you is that you're looking at yourself with, with clear lenses and truth and recognizing weaknesses so that you can have others help you with those. And that's such a great I think that's such a strong leadership quality. And it's going to carry you forward. All right.

We got to wrap this up. I want to give a big thank you to Dr. Sindhu Joseph, founder of CogniCor, for being on this episode of The Customer Wins. Go check out Sindhu's website at CogniCor.com, and don't forget to check out Quik! at quikforms.com where we make processing forms easier. I hope you enjoyed this discussion. We'll click the like button, share this with someone, and subscribe to our channel for the future episodes of The Customer Wins.

Thank you so much for joining me today, Sindhu.

Sindhu Joseph: 37:55

Thank you so much for inviting me. It was a pleasure to chat with you.

Outro: 38:00

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