Anmol Satija – So hello and welcome back to the unthinkable tech podcast, the go-to source for the pulse on technology shaping our future. I am Anmol Satija. And if you know over the past few episodes, we have been diving deep into some of the most exciting trends in AI. But today it is different. So instead of exploring what’s new, we are focusing on something even more critical. Today we’ll be discussing how do we actually find, build and deliver real value in the rapidly evolving AI landscape. as you know, leaders across industries are navigating a world transformed by artificial intelligence, whether it is streamlining operations, enhancing customer experience or driving innovation. AI is at the core of modern technologies. But with so many possibilities all around, how do you really identify the right opportunity?

More importantly, how do you ensure your AI investments lead to measurable outcomes that truly matter? So to help us answer these questions, I am thrilled to welcome Rushi Ganmukhi, the CEO of Bola AI. Rushi’s passion for AI and languages led him to found Bola AI in 2017. Previously, he held research positions at MIT, working on projects in NLP and AI for the Department of Defense and the FDA.

Rushi has worked in the software industries for over 10 years on AI projects from varying from marketing tech to coding tools. He holds a BS and MS in computer engineering from Boston University. Hi Rushi. So it’s a pleasure to have you here. Welcome to the show.

Rushi – Thank you, thank you so much for having me today. I’m excited to talk about this topic because it’s really where the rubber hits the road, we think.

Anmol Satija – Yeah, so am I. you know, Rushi, as we are talking about, AI is the buzzword everybody is talking about these days. it is very obvious, right, as it is transforming industries across the board. But we have often noticed something very interesting. While so many organizations are eager to dive into AI, they often find themselves stuck at square one.

Anmol Satija – They want to leverage it, but struggle to pinpoint how, like what specific problem they should solve or maybe where AI can really move the needle for them. So let me just kick off things by asking you what strategies would you recommend to companies for identifying high value AI applications that genuinely align with their business goal? Maybe you want to talk about a framework or a mindset.

Identifying high-impact AI applications: A strategic framework

Rushi – Yeah, definitely. So yeah, this is definitely the lay of the land today where you have these big companies and small companies who on the demand side want AI, right? And that may be as far as they’ve gone, right? Where they’ve just said we want AI. And then on the supply side, of course, you have all the new models coming out, companies forming every day, lots of venture money to deliver it. And really, where the rubber hits the road is meeting those two. right?

And it’s a pretty big gap right now. And so it’s a very hard problem to solve. So yes, like I said, you have a lot of companies who are looking at AI, they’re looking at AI for productivity, efficiency of staff, maybe they’re looking to enhance their revenue in some way, or yeah, in just some way, streamline operations. That’s kind of what you really get from these companies.

Rushi – And really the crux of it is diving in with finding a few, ideally a few good companies to work with and diving into what do they really want. So for instance, we’re in the voice technology space in healthcare.

And we get a lot of requests that say, we would like to voice enable this or voice enable that. And of course, we’re happy to build it. We’re happy to get more customers. We’re happy to grow our revenue. But there’s a lot of work bridging the gap between what do they really mean? What are they trying to achieve? Are they trying to help this type of staff member out for this many times a day? Are they trying to drive this other type of revenue? And so step one is really understanding their goal. What is the value they want to see out of this.

How to measure AI project success?

Then once you’ve determined the goal, okay, we want to. So for let’s say, dental practices, right, we want to reduce our appointment times so that our staff can actually do more things. So let’s say, okay, that’s the goal, then, then the second step would be how do you measure that? Okay, well, we can look at average length, average length of certain procedures. We can look at outcomes as well.

Treatment outcomes, are they accepting treatment? Is that turning into revenue? Getting to the hard numbers. And ideally you take a few of those because one metric never tells you the full truth. And you can always gamify it. So kind of working with your partner and bringing that early product mindset to them and introducing them to that way of thinking.

Now, once you’ve settled on that, then really understanding their workflow is number three is how are they doing things today? The worst thing you can do as a tech company, the absolute worst thing you can do as a tech company is come up with an awesome product, but it completely changes how people work or what they’re doing. Because on paper it will be great, right?

But you have to understand, especially like, let’s say, healthcare, again, as an example is the doctors, the dentists we’re working with have been doing what they’re doing for 20 years on average. And so if you’re trying to improve their process, you can’t upend their process, because that is that is how they do things. They’ve honed those skills, they’ve honed that process, they’ve honed their staff into that process over many years. So as seamless as you can make it is ideal.

Rushi – Now, in theory, that’s awesome. But in reality, you will change the workflow a little. And it’s finding those acceptable ranges with customers. And you truly only find that once you’ve built the product and they’re using it. When you build it and show it to them, you’ll get a lot of, yeah, this could solve XYZ problem. But then you actually need to deliver them an MVP.

And get them and tracking usage, right? And seeing, okay, they’re using it. they’re not using it because it doesn’t make sense where we’ve inserted this into their workflow. So that’s really the third step is mapping their workflow. And then the fourth step I would say would be driving the usage, driving the adoption. And then you get those first four, right? Then Revenant will follow up.

Anmol Satija – That’s a very thoughtful perspective, I would say. And I loved how you have given a breakage and with certain steps that you mentioned. And it really underscores the importance of going beyond surface level requests to uncover the real need. And building something in that acceptable ranges, like you mentioned, not doing something that truly changes how they work.

How to spot real AI innovation?

So that’s one important point. But here’s the thing, when they are operating in a space as dynamic as AI, they are no shortage of ideas and solutions being thrown around, right? So some of them genuinely have the potential to transform industries. As you mentioned that they might come up with an awesome product. While others, well, let’s just say they might be more hype than the substance. So.

How can they really differentiate between innovations that are genuinely transformative and those that are just riding the wave of AI buzz? What exactly should they be looking for to make smart and impactful bets?

Rushi – Yeah, so I think this is a really common problem that comes up, especially actually in healthcare as well, is so many hospitals and groups have been burned by this amazing technology that was gonna come that never materialized. So one thing I’ve noticed that companies really, really value is that when they’ve seen you’ve delivered something, when they themselves see, we have X many customers, hey, we’ve done millions of patient charts, you’re showing them the proof of you have delivered something as a company. Now it may not be exactly what they want, but they see that your company can deliver. And that’s a really good stamp of approval. And for early companies, it’s getting as much validation as possible, right? Getting customers using it may not be huge amounts of revenue, but just getting the usage or any other metrics you can track.

And that’s what bigger companies when they’re purchasing should really look at is some type of traction, some type of proof. of course, then walking down that spectrum, having more and more of a finished product, more and more success goes a long way. And all companies, no matter how big they are, always check references, always know people at other companies. So if you can provide references of successes you’ve had, it goes a really long way.

Anmol Satija – Yeah, I think you’re absolutely right, Rushi. And it is so true that proof points like success stories and references go a long way in cutting through all that noise. So those practical considerations, I think, are often overlooked, but can make or break the success of an AI implementation. So this leads me to another challenge that we see in organizations.

Let’s say they have already done the groundwork and identified where AI fits into their operations. Maybe how does it fit in their product offering? But even then, there’s often a lack of clarity on the specific value they are trying to achieve. It’s almost like saying, we know we want AI here, but what does success really look like? So how should companies navigate this gap?

And how exactly can they ensure that their AI investments don’t just feel innovative, but actually deliver tangible or measurable outcomes or results to say.

Rushi – Yes, so I would say the first thing to do, and it’s really hard to do for startups. And luckily, we are, as a company, past this stage. I don’t want to be back in this stage. But it’s don’t chase the revenue for startups. Don’t chase making the revenue today over making a customer happy. And then they’re a customer for three years, and your revenue is through the roof. Because if the startup chases that, you will, there’s a possibility you will end up with customers who, yeah, you did great during the pilot, but you’re not providing them the ongoing value. And that first customer is your reference that first customer, your business is built on. And so you want that to be a solid foundation. And so the number one is you’ve got to trace the metrics they want and show them what they want – the usage, the efficiencies.

Aligning AI with business outcomes using outcome-based pricing

And I think one of the really crucial things is, if you’re a vertical AI player like us going specific verticals, is understanding their business. Through understanding the clinical side for us in health care, understanding what different types of exams look like, understanding what different softwares are out there, understanding how offices operate financially, right? What are they spending on? What are they? -and really going deep and really, really understanding their market.

Then you work with them to say, okay, these are the KPIs and metrics we wanna hit. This is what we wanna hit in the pilot and then this is what we wanna hit on an ongoing basis and getting that buy in.

But it’s a lot of measuring and it’s a lot of it’s a lot of working with them to understand those things. I would say one of the really interesting things that we are also looking at is with a lot of AI companies, I’ve seen outcome based pricing models. So

With traditional SaaS, you have the subscription, The how much per month, recurring fee. Then also with traditional SaaS, a lot of companies do usage based models. So X amount per use, usually like around a dollar and that adds up.

The outcome based model for AI is really beautiful because it’s the model where you are hitting their outcomes, they are deriving some revenue from that and you are having a piece of that revenue. So for something like healthcare, could be approved claims for…

So it could be saving this much time, which means you don’t have to hire that extra sales rep, right? That type of thing, that outcome based pricing model really keeps the company and the company who’s purchasing completely aligned with the goals. Is when you guys win, everyone wins. And it’s the startup’s job to make sure you win. And it’s a really good win-win. So we’re super interested in exploring that.

Yeah, even I would like to second that because here at unthinkable solutions also we are, we have been exploring this same model. Like the focus here is on delivering value rather than just focusing on time or the pricing thing, you know, and like you said, it’s a win-win situation for both the client and the vendor. They win when the customer receives the exact value they have been looking for and irrespective of the time.

Anmol Satija – You know, like you said, it’s an amazing opportunities for companies alike. So I would say, yeah, whatever you have said right now is a very practical approach to see the AI revolution. And it’s easy to get caught up in the excitement of AI. But having that clear vision of what success looks like and actually taking the time out to test is feasibility with your team and stakeholders make all the difference, right?

What’s next? Emerging AI opportunities in underinvested industries

Well, looking ahead though, I’m curious about the opportunities on the horizon. is still evolving very rapidly and it feels like we have barely scratched the surface of what’s possible. So in your view, what do you see as the biggest opportunity for businesses to leverage AI over the next maybe three to five years?

Anmol Satija – Are there any industries or verticals you think are currently flying under the radar but have massive potential in this space?

Rushi – So we are definitely very, very excited about the future of AI. And I will caveat all my answers with, I will probably be wrong. I’ll probably completely wrong. And I’m probably under guessing what can really be done with this technology. So I think one of the big things that is great is I’m on LinkedIn a lot. So I see a lot of articles about what AI can do. there’s a lot of hype, it’s also like, think of it as like a thought experiment, that would be interesting if that happened. And you can really get your gears turning. So staying on top of those is really crucial. And look, most of those probably won’t come to fruition, but just having that expanding your mind to those possibilities. Because we are, you don’t realize it. And even as a startup founder, right, I think I’m on the leading edge of tech, but then you think about things, you read articles and you realize, no, you’re in the old mindset.

So getting in that new mindset is crucial. And I think really a place where AI is truly going to make a big impact, let’s say in the near short term, that three to five year mark, is a lot of the industries, specifically kind of in the, I’m thinking about the US that are massive industries. So we’re looking at healthcare, we’re looking at manufacturing.

Rushi – We’re looking at clean energy that may be maybe not clean energy falls in this category, but that may be a little more laggard industries. don’t have the they’re not getting the coolest dev tools lately. They’re not there’s no maybe AI has a minimal few percent market penetration right now. But really those industries can be completely up ended in a good way by AI completely optimized and looked at. So I’d say the further behind it industry is, think AI can do more in that industry. And this goes back to my previous point too, where

Rushi – Look, most people who start companies in healthcare or manufacturing or some of these laggard industries don’t have the experience in those industries. They haven’t worked in those industries. They don’t know it in and out. So going deep, if you want to go into one of those verticals, massive market opportunity, but kind of the price of entry to be successful there is really understanding the industry truly like at its core economically.

Just everyone’s motivations, how people are getting paid, what the revenue models are. Really, really understanding that industry to a core is crucial for AI startups.

AI startups vs. giants: How niche focus creates an edge

Anmol Satija – Right, that was an insightful Rushi. yeah, could, AI could really, you know, reshape these industries in the coming years. Now let’s shift gears a bit. Let’s talk about startups, which I believe is your favorite topic to talk about. So, so they often find themselves in a David versus Golight situation when competing against larger AI giants.

Anmol Satija – So startups typically don’t have the massive resources that big players have, but they do have some unique advantages. So how do you think startups can leverage them to stand out in this highly competitive AI landscape?

Rushi – Yes, now right now, if you look at the market, right, there’s big players who are coming out with things that break neck speeds, right? A lot of startups are created and then Microsoft creates something and they can do it for free, right? And also those big companies have the distribution power and the power to do things as an add on to their existing software. So it’s definitely something you need to keep an eye on in the market.

Rushi – But I think the advantage of smaller startups is really going for those vertical plays right now. And I think I would say that going vertical does not mean you’ll always be in that vertical. You can always expand. Now it’s extremely hard to expand. I We’ve always I’ve always faced that it’s much harder than you think to expand into other verticals. But starting in a specific vertical that you can really, really understand and provide value in that bigger companies may not be looking at, but that you can get to a healthy revenue amount, a healthy TAM in those markets and then expand. And that’s one really big power that startups can have.

Rushi – And of course, moving fast, working with the customers, providing that service, providing that cycle of feedback. Those are all things that startups can typically outmaneuver bigger companies at. And so you have to find your little safe pocket and win there, right? Get your first win, really own a market, deliver value, grow nicely, and then you can grow outwards. But I know all young startup founders, I included, wanted to, right?

Rushi – You want to go for that horizontal play in the beginning. You always are so attracted to it. we want to do AI that fixes everyone’s problems in every industry and does everything. And that’s so attractive, right? You’re talking billions of dollars of TAM. You’re talking, really cool product. you don’t need to worry about getting into markets because you’re doing it for everyone. And look, there’s companies that do that. There’s massive companies that did that and really succeeded. So it is a path to success. It may just be a less probable path to success.

Scaling from early adopters to the mass market

Anmol Satija – Yeah, I think what you just said is very fantastic. And I think the approach you mentioned is a very smart way to create a defensible niche and truly build impactful products. But here’s a challenge that I would love to highlight. Early adopters are often very enthusiastic and willing to take risks, making them relatively easy to win over. So the real test comes when it’s time to scale.

Anmol Satija – So convincing the broader market, the non-early adopters to say, to embrace AI solutions. So what strategies you found effective in appealing to these more cautious or traditional businesses and maybe bringing them on board?

Rushi – Yes, so this is a great problem to have. means if you’re facing this problem, it means you’ve had success in the early adopter market, and now you’re looking at the broader market, which is typically bigger, and you need to adopt. So I think number one, success in the early market is your foundation, right? They’re your references, they’re your quotes, they’re your usage stats, they’re your success stats. That is like really your core to the success in the next round.

Rushi – And then success in the next round is difficult because you will get a lot of people who don’t want this flashy AI. They want things working as normal but maybe a little better. And I think the difference here is really the usability of the product.

Rushi – And that’s something we’ve put a huge premium on is making it easy to use onboard install understand. We work with a lot of people who don’t have technical backgrounds. And funny enough, people with clinical backgrounds think very, very different than people product backgrounds. It’s they’re also very intelligent.

Actually, this is probably my biggest learning from a startup is like, there’s another way to think that’s completely different than the way I think. Also highly successful. And that was a big here to me. But it’s really, yeah, it’s really getting the usability up. And I think a good comparison here is if you look at like, Apple products and how they embed AI.

Rushi – The AI isn’t, and I haven’t used Apple intelligence, but in general, how they’ve done Siri and some other things is the AI isn’t smacking you in the face right there all the time. Big branded AI kind of general, it’s behind the scenes, it’s working. Behind the scenes, it’s making your life a little easier. And then one day you go to do this thing and you’re like, it’s already done or this can be done this way. And that little pleasant surprise is what drives their adoption. And it’s almost the AI is working behind the scenes. So that may be something a lot of companies need to do as they move into the broader market, where it’s not AI that can do this for you. It’s, hey, we’re doing this for you. We’re streamlining your operations, and the AI is working in the background. It’s almost imperceivable to the customer what you’re using.

What makes a use case stick?

Anmol Satija – Yeah, I think that’s a great point Roshi and it’s all about, you know, just seamlessly sliding into the workflows that can really make or break the adoption scenario, right? Especially for non-early adopters. So as we wrap up this conversation, I want to touch upon something I think is a key for all our listeners, that is sustainability. So many AI applications are exciting and generate a lot of buzz initially, but they often fizzle out when the novelty wears off.

So in your opinion, what are the hallmarks of an AI use case that delivers sustained value over time? And maybe what should leaders look for to ensure that AI investments stand the test of time?

Rushi – So this is a hard one and this goes back to probably the hardest thing to do as a startup founder is you want to have that billion dollar vision. You want to have that, yeah, this is going to be, this is going to revolutionize this market and this is what it looks like and we’re going to do these 10 other products along the way. And you want to have that billion dollar vision and you want to have that. You want to share it with the team. You want to get everyone excited about that.

Anmol Satija – Yes.

Rushi – But on the other side, you need to live in absolute detail of reality of how is this customer do? How is my biggest customer doing? How is my first customer doing? Are they are they using the product like the simplest question, right? And you need to live in both worlds all the time. And I think through that, if you successfully do that, you will see the sustainability because you want to bring most things that fizzle out.

Rushi – Have not provided sufficient value in the beginning. If they fizzled out, if they write, you’ve maybe successfully done a pilot, maybe successfully got your first customer, but they’re not seeing repeated success. And then you go sell to other people and you kind of, keep getting those contracts, you keep growing, but they’re all their usage is fizzling out and they’re churning over time. And so you’re seeing that pattern where yes, people want it, which you’ve created.

Dream big, execute small

On paper creates value and so they’re buying it, but in reality it’s not creating value so they’re sizzling out. So what you really need to do is make sure that that value remains and mean ideally grows over time for those customers.

And once that happens, you’re golden because they will come to you and they will tell you two more products. Then you will also have enough knowledge of the market so you can start your engineers look at how a dentist is doing something and they’re like, why are they doing it this way? Let’s just build this, right? Then you have enough knowledge to front run.

Rushi – And so it becomes a runaway cycle, but it all has to do with how successful your early customers are. And you can keep growing and keep sustaining that way. And then of course, having that vision in your mind, but that vision is gonna change. And it should change because that vision is untested, it’s not real. Then so bringing that vision into reality, it will change and kind of rolling with that and changing your trajectory to build that out.

But again, it’s all about fundamentals, and we really stress fundamentals of, just get your first few customers really happy, get them value, and then things will grow from there.

Anmol Satija – Right, I think that was such a strong note to end on. Focusing on your first customers and staying relevant in the round and it all comes down to how you have explored and how you have marketed the product. And efficiency may alone not be so good, but if it doesn’t translate into tangible results,

Rushi – Yes.

Anmol Satija – that also impacts the company’s bottom line. So it’s not going to hold up.

Rushi – Exactly, exactly. And you want them coming to you at renewal time and it not even a conversation. It shouldn’t be a conversation. It should just be there.

Anmol Satija – Yeah, so this has been such a fantastic conversation, Rushi. I have enjoyed every bit of it. And you have shared some incredibly valuable insights that I’m sure our listeners will be able to take back in their organization as they navigate the evolving world of AI. So thank you for joining us today and for all the wisdom that you have shared.

Rushi – Thank you so much for having me. Always happy to talk about this.

Anmol Satija – Yes, and to our listeners, I hope you enjoyed this episode of the Unthinkable Tech Podcast. And if you are looking to stay up to date with all the technological advancement, be sure to tune in for the future episodes. We have some very exciting themes lined up for you. So keep listening and don’t forget to follow. Thank you and bye.