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ZINNOV PODCAST   |   Business Resilience

The AI Paradox: Between Human Compatibility and Empathy

Chetan Dube & Lanham Napier
Chetan Dube Founder and CEO Amelia
Lanham Napier President Amelia

As we witness rapid advancements in Artificial Intelligence (AI) and Automation, the concept of intelligent bots wielding unprecedented power becomes an intriguing prospect. Will these machines, equipped with ever-evolving capabilities, surpass human intelligence and ultimately dominate our society? The notion may seem like science fiction, but as technology continues to reshape our world and advance at warp speed, it is essential to explore the possibilities, ponder the ethical implications, and navigate the path ahead with caution and foresight.

In this thought-provoking episode of our podcast, we delve into the fascinating realm of AI and explore the profound implications of AI bots becoming increasingly smarter, more efficient, and eventually developing the capacity to experience emotions.

Chetan Dube, Founder & CEO Amelia, and Lanham Napier, President, Amelia, talk to Pari Natarajan, CEO at Zinnov, about the evolution of AI bots and their potential impact on human productivity. With insights and captivating anecdotes, this episode explores how AI will soon become a way of life, replacing human functions as we know them today.

What does AI really mean for us, as a human workforce? Chetan and Lanham offer diverse perspectives on the capabilities of an AI worker, drawing from psychology, neuroscience, and AI ethics, as they delve into the complexities of human-like emotions in machines, powered by AI.


2:39 – The implications of Generative AI on Amelia
6:32 – How Amelia is keeping up with disruptions
9:09 – How technology can solve problems while building customer relationships
13:17 – How AI will play a role in the future of SMBs
17:17 – What is the technology evolution required for the world to see digital employees?
21:20 – How is Amelia thinking about catering to different verticals?
23:39 – The role of partnerships in building vertical expertise
24:48 – AI and ethics
27:29 – Making AI global ethics-compliant
29:53 – The future of Amelia


Pari: Hello everyone and welcome back to another exciting episode of Zinnov Podcast Business Resilience Series. I’m Pari Natarajan, CEO of Zinnov, your host for this episode. We are in this era of Artificial Intelligence and Generative AI is ushering a new set of technology innovations.
It’s said that it’s going to be bigger than the internet in some level and this is fascinating what changes we have seen in the last six months, be it in terms of newer use cases it’s creating, the disruption it could potentially create in a short term in terms of job losses. And one of the number one question in every boardroom, be it your large enterprise or a mid-market company, it’s about what is the company’s opportunities around Generative AI, and is that any of the business is going to get disrupted by Generative AI. The third is how do I now improve my productivity of my organization and do more with less? And how do I use AI to drive that? So lots of interesting conversation.

We haven’t seen the global technology narrative being hijacked by one such technology in a long time. So going with that, we are going to have a very interesting conversation with couple of senior thought leaders in this area. We have Chetan Dube, CEO of Amelia and Lanham Napier, President of Amelia. Chetan, a Mathematician, has been recognized as one of the nine greatest AI minds in the world to keep an eye on by Forbes. He has led Amelia since its inception and has steered the company to create a radical shift in the way IT is managed. Lanham joined Amelia in January 2023 as part of the strategic partnership between Amelia and Lanham’s Build Group. A consummate global leader, Lanham has been recognized by Forbes as one of America’s most powerful CEOs under the age of 40 and under, and I guess Lanham, that was a while ago. Welcome, Chetan and Lanham. It’s great to have you with us today.

Chetan: Well, thank you for having us, Pari.

Pari: Without further ado, let’s dive in. Chetan, Amelia has been in the forefront of AI and the AI landscape between evolving at warp speed, right. And with emergence of Generative AI and ChatGPT, what are the implications you anticipate for Amelia and how is your team preparing to address these changes?

Chetan: Yeah, Pari, you’ve known us for a little while and as Turing purists, we have been saying since, I remember in 2015 we said that, by 2025 digital labor will become indistinguishable from human labor. And as you pointed out at the onset of this podcast, that with the advent of Generative that has transformed into reality. Generative has been around that whole 10 year period that we have been seeing the strength coming. Go back to, if you just look at the Generative technologies itself, and you would forgive my preoccupation with numbers as a mathematician, I just like to be able to go to, what do the numbers tell you.

ELMo language models, all cartoon characters, we kept running out of them, but BERT with a bidirectional as opposed to unidirectional ELMo. You now looked at bidirectional encoder models. What was the parameter size for BERT? 345 million. From that you graduated to Turing ML that was from Microsoft. And now you were looking at a parameter side of about 10 billion to 17 billion. And then comes GPT 3.5. 175 billion parameters. And GPT-4 over 1.1 trillion parameters. So you can see the curve, 345, to 10, to 175, all the way to over a trillion.

So what happens? The hallucinations go down, the amount of entropies in the recall and reinforcement learning with human feedback tend to diminish, and you start to get to much more convergence with realistic-sounding output. That’s where we are. Now the question for us to be able to see is that, so we have seen this coming, we’ve been following and we’ve been active participants in this as we have seen from bidirectional LSTMs to where GPT has come along now. The question is what will happen with these Generative technologies? You are going to see not just OpenAI. You are going to see Bard.

You’re going to see Stability AI, you’re going to see Cohere. You’re going to see LLaMA. You’re going to see Amazon’s LLMs. You’re going to see LLaMA from Meta. Is there going to be a big distinction between the output? Go to Bard right now and ask a question.
Go to GPT-4 four, or go to Entropik and ask a question, and you start to see that the variances between the outputs will start to diminish with time. And within the next six months, by the end of the year, you’ll start to see, wow, these LLMs are getting fairly sophisticated and becoming very, very exact.

Obviously. we used to have bottom 10% of the law exams, now top 10% of the law exams. So of course we can see they’re becoming fairly sophisticated, but they’re also becoming fairly commoditized. That’s what will happen. This will be a very foundational technology that’s just about everything, everybody will leverage. The question is, what do you do with this Generative and what can you build on top of this Generative?

For instance, I ask you, can you take the output of Generative? Can you actually have a Natural Language Compiler that understands what was the output of Generative, and can you raise that to the power of cognitive to be able to push button print your digital employees. Not just in what will become commoditized Generative technologies, and that’s where our focus is, is to be able to see how Amelia can leverage and harness the power of these technologies that we’ve been following and actually been involved in researching and raise them to the power of cognitive to be able to address the needs of the industry.

Pari: Very interesting, Chetan, so it’s really when the Generative AI gets commoditized, it’s easily available across all the different hyperscalers and other platforms; then the differentiation is going to be understanding tha context of the enterprise or problem, and be able to build solution, which is going to solve the real world problem.
Lanham, could you give us some examples of how the Amelia AI platform has helped organizations in kind of creating this kind of impact? And how do you even measure success and ensure that that platform continues to deliver value because the level of disruption on technology like what Chetan said, things are getting commoditized quite rapidly and the customer expectation and value, I’m pretty sure, are also changing on a regular basis.

Lanham: So an easy way to think about the application of this emerging tech is, you know, companies just need to get AI into their digital workflows. I mean, we went from, with the internet, we all went from, you know, call it a spreadsheet, paper-based process, you know, into a digital process. Basically the Cloud gave us a ton of SaaS.
And what’s happening now is AI’s going give us smart SaaS and we’re going to get AI in all our workflows. So a good example, you know, inside of Amelia today with the tech that Chetan and Amelians have built is, you know, what we do for a customer like Vodafone. So, you know, the measurement we use as containment, right?
These are in inside a call centers, where a customer calls in, you know, has a question, has a problem, wants a resolution, you know, these are hundreds of thousands of calls a day. And Amelia learns, you know, the AI learns once it’s inside a customer’s workflow and starts to solve 60% of the calls that show up. So when we think about from a business point of view, just the productivity boost there, it’s pretty shocking.

These are early days. I mean, while the LLMs have been around and while the technology’s been around, it’s the rate, the curve that Chetan just took us through, you know, shows us how fast it’s improving. So today when systems are knocking out over half the problems that show up, you know, there’ll be a day where it’s knocking out almost all the problems that show up. And the productivity boost, you know, the application of that tech in a business, when you think about output per employee or whatnot.
Now what’s an employee? That’s going to be an interesting thing to define. You know Chetan was just talking about digital employees, but I think net net means getting AI into a workflow and solving problems is what is going to happen inside of businesses.

Pari: Very interesting. And Chetan, if you’re coming into, you talked about newer applications you’re building on top of Generative AI right?
And Lanham talked about 60% of the problems are getting automated completely. But if you look at the role of a call center agent, and it’s moved from a call center agent solving a customer problem to somebody who can continue to upsell offering into the customer, and the role has been transforming over a period of time.

So now I’m going to automate 60% of the calls without a human touch. I’m going to follow it and hopefully it’ll get to about 100% of that. Now you lose an opportunity for a human touch with the customer. Right? And how do you see that the technology evolving in a way that Amelia’s able to also take on a role of adding new revenue rather than saying, I’m going resolve the problem as soon as possible, but I’m going to upsell X, Y, Z based on what the customer is looking for. Or you see a world where there is a different engagement model for the individual physical agent. How do you see that technology evolving? And it’d be great to hear your roadmap around these.

Chetan: Pari, that’s a great question. The applications are going to be so profound that the human touch, one of the things that you pointed out, are you really going to be able to distinguish whether you are talking to a human being or whether you’re talking to a digital? Are you going to be able to distinguish whether you’re talking to a carbon, hydrogen, nitrogen form as opposed to a silicon gallium arsenide form?

These technologies will emulate humans. And I think why, because Turing had it right when he was just before 1950s, he had said that you will not be able to distinguish between the two. So these will be empathetic. Japan where the number of adult diapers over the last three years have outsold the number of babies diapers. The need for human contact will be addressed by these technologies that will be empathetic and sympathetic and have all the emotions that you can find in humans and they will grow with you and they will have personalization to be able to understand what Pari likes and does not like. And be able to graduate with time and to be able to have that vector of empathy with you that will actually resonate with you. The PAD-OCC models see today allow us to be empathetic with a person, whether it’s a customer or whether it’s a person or whether it’s a personal concierge you will see that empathy vector and sympathy vector growing.

PAD-OCC, is the Pleasure, Arousal, and Dominance, three-dimensional model that tries to capture human sentiment. So that’s the evolution of the technology and the roadmap of the technology. You could never be more excited about the futures of where we are going to be able to participate. And if you allow a moment of immodesty team, where we’d be able to lead the industry, the biggest part of the future, which Lanham have been a very strong advocate of. Why is AI mostly weaponized by the big enterprises that can afford it?

Can we take AI downstream the SMB segments, and eventually go down to the consumer layer? Can we disintermediate and can we actually (much abused word) can we actually democratize AI? Can you be a pastry chef owner in San Francisco and say, I want to be able to get my inventory manager? Please send me my digital employee that does inventory management for me. Can you have a bicycle shop and can you say, I want to make appointments for the bicycle consumers to be able to make appointments for me. And please have an employee Tom created for me that can do appointments for me.

Can you build these models and can you truly democratize AI? Can you make them available push button to the SMB segments, and tomorrow to the consumer segments. That is one of the biggest directions that we are very excited about the future of the platform. In AIOps itself, I mean, I think there is a bunch of threads that we are researching and we are actually going to be bringing to the industry that will be mind boggling.

Pari: Very interesting, Chetan, in terms of the opportunity with small business and consumer. At some level in the future, the human is not going to call into the call center of an enterprise.
The human’s agent, the AI agent is going say,’ Hey, I need to now change my flight ticket.’ And then that’s calling into the enterprise. So the AI on both sides is interfacing with each other to be able to enable that. So it’s like, in some level the human is not even involved in that interaction.

And you talked about SMB and Lanham, you know, that’s an area we are fascinated about. You’ve built great businesses which are multi-billion dollar businesses in that area. One hypothesis we have is we are in the verge of the end of industry evolution. So the last 200-300 years is, it’s relatively a very short period in the evolution of humans where we organize ourselves into large companies and then people were asked to do mundane roles, because Ford built the assembly line and then made everybody do a very small part of the work instead of using human ingenuity.

Now with AI, you’re talking about applying to this small businesses. In some level, a small business could be an autonomous small business where somebody has a great idea, but everything around building the business and running the business can be automated. So how do you see AI playing a role in SMB and you built SMB business 20 years ago? Like, what’s changing?

Lanham: First of all, it’s super fascinating and I’ll tell you the story I tell myself. What I want to believe is that access to this emerging tech is a great leveller. And what I mean there is you know, if an entrepreneur has an idea and can somehow automate a lot of the tasks and activities inside the firm, I think we end up with basically a new type of firm.

Because, you know, one of the questions on my mind for years has been what comes after the corporation? You know, we sort of started with, you know, Italy, with the Medici with the family business, family partnership thing. And then just the innovation of a corporation itself was a heck of a good thing for humanity.
You know, look what we’ve got now in terms of economic systems and business outcomes, and I’ve always kind of wondered what’s next. Well, what the internet started to do was enable a small firm to have a global supply chain, literally from their living room. And what the pandemic taught us is that we can have labor pools decentralized and distributed all over the place.

And I think what AI is going to enable us to do is do the same thing with those digital workflows we talked about earlier, is that as a small firm with the ubiquitous connectivity of information and putting intelligence in those workflows with AI, I think a small firm’s going to be able to tap into scale they never had before.
And this is why I kind of think it’s a little bit of a leveller. And the interesting thing that Chetan just referenced that I’m not sure if everybody caught, is today the advantage in AI is for the big firm. The guy that’s got the huge enterprise IT department and an army of developers and all the world’s data.

Because part of the little trick here is, you know, without interesting data, AI is kind of worthless. So the question for the small firm, is what’s the interesting data they can get their hands on, whether they’re taking a vertical approach or not. That to me is the conundrum for the small firm. So my prediction there is that we will see organizations rise that are basically data brokers for interesting verticals for people to get consumption data or production data or whatever one requires to launch a new firm in that area. So we’re seeing some of these pieces come together now, but my prediction is just that there will be ecosystems that form for SMBs to aggregate the power of data and access AI as if they were a large enterprise. I think there’s a big business opportunity in that.

Pari: Of course you’ve seen what Cloud did for IT infrastructure. That’s exactly what it did for small business, right? Everybody has access to a massive infrastructure capability. But only the large companies, even 10 years ago had expertise. And today, if you are a designer, you don’t have to work for a large fashion label. You can use Generative AI to create your designs, have it manufactured in China, have it discovered in platforms built on Amazon and other platforms, like Toast, and then have the payment and all of that completely automated, including Amelia, using Amelia to be able to provide the complete integrated customer experience. So how far are we, if you think of a small business, if you think about the growth over the next 20 or 30 years, will the growth move from a corporate to small business entrepreneurs to self employees? And what is the technology evolution we need to do to be able to get there?

Chetan: I think with Lanham coming on board, he’s a master disruptor. By even as early as the next quarter, you’ll start to see evidence of people being able to hire their digital employees and what you’ll also see is that these are going to be very specialized employees that people will be able to get. You hire an employee. Are the employees able to do decision AI for you? Are the employees able to do human employees? Are they able to do trend analysis for you?
Typically, they’re so swamped with their day-to-day activity. Are they able to study all the digital exhaust to be able to understand what the construction of the NPS is, to be able to understand which causal factors are causing your customer experience to be good or bad?

Not just data NPS was high or bad, but is it the price? Is it the customer experience? Is it the agent? Is it the data center? Is it the technology? What caused you to have that so that you can neuro-surgically adjust the NPS? Today our human employees are so swamped. They’re not able to do these trend analysis. They’re not able to do decision AI. They’re not able to mine the embedded intelligence from the digital exhaust that the digital counterparts of theirs are going to be able to see. So these are not just going to be digital employees that will just get the job done. They will also help the management with better business outcomes. They’ll be able to read the digital exhaust, mine embedded intelligence, provide better decision AI, outcomes, suggestions and recommendations, implement them. The big question here is that, where is the differentiation going to lie?

And if you look at more of the businesses at the core of the business, I think this is obviously one of the largest banker’s theory, their CEO, the core of banking whether you go to JP Morgan or you go to Wells Fargo, or you go to Bank of America, the constant theory is that the core of banking is getting commoditized. You get just about the same, you’re not getting a very wildly different interest rate for a 5/1 adjustable mortgage. So where is the difference? The differentiation is the core starts getting commoditized. The focus of the organizations to be able to use their digital employees for the core. Start to leverage the power of generative raised to the power of cognitive for the core of your organizations.

The core, the base part, the commoditized part of your organization, the nuts and bolts of your organization. The differentiation will start percolating up to the edge. That differentiation is where you should focus your human employees. Human employees should focus on multi-modal ways of attaching to your customer, better engagement models.

How do we have better originations happen? How do we do the deconstruction of the NPS to be able to improve the customer experience? How do we create better engagement models for customers? This is the way the hybrid workforce can be the most effective for organizations. Take the commoditized part and move your human employees up the chain to the edge.

Pari: So Chetan talked about commoditization at the bottom and then moving people to the edge. And which also means that when customers buy Amelia are they looking to buy for commoditizing their core? Are they looking at verticalized capabilities, verticalized solutions? So how are you going to market? Are you going to market different verticals, your value prop for bank is different to an industrial company or a telecom company. How are you thinking about the business?

Lanham: This is a super fascinating thing, and I think right now what’s happening in the market is the technology’s expanding at such a rate the short interest people want to do both. People want a vertical approach that’s super tailored to what they’re doing. And then the reality is there’s also generalizable stuff across industries. Okay. So when you think about what is Amelia doing about this inside of Amelia?

You know, Chetan and I talk about having Amelia run Amelia. So when Chetan is talking about inside the core, you know, to use the digital employees, like this is what we’re trying to do for ourselves. You know, we figure we got to dog food ourselves I mean, we’re going to talk to customers about how it works.
I mean, we’ve got to do it inside our own company first. And so the principle is to tie it back to Chetan’s comments, I think there is this generalizable core where digital employees, Applied AI, there’s some set of tasks there and processes there that that’s a square hit.

Where things get really powerful vertically is never underestimate the importance of domain expertise. There is nuance and there is value created in these little changes across companies and across industries. And so this is a part where, for the products we’re building for customers, we absolutely think about.
Tailoring Amelia by industry. We absolutely do. When we look at the data we use in the models and how they’re applied, understanding how that happens within an industry matters because that’s where the context flows. If we don’t understand what industry we’re doing, like the context is very different in healthcare versus banking versus retail versus hospitality.
So the verticals help us with the context that technology ought to provide a productivity and quality boost for all the companies. And then the way we lead the way is we do it for ourselves first.

Pari: Got it. And for a product company like Amelia, it’s very hard for you to build all of this vertical expertise because the domain capability is different. Are you switching partnerships around that to bring in that vertical know-how, like what’s your partner ecosystem will look like? What is it today? How is it going to look like?

Lanham: We are going to expand it. The reality is partners are critical, you know, in a vertical. And I think this is true and probably for, you know, whether we’re a service company or a product company, I mean, generally speaking, nobody has the whole package. Nobody knows everything. And, you know, as industries develop, expertise matters. And so partnerships really matter. So, you know, for us, I mean, we have advisory partners, we have system integrator partners, we have services partners. All that is critical because, industries do operate differently, a regulated industry versus a non-regulated industry.
There’s a big difference, you know, the government versus business, there’s a big difference, you know, so the reality is partners play a critical role for us, and we are dependent upon these partners. You know, I guess the telecom expression would be the last mile. I would tell you from a product point of view, I think they’re doing more than the last mile. These folks are critical.

Pari: Got it. And Chetan jumping onto the technology side, like one of the concerns people have is around the ethical nature of the AI we are building, the biases which would be built in based on the training data what they’re going to have.
So what are the steps Amelia is taking to ensure that your AI platform is free from biases around race and gender and age and orientation and other factors. Is that something you are focusing at your level, or you think that’s something that has to be done by the base models, which you think you said is getting commoditized? Where does that innovation and focus reside?

Chetan: Yeah, I think Lanham has been a big advocate of that and I think. he has raised the awareness since joining hands with him about, and in fact, the Chairman of AI at Davos also had asked us to be able to focus on ethics and AI and. Actually we need to be able to programmatically enforce good values.
We go to the church ourselves. We need to be able to make sure that Amelia goes to church. Or whichever temple or institution that you fancy, we need to be able to make sure that these good ethics are programmatically enforced. We have from the basic toxicity, we teach our children that toxicity some things and four letter words are not good words to use.

We need to be able to make sure that that basic training to the basic civilities to good behaviours are programmatically enforced in the fabric of these technologies. It’s very important right now. We are getting to that very interesting point in time when it could go one of the two ways. I was walking my son down the driveway to collect the newspaper and he turned to me and he asked me, ‘Dad’, he said, ‘are you going to be a robot dad?’ That’s a very scary thought.

You really want to try and make these systems be serving humanity to be able to get a much better life for humanity. And to do that, we need to make sure that these systems have got good ethics, good values and we are very, very serious about making sure that every module of Amelia starts to have that ethical compass and we maintain that ethical barometer that we are always going towards the side of good as opposed to straying towards the side of what might be bad, whether it’s business value, bad or societal bad, we need to be able to make sure that we enforce that. And it’s very important that all AI systems and that there is an AI consortium that Musk has also advocated about the fact that we teach these systems the good ethics.

Pari: And this is going to be different for different countries, a different perspective on what is ethical and with human evolution, and that has been changing even within same cultures. How is that, you know, what would be the role of sovereign compliance and then how would you ensure that the products are kind of compliant across all of these different as you become a more global company?

Lanham: When we think about markets, up to this point in time, markets are primarily comprised of people. What we were talking about earlier in this podcast is having all these personal AIs actually functioning in the market.
I would just tell you as an entrepreneur and, closet technologist here, I just think this market’s going to need help. It’s going to need help just like every other market. And when Chetan’s talking about the need for technologists to embed values and ethics into these systems, I think that’s exactly right.
And your point about ethics and values are different across different societies that’s interesting too. And so what I want to believe is that diversity is a strength, that different perspectives make things better. The rapid development open source software I think is a proof point around the value of communities with diverse points of view in it.
And so my hunch here is that I’m on the utopian side of things. I think there’s plenty of risk in what we’re doing, but I think we will discover a framework and, and I think that framework will help advance this and keep us safe. Nothing’s perfect, prepare for foot faults and, you know, stepping on problems, okay?

It will officially happen. There’s no doubt about that in my mind. But I think we will wake up in the future with this next wave of an amazing piece of technology that human humanity innovated and built, then I think this platform itself will innovate on its own, which will be a pretty darn amazing thing, which is why the regulation and the guidance matters.
We’ll wake up with more jobs on the other side, but things will absolutely be disrupted in the short term. I don’t have any doubt about that. Things are going to change rapidly, but the good news about humanity is we’re pretty darn adaptable. One thing about us throughout different cultures and time periods is man, humanity figures a way to adapt and overcome. I think this will be the same thing for us.

Pari: You have made a major investment and bet on Amelia. And what are the key decisions for you to get into Amelia, and how do you see the future of Amelia kind of grow in and how do you see that shaping the growth of the company, AI shaping the growth of the company’s success?

Lanham: Yeah, so for us at Build Group, Amelia’s an important investment for us, so I moved in with Chetan here for a little while on that deal.
And I think our mission and purpose is pretty straightforward. We think Amelia is incredibly well positioned in the industry. Chetan’s like one of the godfathers running around in AI. And so the company’s got incredible customers, really cool tech.
And we’re sitting here on the launch pad as things accelerate. And so when we think about why we made the investment and what gets us excited, it’s that plus the Amelians, like the people inside of Amelia that work here, they’re uber cool. They’re fantastic people. So our investment thesis is all about, man, we want to have a voice and a role in helping shape this industry, in this market to do great work for customers and employees. I mean, that, it’s really for us, that simple. And I think the role that AI is going to play in the technology itself is how do we apply it inside Amelia. We touched on this a little bit earlier in the podcast around having Amelia run Amelia inside that core. And having the Amelians do those differentiating things that only they can do, that the machine lacks that human touch. or lacks that “I have an outcome” way of thinking. and we plan on being a role model and industry leader in that.

And so we’re grateful to be part of it. I think the timing’s incredible. I mean all of a sudden AI’s on everybody’s mind and here we are inside a company with a leadership position. I think that our belief here is whether it’s an SMB or an enterprise, people want to be safe.
They want to capture the power of this technology. They want to deliver better outcomes to their customers, their employees and shareholders. And I think AI companies specifically are going to help other companies do that.

Pari: Amazing. And we have been tracking Amelia for the last several years and we wish you the best in the investment, so thanks Lanham and Chetan for a very interesting conversation.
We touched upon a wide range of topics in a very short period and now Amelia has done a great job in continuing to be a leader, thought leader and helping CIOs and CEOs, decide on how they need to adopt this. I would say once in a century kind of technology and wish you the very best to continue to lead the industry in driving those.
I had a great conversation with both of you and thank you for tuning in to this episode of Zinnov Podcast. We’ll be back with another episode, another leader soon. Till then take care and stay curious.

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Business Resilience Strategy to Execution: The 70-30 Rule to Generative AI Efficiency Professor Mohanbir Sawhney | Associate Dean for Digital Innovation | McCormick Foundation Professor of Technology; Kellogg School of Management & Sidhant Rastogi | President, Zinnov | Host 03 Apr, 2024

Join Prof. Mohanbir Sawhney as he shares his playbook on Generative AI adoption strategies - how to scale and leverage Gen AI efficiently.

Business Resilience AI Mixtape: Insights from C-Suite Technologists 22 Mar, 2024

Hear insights on AI's potential from industry leaders discussing current capabilities, use cases, challenges around workforce and the human-AI balance.

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