3,000 Tools and Enterprises Only Use 15% of Them
What if your company is sitting on a goldmine of data but only using a fraction of it to its full potential? In this episode, Sean talks with Saurabh Gupta, an AI and enterprise data expert who reveals that while businesses have access to roughly 3,000 tools, most enterprises are only leveraging around 15% of them. Saurabh explains why focusing on outcomes rather than activities is the key to cutt
Guest
Saurabh Gupta
Saurabh Gupta is an AI and enterprise data expert with over two and a half years of experience helping enterprises unlock the full potential of their data assets. He specializes in delivering outcome-focused AI solutions, consistently bringing results to clients in three to four weeks rather than the industry-standard six. Saurabh is known for cutting through AI hype by prioritizing measurable business outcomes over activities, and for helping organizations address critical challenges around data quality, data protection, and the underutilization of modern data tools.
Key Takeaways
- 1Stop selling features and activities — focus relentlessly on business outcomes. Customers are drowning in AI buzz and empty promises, so winning pitches center on the specific results you'll deliver, not the process you'll follow.
- 2Offer a short, low-risk proof-of-concept window (4–6 weeks) to break through enterprise skepticism. When a prospect has been stuck on a problem for over a year, a 4–6 week trial feels like almost no risk, making it far easier to get a foot in the door.
- 3Set internal decision checkpoints early — don't let projects drag on without clear progress signals. The first two weeks of an engagement should tell you whether you're heading toward success or failure, and teams should be empowered to escalate or pivot immediately rather than hiding problems until a deadline.
- 4Rather than complex guarantee structures, keep engagement costs low enough that walking away is a fair option for both sides. This builds trust and removes the adversarial dynamic that can damage long-term client relationships.
Key Terms Defined
New to some of the jargon in this episode? Here are plain-English definitions for the terms that came up.
- CRM (Customer Relationship Management)
- Software that tracks interactions with leads and customers throughout the sales process — storing contact info, deal stages, communication history, and pipeline forecasts.
- B2B Networking
- Business-to-Business networking refers to relationship-building activities between companies or professionals with the goal of generating referrals, partnerships, or new clients.
- Financials
- A collective term for a company's financial statements and data, including revenue, expenses, budgets, and profit/loss reports. These documents are used by leaders to make key business decisions.
- Agile
- A family of iterative software development approaches (including Scrum and Kanban) where teams work in short cycles, release frequently, and adapt based on feedback.
- Data Quality
- The accuracy, completeness, consistency, and reliability of data used in business decisions and AI models — poor data quality leads to "garbage in, garbage out" results.
Chapters
Full Transcript
Sean Weisbrot: What's the hardest thing about what you do?
Saurabh Gupta: The hardest thing, Sean, is
Saurabh Gupta: where we are at the stage to convince a customer that we have a solution which is better for them, and it will do
Saurabh Gupta: it will not only do get them results faster, but also will be a much more efficient bet for them in the long run.
Sean Weisbrot: How do you convince them of that?
Saurabh Gupta: And,
Saurabh Gupta: so this has been a big challenge for us, and,
Saurabh Gupta: over the course of last two two and a half years, we learned.
Saurabh Gupta: So initially, we used to go and we used to try to sell hard, and
Saurabh Gupta: we we were not very successful.
Saurabh Gupta: But what is working out for us now is
Saurabh Gupta: this whole AI buzz is under tremendous pressure.
Saurabh Gupta: There's lack of outcomes,
Saurabh Gupta: and our positioning and pitch to our customers is that if you have a problem which you are struggling with, we can solve it in four to six weeks.
Saurabh Gupta: Give it as a shot.
Saurabh Gupta: And, most of the time, our customers really, really get excited
Saurabh Gupta: or they give they get intrigued and they give us a chance because,
Saurabh Gupta: they have been struggling it with that problem for maybe a year, year and a half.
Saurabh Gupta: So six weeks or four weeks is not a big deal for them.
Saurabh Gupta: And they say, okay.
Saurabh Gupta: Let's give it a shot.
Saurabh Gupta: And, we have been mostly successful.
Saurabh Gupta: And, where we, really, really come out strong is
Saurabh Gupta: when we focus on outcomes rather than activities.
Saurabh Gupta: And most of
Saurabh Gupta: the challenge that industry is facing, especially on the consumer side, customers, is they just get drowned
Saurabh Gupta: in the bunch of activities that people keep talking about.
Saurabh Gupta: So So we just go after the outcomes.
Saurabh Gupta: We focus on the outcomes that really, really matter to the business, and
Saurabh Gupta: we try to beat our promise of four to six weeks.
Saurabh Gupta: Most of the time, we have done it within three to four.
Sean Weisbrot: Do you offer money back?
Sean Weisbrot: It's a 100% money back guarantee if you're not happy in four to six weeks.
Saurabh Gupta: So most of the time, like, the bet is very small.
Saurabh Gupta: So if the customer is not happy, we really don't want nobody.
Saurabh Gupta: So
Saurabh Gupta: then we'll be more than happy to go back and
Saurabh Gupta: give a better solution when we are ready if we fail
Saurabh Gupta: than trying to penalize the customer for not liking and still have to pay for us.
Sean Weisbrot: There's a lot of people if they if they,
Sean Weisbrot: either they offer, like, a thirty day money back guarantee these are, like, you know, certain kinds of businesses.
Sean Weisbrot: They'll say,
Sean Weisbrot: if you don't get these results in ninety days, then we'll work with you for free until we get you there.
Sean Weisbrot: Obviously, I know in your business, it's not easy to do something like that because it gets very expensive for you, but have you considered that?
Saurabh Gupta: So, Sean,
Saurabh Gupta: I really don't believe in that.
Saurabh Gupta: If we are not able to show like, a six week engagement,
Sean Weisbrot: Yeah.
Saurabh Gupta: our intention is always to deliver it in three to four weeks.
Saurabh Gupta: In two weeks, if we are not able to figure out if we are getting in the right direction or not, then there's a problem with us. And most of the places, the the first couple of weeks are very, very decisive.
Saurabh Gupta: They will tell whether we are going to get to the results or not.
Saurabh Gupta: And the the problem could be ours that we had we don't have, we misunderstood the problem.
Saurabh Gupta: We didn't don't have the skills to do it. On the other side, also, it's possible where
Saurabh Gupta: a very, very likelihood we didn't understand the complexity of the customer's landscape,
Saurabh Gupta: and that could be slowing us down.
Saurabh Gupta: So for me, first two weeks, very critical.
Saurabh Gupta: That should be the decision making point.
Saurabh Gupta: And
Saurabh Gupta: no one in the company is allowed to drag to six weeks
Saurabh Gupta: just to make a point that we are trying to do something.
Saurabh Gupta: No. And we walk out.
Saurabh Gupta: That's that's the most important thing.
Saurabh Gupta: We want
Saurabh Gupta: the most in inelastic asset that people have, all of us have, is time.
Saurabh Gupta: So we don't want to waste anyone's time on this.
Sean Weisbrot: Why should people care more about their data?
Saurabh Gupta: And
Saurabh Gupta: why should not people care about their data?
Saurabh Gupta: It's the most, most, most important asset.
Saurabh Gupta: Today, literally, if you don't protect your data, someone is going to abuse it.
Saurabh Gupta: It. Your data is going to be like, your personal data
Saurabh Gupta: is very important.
Saurabh Gupta: Your company data is equally important.
Saurabh Gupta: How you can leverage your data to get a bot better competitive edge,
Saurabh Gupta: increase the top line, reduce make your whole operations more efficient.
Saurabh Gupta: There's a lot can be done with data.
Saurabh Gupta: And what is also interesting that's happening is, like,
Saurabh Gupta: we talk a lot about our old internal data.
Saurabh Gupta: There's a lot of third party data also that comes in and starts influencing.
Saurabh Gupta: There are the time where I can give you an example from my past where
Saurabh Gupta: an organization where I worked with for for many years, they could save 14% of electricity bill just because they put sensors in there.
Saurabh Gupta: So suddenly, if you are, like, still and then not and working and thinking, suddenly, the lights will go up. So you have to move around a little to make sure the lights get on. So I
Saurabh Gupta: they were able to figure out, like, which offices that the ACs go at a certain rate,
Saurabh Gupta: which a offices the lights go off at a certain time where they couldn't even switch on the corridor lights if 90% of the people are not.
Saurabh Gupta: So I think that a lot can be done, not just from top line, but also from bottom line point of view.
Saurabh Gupta: And,
Saurabh Gupta: access to data and managing this data gives us multiple ways of sort of taking advantage.
Saurabh Gupta: And with that whole excitement around AI and how AI can be is being used, I think data becomes even more critical.
Saurabh Gupta: Where I see
Saurabh Gupta: an area which people have to really, really put in effort is making sure the data is high quality.
Saurabh Gupta: Those volumes of data can increase as much as possible.
Saurabh Gupta: The quality is bad, and you put
Saurabh Gupta: analytics on it or AI on top of it, you're not going to get good results.
Saurabh Gupta: And even if you
Saurabh Gupta: hope to get good results, you'll end up spending a lot of money on compute.
Saurabh Gupta: So
Saurabh Gupta: the overall foundation of data has to be done.
Saurabh Gupta: Right?
Saurabh Gupta: And, I I feel like,
Saurabh Gupta: we are just starting.
Saurabh Gupta: Every five years, I thought, like, we have reached a maturity in data, and suddenly you see another uptake.
Saurabh Gupta: So I I'll be surprised if this data revolution is gonna slow down.
Sean Weisbrot: So for the audience, an example of, like, bad data management is when you and your team are,
Sean Weisbrot: you know, viewing the pages of your application or your website, and yet you allow your IP addresses to be tracked.
Sean Weisbrot: And so you say, wow.
Sean Weisbrot: We have so many people looking at our website, but, actually, it's just you.
Sean Weisbrot: And so you've,
Sean Weisbrot: polluted your data with the wrong people's information.
Sean Weisbrot: And so you have to find a way to remove yourself from the analytics in order to make sure that you understand what people are actually doing when they're using your systems.
Saurabh Gupta: That's a good example.
Saurabh Gupta: I can give you another example of, actually, what is the cost of bad data to enterprises?
Sean Weisbrot: K.
Saurabh Gupta: So if you think about it, like, so when I was in IMF, International Monetary Fund, we collected data from 180 countries.
Saurabh Gupta: And this get data was across different domains, balance of payments, national accounts, etcetera.
Saurabh Gupta: And
Saurabh Gupta: collecting this data in different formats, different structures,
Saurabh Gupta: and it goes through a processing, checking, transformations, lot of value add,
Saurabh Gupta: and getting to a place where it is shared.
Saurabh Gupta: And there were occasions where
Saurabh Gupta: when people start consuming data, they realize the data has issues.
Saurabh Gupta: And there could be
Saurabh Gupta: small issues like fat finger, some number got deleted in Excel sheet, or a decimal got shifted.
Sean Weisbrot: Yes.
Saurabh Gupta: But getting it caught on the consumer side means imagine not only the exposure you get, but all the effort that goes back again to the collection side.
Saurabh Gupta: So
Saurabh Gupta: the reason I brought this up is there is a lot of talks about data quality.
Saurabh Gupta: And
Saurabh Gupta: if the same bad quality data goes, AI is not gonna fix it. No one can fix it. There is someone has to check it. So moving data quality to the left, to the source, was something which everyone talks about.
Saurabh Gupta: But is it really happening?
Saurabh Gupta: What does data quality mean actually?
Saurabh Gupta: It's these are all very, very comp get these things are getting more and more complicated.
Saurabh Gupta: One simple example could be is, like,
Saurabh Gupta: a population you gave an we were talking about, Portugal having 10% immigrants or a significant percentage immigrant.
Saurabh Gupta: If suddenly
Saurabh Gupta: that number gets messed up, like someone types in a wrong number and it becomes 20%.
Saurabh Gupta: There should be some checks and balances around it. And and and that
Saurabh Gupta: so how do you make sure that checks are there?
Saurabh Gupta: And maybe that number is also right.
Saurabh Gupta: So is there sufficient better data around it? Is there sufficient context around it provided so that data that looks bad has supporting
Saurabh Gupta: details that, no. This is not bad, and this is right because of that, because of this particular reason.
Saurabh Gupta: Suddenly, if a country allows a lot more immigration happening, you will see unusual spike.
Saurabh Gupta: Then is it supported by a metadata?
Saurabh Gupta: And is that metadata traveling with the data across from
Saurabh Gupta: validation to checks to transformation and all the way to consumption?
Saurabh Gupta: And not limited to just your website.
Saurabh Gupta: All aspects like, all the activations,
Saurabh Gupta: layers are getting the same input, whether it is through API, through analytics, whether the AI is getting the same.
Saurabh Gupta: So I think there's a bunch of,
Saurabh Gupta: complexity that is going to come in with increased volumes of data.
Saurabh Gupta: And,
Saurabh Gupta: a lot of it isn't getting tackled, but I think we'll keep getting surprises, and we have to be prepared for that.
Sean Weisbrot: How can people get closer to the source of truth for the data to minimize the that pollution?
Saurabh Gupta: So the best way to do it is get closer to the source.
Saurabh Gupta: So if you are connecting to, say, your Salesforce data, bringing it into a data lake, data warehouse,
Saurabh Gupta: some basic checks and balances should be put in white before even consuming it,
Saurabh Gupta: before even moving the data.
Saurabh Gupta: And if there are challenges and issues with the data, probably a call should be taken.
Saurabh Gupta: And, again, data quality is something which is very use case dependent.
Saurabh Gupta: In some places, you are okay tolerant with errors.
Saurabh Gupta: Some places, you don't have.
Saurabh Gupta: So,
Saurabh Gupta: getting as close as possible to the ingestion point,
Saurabh Gupta: check and validate the data is gonna be the key.
Sean Weisbrot: So, for example, like, right now, I get information from posthog, and I get information from, GA four, right, Google Analytics four, and I get information from CloudFlare.
Saurabh Gupta: Mhmm.
Sean Weisbrot: I don't know which one to trust because as far as I know, they're all the sources of truth, but they're all giving me different numbers.
Saurabh Gupta: So
Saurabh Gupta: so I'll I'll give you an example from a mesh point of view data mesh point of view.
Saurabh Gupta: Like, getting these data from different sources
Saurabh Gupta: and creating a single source of truth is a process.
Saurabh Gupta: So creating these
Saurabh Gupta: source aligned data products, one is coming from CloudFlare, one is coming from Google Analytics,
Saurabh Gupta: and having a bunch of routines run out to to validate which data is making sense and which is not,
Saurabh Gupta: and then bringing it together into one source.
Saurabh Gupta: I'll give you an example from, customer data.
Saurabh Gupta: Like, as like, in a bank, they have multiple customer datas.
Saurabh Gupta: Sean can have a mortgage account.
Saurabh Gupta: Sean can have a savings account.
Saurabh Gupta: And both the Sean's are not connected.
Saurabh Gupta: So
Saurabh Gupta: many of the banks are struggling with bringing a a customer three sixty view.
Saurabh Gupta: And most of them realize later on because,
Saurabh Gupta: the business units are trying to figure out how can they upsell, cross sell services and products to Sean.
Saurabh Gupta: But the same thing, if it's brought to the left before and a single source of truth is created with the with the customer data,
Saurabh Gupta: then they exactly know there's no point selling mortgage to Sean because he already has one.
Saurabh Gupta: So I I think it is needed, moving the data sources,
Saurabh Gupta: or the checks and balances as close as possible to the left side
Saurabh Gupta: is is going to be a critical part for, making our operations more agile.
Saurabh Gupta: And, like, to some extent,
Saurabh Gupta: some of the AI capabilities, agentic capabilities can help in identification that
Saurabh Gupta: Sean in this data source and that's the data source I'll say.
Saurabh Gupta: But it goes complex very fast because Sean,
Saurabh Gupta: who has addressed in Wyoming, or says Sean who has addressed in Portugal,
Saurabh Gupta: I don't think any AI will be able to connect that easily.
Sean Weisbrot: And there's not many people with my name, so I think my case is is unique.
Saurabh Gupta: But if think about it. If you in a place where your name is Sean w, And in another place, it is, Sean Weisbrot.
Saurabh Gupta: How will you connect those?
Saurabh Gupta: And there are a lot I I I'm sure there are plenty of Sean's.
Sean Weisbrot: Yeah.
Sean Weisbrot: I I think it depends on the context.
Sean Weisbrot: And then if you go to
Sean Weisbrot: Asian countries, they insist on using my middle name, where in Western countries,
Sean Weisbrot: they don't really care about it. It's not important.
Saurabh Gupta: Yeah.
Sean Weisbrot: So, like,
Sean Weisbrot: when I do business in Singapore, I have to sign or in Vietnam and China,
Sean Weisbrot: I had to sign my middle name on all documents or it wasn't considered complete.
Sean Weisbrot: But if I sign my middle name on a Western on a document in the West, nobody cares.
Saurabh Gupta: Wow. Okay.
Saurabh Gupta: Yeah.
Saurabh Gupta: Yeah.
Saurabh Gupta: Yeah.
Sean Weisbrot: But I've had issues where if I get a flight
Sean Weisbrot: and I don't have my middle name on the flight booking,
Sean Weisbrot: and then they see they go to check me in and they see my middle name on the passport, they'll say I'm not the same person.
Sean Weisbrot: Of
Saurabh Gupta: Yeah.
Saurabh Gupta: I mean, I think these challenges will continue, Sean.
Saurabh Gupta: Like, and,
Sean Weisbrot: course.
Saurabh Gupta: they are just gonna get more complicated.
Saurabh Gupta: Think about it like,
Saurabh Gupta: temperature data coming from two different sources.
Saurabh Gupta: One is in Fahrenheit, one is in century.
Sean Weisbrot: The one in the one in Celsius is obviously the correct one,
Saurabh Gupta: I know.
Sean Weisbrot: even though Americans would tell you that's not true.
Sean Weisbrot: Because
Saurabh Gupta: Slow.
Sean Weisbrot: how can water freeze at, what, 32 degrees?
Saurabh Gupta: Move it up.
Sean Weisbrot: It's it should freeze at zero and boil at a 100.
Saurabh Gupta: Yeah.
Saurabh Gupta: I agree with you.
Sean Weisbrot: Sorry, Americans,
Saurabh Gupta: I got
Sean Weisbrot: but you're wrong.
Saurabh Gupta: it took me a while to get used to the Fahrenheit.
Saurabh Gupta: I grew up in India, so everything was Celsius.
Sean Weisbrot: Yeah.
Sean Weisbrot: And the thing that that's funny to me is any American that goes into STEM learns the metric system.
Saurabh Gupta: Mhmm.
Sean Weisbrot: Why not just teach all Americans the metric system?
Saurabh Gupta: Yeah.
Sean Weisbrot: It's not a difficult thing to do.
Sean Weisbrot: Like, a few million people every year learn the metric system.
Sean Weisbrot: Like,
Saurabh Gupta: Yeah.
Sean Weisbrot: I don't think it's that complicated to do, folks.
Saurabh Gupta: Something to
Sean Weisbrot: And then people and and then people find it funny that as an American,
Saurabh Gupta: think about.
Sean Weisbrot: I understand the metric system
Sean Weisbrot: because, of course, I'm living outside The US. So, like, why do you know?
Sean Weisbrot: And, like, I I even even weirder and completely more off topic.
Sean Weisbrot: So I was in Lisbon, and a friend of mine was going to Riga.
Sean Weisbrot: And
Sean Weisbrot: I was like, oh, where are you headed?
Sean Weisbrot: She's like, oh, I'm going to Riga.
Sean Weisbrot: I'm like, why are you going to Latvia?
Sean Weisbrot: She's like, how do you know Latvia?
Sean Weisbrot: And I'm like, how do I not know Latvia?
Sean Weisbrot: She's like, you're American.
Sean Weisbrot: I go, that doesn't mean I don't know a European country.
Sean Weisbrot: She's like, yeah.
Sean Weisbrot: But you're American.
Sean Weisbrot: I'm like, so?
Sean Weisbrot: Like, sure.
Sean Weisbrot: Half of the Americans don't have a passport or probably could show you where The US is on a map, but I'm not one of those.
Sean Weisbrot: I I don't live there.
Saurabh Gupta: I'd Yeah.
Saurabh Gupta: Yeah.
Saurabh Gupta: Yeah.
Saurabh Gupta: I think that's a I mean, this is gonna change.
Sean Weisbrot: Is it though?
Sean Weisbrot: I feel like American education is really bad.
Saurabh Gupta: The education is but the influence is also there now.
Saurabh Gupta: Right?
Saurabh Gupta: If you think about it,
Saurabh Gupta: a lot a big part of America is made out of immigrants.
Sean Weisbrot: Okay.
Saurabh Gupta: And,
Sean Weisbrot: So well, that's going down a different path.
Sean Weisbrot: So you're saying the the immigrants are naturally going to make America smarter?
Saurabh Gupta: yeah.
Saurabh Gupta: Yeah.
Sean Weisbrot: No. Okay.
Sean Weisbrot: No. No. Let's no. That's this isn't the right place to have this conversation, but I understand what you're trying to do. Yeah.
Sean Weisbrot: We're we're gonna cut that, and we're gonna just keep moving forward.
Sean Weisbrot: I don't wanna get I don't wanna get political.
Saurabh Gupta: I'm not saying smart yeah.
Saurabh Gupta: Yeah.
Sean Weisbrot: Don't wanna get political.
Sean Weisbrot: So Adan Adan is my editor.
Sean Weisbrot: Adan, please cut the part about the political stuff because that gets really heated really fast.
Sean Weisbrot: Like, I'm I'm on your side, but, like, those people, I don't want those people to know that I exist.
Saurabh Gupta: I know.
Saurabh Gupta: And I'm also trying to be careful.
Sean Weisbrot: Yeah.
Sean Weisbrot: Because you wanna work with you wanna work with governments.
Sean Weisbrot: So you don't wanna piss off the white supremacist American government.
Sean Weisbrot: So, again, this is being this is being cut.
Sean Weisbrot: It's still being cut.
Sean Weisbrot: We're gonna okay.
Sean Weisbrot: We're cutting, and we're starting from here.
Saurabh Gupta: Yeah.
Sean Weisbrot: Okay.
Sean Weisbrot: How do you see
Sean Weisbrot: this industry evolving?
Sean Weisbrot: You said you feel like it's gonna become more complex, but do you have any specific thesis
Sean Weisbrot: or something that you're experiencing right now that's telling that's informing you of how it's evolving?
Saurabh Gupta: So I can give you multiple couple of reasons where I think,
Saurabh Gupta: there's gonna be a huge change in the industry.
Saurabh Gupta: So I wanna start with the modern data stack.
Saurabh Gupta: If you go to modern data stack, there are over 3,000 tools that exist for data ingestion, for data quality, orchestration.
Saurabh Gupta: And, like, there's no limit to it. BIAI, 3,000 plus fields.
Saurabh Gupta: And you go to any large enterprise, midsize enterprise, they would have had about
Saurabh Gupta: 10 to 15 of those to put together their data platform.
Saurabh Gupta: Now not only they have done this, they have invested in people's skills, the compute that goes with it. And a small change that comes in, they have to go and redo multiple places, changes and adjustments.
Saurabh Gupta: The cost that this comes with also is, like, someone like, they have also many of these enterprises or or several of them have partnership with SIs,
Saurabh Gupta: whom they are dependent on learning their operations.
Saurabh Gupta: And if you look into
Saurabh Gupta: a little bit more detail, what you will realize is
Saurabh Gupta: these 10 to 15 tools that every enterprise is paying for,
Saurabh Gupta: actually, they are not using more than 15 to 20% of the tools capability.
Saurabh Gupta: But these tools are very, very funk highly functional.
Saurabh Gupta: They have deep cap vertical capability that not every enterprise uses them.
Saurabh Gupta: So I think the first round of disruption and transformation that is going to happen is enterprises are going to look for
Saurabh Gupta: platforms and tools and products which can give end to end capability.
Saurabh Gupta: They don't need to go too deep, but it should be one.
Saurabh Gupta: And that's going to be a big, big shift because
Saurabh Gupta: it will reduce the cost of managing, cost of building.
Saurabh Gupta: And at the end of the day, it'll simplify
Saurabh Gupta: experimentation.
Saurabh Gupta: You don't need to touch 15 places to try something.
Saurabh Gupta: So I think that is one big shift going to happen.
Saurabh Gupta: AI is just going
Saurabh Gupta: to force it a lot more because AI needs access to data.
Saurabh Gupta: And every time there's a new change, new data, new transformation, meaning you don't need to tag these 50. So that's that is coming.
Saurabh Gupta: I I strongly believe that is gonna happen.
Saurabh Gupta: The second shift churn,
Saurabh Gupta: I feel, is if you think for the whole life cycle of any data,
Saurabh Gupta: there's a source system where the data is getting produced.
Saurabh Gupta: The engineering effort to bring data to a data lake data warehouse.
Saurabh Gupta: Then it is taken over by analytical engineers who their job is analyzing,
Saurabh Gupta: finding insights out of the data, turning into, dashboards, reports, and then the consumer side.
Saurabh Gupta: The consumer side could be executives, line managers, general regulatory reporting.
Saurabh Gupta: I strongly believe the middle layer, which is the analytical layer, is going to go
Saurabh Gupta: up. The bunch of models that we are having, like, whether it is anthropic or OpenAI,
Saurabh Gupta: they are going to eliminate the interpretation part, which is mostly the analytical engine.
Saurabh Gupta: So the whole system will, like, be engineering, producing good controlled data, metadata, and context consumed by these models,
Saurabh Gupta: and the consumers are going to interact with these models.
Saurabh Gupta: And I think that shift is coming.
Saurabh Gupta: It is going to also go through
Saurabh Gupta: some changes in terms of of our pressure test, in terms of token consumption.
Saurabh Gupta: Suddenly, you'll see a huge bill coming too.
Saurabh Gupta: But
Saurabh Gupta: that's a good place to be where the discussion goes away from
Saurabh Gupta: whether a model is going to be used or not.
Saurabh Gupta: I think it's given everyone is gonna use it. So in some form or fashion, how do you optimize it? And that change is going to bring
Saurabh Gupta: a shift in the skills and the jobs
Saurabh Gupta: where they are going to exist.
Saurabh Gupta: A lot of jobs should shift towards the left hand side with the engineering side.
Saurabh Gupta: The analytical side should become thinner and thinner.
Sean Weisbrot: And you think the models themselves are going to become specialized for that purpose?
Sean Weisbrot: Or
Saurabh Gupta: So models specialize for interpretation.
Saurabh Gupta: So the way, Sean, I see it is these models are, they are sending a lot of knowledge.
Saurabh Gupta: And these knowledge these
Saurabh Gupta: domain knowledges and the industry knowledge used to be typically with analyst.
Saurabh Gupta: Those are getting abstracted.
Saurabh Gupta: What the model is going to is going to help is interpret the data from a lens of
Saurabh Gupta: knowledge they have or the domain understanding there.
Saurabh Gupta: Whether it is right or not, it's still going to be on a human or who's consuming it. But that the amount of knowledge that each model can has accumulated
Saurabh Gupta: can eliminate billions of with tons and tons of analysts.
Saurabh Gupta: And I I feel we should look at models as interpreters rather than someone who's analyzing.
Saurabh Gupta: Analysis is still going to be on the consumer side.
Sean Weisbrot: Why do you think that is?
Saurabh Gupta: Because it's limited to the knowledge these models have.
Saurabh Gupta: Right?
Saurabh Gupta: If someone wants to so let's take a simple example.
Saurabh Gupta: I have all financial data created in a data product.
Saurabh Gupta: And I could there's a report which talks about how quarter on quarter performance has been.
Saurabh Gupta: Now the moment
Saurabh Gupta: an executive looks into this and he he or she finds something strange,
Saurabh Gupta: they go to the analyst and say, hey.
Saurabh Gupta: Can you give me a little more details of this?
Saurabh Gupta: Panelist will do go dig into the details of the data.
Saurabh Gupta: Give another one.
Saurabh Gupta: Then there's another question.
Saurabh Gupta: There's a back and forth continuously going on.
Saurabh Gupta: And the part of this whole back and forth is also getting to understand what the question is from the consumer side or the exec side
Saurabh Gupta: and how it can be sort of responded to the data.
Saurabh Gupta: Now all this knowledge is already with models.
Saurabh Gupta: Specialized models exist, which you'll have.
Saurabh Gupta: Now if I'm seeing a report, which I serve through a model,
Saurabh Gupta: same quad quarterly performance report, and then I say,
Saurabh Gupta: can you give me more details of this?
Saurabh Gupta: I see something strange.
Saurabh Gupta: The model should be able to engage, interact with the data and get me the answer.
Saurabh Gupta: And it should be capable enough or sufficient guardrails should be put that
Saurabh Gupta: it should not start hallucinating and try to answer for something which doesn't exist.
Saurabh Gupta: It should say, okay.
Saurabh Gupta: You're asking suddenly
Saurabh Gupta: you're looking at sales data, and suddenly you're asking for salaries of employees.
Saurabh Gupta: That doesn't exist going up to your engineering team.
Saurabh Gupta: But these models are going to get more and more capable just because it is
Saurabh Gupta: they have a lot of knowledge embedded in them.
Saurabh Gupta: And the time for a consumer to interact with a model and get an answer is going to significantly shrink.
Saurabh Gupta: This was
Saurabh Gupta: that's a place where, like, the time sync was with the analyst, which is going to have to go.
Sean Weisbrot: You mean
Sean Weisbrot: the people talking to the AI is going they're gonna get their answers faster,
Sean Weisbrot: or they're gonna use it less often?
Sean Weisbrot: Or
Saurabh Gupta: So that they'll get the answers faster,
Saurabh Gupta: and they have to they have to spend time
Saurabh Gupta: in looking into whether this the answer that we are they have got is right or not.
Saurabh Gupta: Is it making sense or not?
Saurabh Gupta: So so think about this, Sean.
Saurabh Gupta: Like, if I have a tabulated report on quarter on quarter results,
Saurabh Gupta: I want to go into the next level of details.
Saurabh Gupta: And if that report doesn't exist, an analyst will take a couple of days to a couple of weeks to build it for.
Saurabh Gupta: But if the data exists, the model should be able to extract it for me. So imagine that
Saurabh Gupta: if three weeks can be shrunk into three minutes, it's a huge compression of time.
Sean Weisbrot: Sure.
Sean Weisbrot: Yeah.
Sean Weisbrot: I mean, I I do that with Claude right now.
Saurabh Gupta: Exactly.
Sean Weisbrot: Like, I've done 320
Saurabh Gupta: Mhmm.
Sean Weisbrot: interviews, and I've taken the time before I had AI to generate the transcripts.
Sean Weisbrot: You know?
Saurabh Gupta: Yeah.
Sean Weisbrot: And now Claude just generates the transcript for me
Sean Weisbrot: when I upload the video into a Google Drive folder for one of the guests.
Sean Weisbrot: Like, this will happen for your interview.
Saurabh Gupta: Mhmm.
Sean Weisbrot: And from that, I can then
Sean Weisbrot: upload a YouTube video automatically.
Sean Weisbrot: It automatically takes the tags and the titles and the description, and it puts all of that in there.
Sean Weisbrot: It generates a thumbnail for me, puts all that into the YouTube,
Sean Weisbrot: and then it creates a blog post in the website
Sean Weisbrot: using the transcript data and the YouTube URL data.
Sean Weisbrot: It pulls out all in, generates a new page, and it puts the transcript on the the website.
Sean Weisbrot: And so now
Sean Weisbrot: Claude has access to every transcript from every interview I've ever done.
Sean Weisbrot: And so I can ask Claude
Sean Weisbrot: about all of the data from all of the transcripts.
Sean Weisbrot: And it's generated ebooks.
Sean Weisbrot: It's generated reports.
Sean Weisbrot: It's generated articles.
Sean Weisbrot: It's generated a lot of things for me based on the sum total knowledge of all the conversations I've had with guests.
Saurabh Gupta: So so your example is a perfect example.
Saurabh Gupta: You eliminated
Saurabh Gupta: or really, really compressed a lot of time that a developer, a website designer, a ebook creator would have spent on. You just eliminated that.
Saurabh Gupta: And
Sean Weisbrot: Yeah.
Saurabh Gupta: not not only that, like, your effort has drastically shifted
Saurabh Gupta: in place of getting things done, getting these books created, getting
Saurabh Gupta: transcripts ready to start validating the finished product.
Saurabh Gupta: I think it's a huge
Saurabh Gupta: saving of time and shifting of energies where in place of
Saurabh Gupta: spending time in building, you're just looking into analyzing and validating.
Sean Weisbrot: Yeah.
Sean Weisbrot: And I used to do all of this stuff manually
Sean Weisbrot: every every episode, you know, from people don't realize, but, you know, from the thirty minute intro call to the one hour recording, and then I have a person that does the editing.
Sean Weisbrot: But the pushing of data around from different spreadsheets and different applications and all of this from end to end could be four hours or five hours of work per episode from me, not even including my editor.
Saurabh Gupta: Yeah.
Saurabh Gupta: Yeah.
Saurabh Gupta: Yeah.
Saurabh Gupta: Yeah.
Saurabh Gupta: Yes.
Sean Weisbrot: And I've been able, with AI's help, to automate 95% of that work into, like, automated workflow scripts that Claude created for me. Plus, Claude has access to my repository and my host,
Sean Weisbrot: and so it's using my serverless host to create these functions and these scripts
Sean Weisbrot: in an automated way.
Sean Weisbrot: And so now I don't manage the pushing of data.
Sean Weisbrot: I manage the workflows.
Sean Weisbrot: Did the workflow break or did the workflow work?
Sean Weisbrot: So for example, when this interview finishes,
Sean Weisbrot: SquadCast is supposed to transfer the recordings to your Google Drive folder that was created when you you booked the interview.
Saurabh Gupta: Yeah.
Saurabh Gupta: Yeah.
Sean Weisbrot: And if that doesn't happen, I have to go to cursor and tell Claude to check on why that didn't happen, and then Claude will push it'll trigger the event to happen, and then it cascades into the the rest of the workflow.
Sean Weisbrot: And so instead of me putting, like, you know, an hour of work from downloading it to my computer and then uploading it into the Google Drive folder and then generating the thing and then, you know, telling my editor, it does all of it for me. So now I just manage workflows instead of managing the pushing of data.
Saurabh Gupta: Yeah.
Saurabh Gupta: Yeah.
Saurabh Gupta: Yeah.
Saurabh Gupta: Yeah.
Saurabh Gupta: And imagine.
Saurabh Gupta: Right?
Saurabh Gupta: I'll
Saurabh Gupta: I'd actually, I should ask you this question.
Saurabh Gupta: Are you able to do more than what you were able to do before cloud?
Sean Weisbrot: Yeah.
Sean Weisbrot: I can do a million things I couldn't do before.
Saurabh Gupta: Yeah.
Saurabh Gupta: I I think that's where the shift is happening.
Saurabh Gupta: And, the shift is, get will be complex, with structured data
Saurabh Gupta: because that requires a lot more domain understanding and understanding of sources of data.
Saurabh Gupta: But it's it's gonna be very, very highly valuable.
Saurabh Gupta: I remember, scenarios when we used to project, the growth of forecast growth.
Saurabh Gupta: And,
Saurabh Gupta: and they will circular dependencies where if one country is projecting a certain growth,
Saurabh Gupta: it it impacts another country.
Saurabh Gupta: So we have to keep doing these iterations.
Saurabh Gupta: And, it used to take weeks
Saurabh Gupta: to come to a like, for these models to resolve.
Saurabh Gupta: Now it can be done much faster.
Saurabh Gupta: Very, very fast.
Sean Weisbrot: What's the most important thing you've learned working with data so far in your career?
Saurabh Gupta: So
Saurabh Gupta: the most important thing, Sean, is what I have learned about working with data is you have to be very disciplined.
Saurabh Gupta: The moment you try to cut corners,
Saurabh Gupta: it'll come and bite you.
Saurabh Gupta: And the only reason why I'm saying this is the most important for now is because,
Saurabh Gupta: the volumes of data is going to continuously increase.
Saurabh Gupta: The type of data is increasing.
Saurabh Gupta: Sources are increasing.
Saurabh Gupta: So one
Saurabh Gupta: corner that you've got, it'll come in with you in the future.
![AI Is a Block of Clay and Most Founders Never Sculpt It](data:image/svg+xml,%3Csvg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 400 300"%3E%3Crect fill="%23f3f4f6" width="400" height="300"/%3E%3C/svg%3E)
