Innovating Leadership:
Co-Creating Our Future

Resources:

You can learn more about Suchi’s work at Boston Consulting Group on the firm’s website: https://www.bcg.com. Search for her podcast, In Her Ellement, on your favorite podcast platform; for example, it’s on Podbean at https://www.podbean.com/podcast-detail/n9cty-268d9c/In-Her-Ellement-Podcast.

Our host, Maureen Metcalf, posts a newsletter every week on LinkedIn. You can subscribe here.

She’s also written several award-winning books on leadership development. You can peruse and purchase them here.

Books we’re reading for fun or personal development right now include:

• Three Rocks: The Story of Ernie Bushmiller, the Man Who Created Nancy by Bill Griffith
• The Five Lost Superpowers: Why We Lost Them & Hot to Get Them Back by John Reid, et al
• The Parable of the Sower by Octavia Butler
• The Left Hand of Darkness by Ursula K. LeGuin

Other episodes you’ll enjoy:

– AI, Your Story, & the Professional Identity Crisis with Christopher Washington

– Leading When We’ve Stopped Thinking: AI’s Red Flag with Srini Koushik

– AI at Work: The Human Side of Tech Transformation with Neil Sahota

Guest(s):

Guest(s) Bio:

Suchi Srinivasan is a managing director and partner at BCG and has occupied the role since 2022. Suchi grew up with a dad who worked as an engineer, and vividly remembers the tech he brought home with him from work. After her studies, she went on to work as a software engineer at the former Bell Telephone Laboratories, moving on to Microsoft during a time of huge change and innovation in the company. Suchi is passionate about using technology as a way to solve some of our world’s most complex and pressing challenges such as climate change.

In 2011 Suchi became one of the founding members of the Cleanweb Hackathon, a non-profit community-building event for global entrepreneurs and software developers. The goal was to drive IT-powered innovation and large-scale climate change impact. The event grew into an organization that boasted over 10,000 members across 25+ countries.

Suchi is also an advisor and sponsor for Women In Cloud, a community-led economic development organization taking collective action to generate $1B in new net economic access for women entrepreneurs and professionals by 2030.

Suchi is a seasoned executive operating cross-vertical to help businesses recognize the threats and opportunities brought on by Digital and AI.

She has two decades of experience across Microsoft, McKinsey and Dell envisioning and operationalizing response strategies to build ecosystems, deliver solutions and services, grow customer satisfaction and bring new products to market.

Suchi has 20+ experience in digital, B2B software and hardware. Track record of driving growth, M&A, GTM operations, and partnerships in Fortune 500 and venture-backed startups. Exceptional business acumen, operational savvy, deep intellectual curiosity, and entrepreneurial spirit to lead investors and board members, customer CxOs, and cross-functional global teams in engineering-driven cultures and fast-paced, resource-constrained environments to deliver on ambitious breakthrough initiatives.

Transcript
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Speaker 1 0:00
Many people live in fear of AI, whether it’s from concern about job loss or AI taking control of the world, but today’s guest, Suchi Srinivasan, points out the power AI has to make our lives better, both for our P and L statements and for solving food insecurity and other global issues. When others see doom, but you see opportunity. That’s an abundance mindset. Resilience and abundance are core tenets of the Innovative Leadership Institute’s leadership model. Find out more right after listening to the podcast, of course, online at Innovative leadership.com

Speaker 2 0:40
This is Innovating Leadership, co-creating our future. I’m your host, Maureen Metcalf, the founder and CEO of the Innovative Leadership Institute, where we help leaders be future ready. Helping us in that mission today is Suchi Srinivasan, managing director and partner at Boston Consulting Group, Suchi is an AI in the enterprise expert, podcaster, and board member. Suchi, we’re delighted that you are here with us today to talk about AI and also about your podcast.

Speaker 3 1:14
Thank you, Maureen, for having me on your show. So excited to be having this conversation with you. So, I am a technologist, engineering by training, and have been in the technology space for over two decades now. Have operated in a number of enterprise spaces, spent time at Microsoft, at Dell, back in the day in Bell Labs as well, and now, of course, at BCG. In my current role, I lead AI transformations for clients across diverse verticals, ranging from financial services to energy and renewables, and help them think through the creation of enterprise-grade AI platforms and the human operating model that they need to put in place to go along with the technology.

Maureen 1:59
Let’s jump into the human operating model, that’s fascinating. While the technology is changing very quickly, I hear the range of comments from we have a responsibility to our humans to we have a responsibility to our PNL to maximize economic efficiency. What kind of business models are you seeing with your large clients, and what kind of ethical dilemmas?

Speaker 3 2:26
Oh, that’s a loaded question. Let’s kind of unpack that, and I’ll start with the ethical piece first, and then we can go to the efficiency and the PNL, and the value generation is maybe a better way to think about that. So, from an ethical point of view, I think the enterprises all over the world are really grappling with how to introduce and use and launch this technology at scale across their enterprises in ways that are broadly, we call it responsible AI, right, and responsibility means a lot of things. It means certainly security, you know, if you’re holding on to customers’ private data, you have a responsibility to make sure that it’s, you know, not leaving your enterprise perimeter, that you’re taking care of it, you’re addressing it in a secure way, upholding sort of, you know, all of the contractual obligations. Secondly, another facet of responsibility is not introducing bias. I mean, imagine if you were a bank or a lending institution, and there are biases that actually do exist in this technology, unfortunately reflecting many biases that exist in our real world, around, for example, women and minorities, and you know, other kinds of such broadly, unfortunately, prevalent biases, and these get replicated in things like training data, and therefore in the algorithms. So, how to ensure that those kind of biases don’t perpetuate? Then there are legal issues, right? Like copyright battles, of course, are getting fought out in very much the public eye. You know, the Authors Guild has just launched a class action lawsuit claiming that, you know, they’ve not been compensated for the usage of their works in training for an enterprise to be able to launch an end customer facing product with these technologies. How to ensure that the chain of legal right to use throughout the value chain of that technology and the data behind it exists is a very important consideration. This is, by the way, just the tip of the iceberg. There’s probably 10 other things like this, but it gives you an idea of the responsibility of enterprises today, almost in the wild west of the AI vendor ecosystem, to check for these things is quite a lot. There aren’t established standards and practices. This is all still evolving, so that’s a minefield. And then, on the other hand, you talked about productivity, efficiency. What enterprises are in search office? They’ve spent at this point now actually more than a decade investing in data initiatives. How do we modernize our data? How do we set up our. Data systems, how do we make our data accessible, all in the hope of getting ROI from that data. The promise of the latest wave of AI that has been hitting the headlines is that it offers a faster path to ROI, especially because it is unleashing what we call unstructured data, those textual files, those videos, those chat logs, which every enterprise is rife with. So, there is a path to ROI now. Efficiency is one side of the mirror. Yes, is this automation 2.0 Can you be more efficient with the AI Copilot, no matter what your knowledge workflow is? Of course, it can be possible with a whole bunch of constraints around trust and explainability, but there is another more interesting side of ROI that some of our more forward-looking clients that I work with, I see them exploring quite enthusiastically, which is the disruptive potential to introduce new commercial models, to introduce new products and services and genuinely deliver greater value to their customers, to their stakeholders, and that’s very exciting, because there are things that were, let’s say, economically not possible, or just not doable, which this wave of technology now starts to make possible at a scale and a scope. So many sites to that coin?

Maureen 6:21
Can you share more about the new business models? Because I’m working with clients who are going to use bots for accounts receivable, accounts payable, the more repetitive tasks that they’re automating. To your point, it’s more exciting to explore what would those new business models look like

Speaker 3 6:41
in many industries today, we have companies that are, for example, in the position of selling data, you know, data providers, consulting providers, marketing research providers. They’re a category and an archetype that I will use as an example. Here, in the past, data was sold, perhaps you had API access, but you were in the business of aggregating the data and selling that data, and that’s how you made money. What this technology does now is, guess what, you can deliver more ready to consume insights rather than data, right? Because you can take all that data, imagine sort of putting a copilot on top of that data and saying you can talk to your data, you can chat with your data. So I’m not just going to sell you the data in a package with an API and leave it to you to navigate it, but in fact you can chat with it. Perhaps it can even generate a first draft of the kind of report, whatever your end user persona is. Right, you could be an energy trader, you could be a market researcher searching for innovative trends, things like that. There’s more ability to synthesize and generate a first draft with this kind of generative AI technology, which means you’re reducing the burden of the end user persona, making them more productive, which by definition means two things: you’re more intimately involved in the end user persona than just delivering data, which gives rise to all kinds of interesting possibilities of hyper personalization and new products, new services, and it is a step up in the value chain from where you were before, and now, of course, new billing models, new commercial models, and how you monetize this interaction, how you stay connected all the time, that’s the promise. A version of this exists in almost all industries, and in some of the more even physically intensive industries, like if you take exploration, mining, oil, and gas, even there, there’s a lot of exploration of how can this technology be used, for example, to augment data sets, reduce time for exploration, so you know we’re having a range of fascinating conversations with our enterprise clients on the potential of

Maureen 8:49
this. Yeah, just as you say, mining and oil and gas, the ability to find whether it’s diamonds or nickel for rare earths to locate those efficiently with a higher probability and less physical digging to identify the size of the reserve, so we can make smarter decisions about where do we invest our mining activities.

Speaker 3 9:13
Yeah, you’re right, Maureen. So, if you take the specific example of exploration, you know, not only is it the reduced time cost expense and you know also reduction in the failure rate, so those are business metrics that businesses obviously care about, but it’s also sort of the potential from a sort of more environmental angle of right, I mean the reduced number of failed digs directly is less impact on the environment, right, so there are many such metrics that are both societal as well as business that this can impact, and all of it comes down to there’s a vast amount of data that already most enterprises are sitting on. Unfortunately, it’s very siloed, you know, you can have permitting history of all the past digs, you can have production rates of where. Various types of wells, gas or oil, or whatever, across time, across location, and usually these are very difficult for a human geophysicist to sit and sift through, but that’s what happens today, and to get the aid of piece of technology that can be that assistant, that smart assistant, to be able to help inform better decisions, and then in future there’s much more exciting technical developments further than that on the horizon, but those are still coming up.

Maureen 10:27
Beyond the in-house data, there’s also public data, so satellite data, Google Earth data, different departments of energy, all of that with a super computing capacity would just give us access to computing power, hence insights that just weren’t possible before.

Speaker 3 10:46
Yeah, I think there’s two, three ways to look at it. So, there is publicly available data, which is commonly available to all participants in any ecosystem, and certainly this wave of both the hardware and the software advances in computing are starting to allow us to take that data, but the real value absolutely does come with the marriage of that, if you will, or the combination of that with the in-house proprietary data, and that’s also what leads to obviously differentiated results for enterprises, and also localization, which is pretty important, as you can imagine, in various parts of the world, whether it’s the language, whether it’s the specific local conditions, the combination of that is pretty powerful. And now, of course, we have the computing advances to be able to process this. On that point, maybe I’ll just add one other thing, which is it is true that today’s AI workloads aren’t necessarily the most again computing friendly in the sense that they’re very intensive in their compute requirements, but we do see absolutely a lot of startup venture activity as well as research that is happening, so there’s a lot of pressure to be able to make these systems a lot, lot more price performant, compute performant, so we expect some exciting developments to make them much more efficient in their energy usage and their compute usage over the next few upcoming years. We should also make them accessible.

Maureen 12:12
I was in a conversation with IBM and the Cleveland Clinic, where they will be hosting the first privately held quantum computer in a hospital, the genome mapping that can happen, and then the search for cures. The combination of AI and quantum computing together sounds like it will absolutely change the landscape of human health from a research perspective. It still won’t exercise for me, so I’m still the recipient of have to get off my fanny and go do yoga, or whatever one does.

Speaker 3 12:48
That’s exactly right. We call it the design space exploration, so it could be in healthcare, it could be in semiconductor design, it could be in electrical and electronics design. You know, lots of these design companies, physically speaking, we just didn’t have the compute capacity and the algorithmic know-how to essentially conduct the exploration of 100% of the design space. If you kind of think about it theoretically, out there in the world exists, let’s say, the cure for this autoimmune disease, but the exact protein combination is sitting there in a design space that’s very vast, and so historically we’ve resorted to all kinds of sampling techniques that have progressively gotten better to be able to try to get at that answer, if you will, and again this applies across many industries. I think quantum is yet another development which is allowing us to essentially put more of these resources in very efficient ways with these technologies in ways that human beings just can’t do, and that’s why it’s actually so exciting for humanity, way beyond the productivity improvements. It’s these things that are going to materially allow us to, I think, improve the quality of life and make me optimistic about the application of this technology

Maureen 14:00
of all of the enterprises you’re working with, and it sounds like across a broad range, is there something that is really compelling or exciting to you personally?

Speaker 3 14:10
I think that the common theme I’m actually seeing across a number of these enterprises as they start to scale up their efforts is once they get beyond the excitement of wow, there’s a 30 to 50% productivity gain when you talk to the human workers who are starting to use and get familiar with the copilot. What’s really exciting to me is to watch the reactions of these individuals, of these professionals, because invariably what they say is, wow, you know, let’s say I’m a salesperson, and I had so much administrative workload that I had no time to do the thing that was really generating where I could generate unique value, which was invest in my client relationships, spend more time with my clients, be. Because I had to devote 50 60% of my time to doing researching, inputting things into the CRM system, and etc, etc, right, and so the excitement at the ground floor once you get past the anxiety of the deployment of the technology is palpable at the individual level once they realize how to work with it, which, by the way, is definitely a change management challenge for companies. The second aspect I would say that’s very exciting for companies is I heard this fascinating story of Betty, the call center worker, who is the one who’s been there for 35 years, is able to add a moment’s conversation, able to understand her client’s emotional needs and beyond, sort of the actual issue that’s getting brought up, and the ability of this technology to bring up new employees, lesser tenured employees, to be as empathetic, to be as service-oriented as Betty is, because of the support that the system is able to

Speaker 2 16:00
give,

Speaker 3 16:00
those are the kinds of outcomes, both for the end customer as well as for the human worker. That’s very exciting to be able to bring that to life.

Maureen 16:08
You mentioned the diversity issues, or the bias issues. It sounds like with Betty and with other humans who are aware of these biases, they’re also able to potentially respond empathetically, even as an antidote to some of the bias, while we get some of the bias corrected.

Speaker 3 16:28
If you think about where are their growing investments, there are absolutely growing investments in building smaller, purpose-built vertical specific models, as we call it our domain-specific models, right. So, we’ve all heard about Open AI’s GPT four and their efforts on GPT five. We’ve heard about Titan from AWS and Bard from Google. You know, these are the almost like the trillion parameter, the giants of the LLM world, if you will. And they are, of course, ingesting like the entirety of the internet data, but with that comes unfortunately things like the biases that are resident inherently in the internet, because it’s a reflection of human society and who we are. But there are exciting frontiers that are being created even as we speak inside of these domains, right inside of a financial domain, or inside of a retail domain, or a telco domain, where there are very specific, curated, much smaller size, so we’re talking anywhere from 10 billion to 50 billion parameter models, which are tiny in comparison to the giants that we just talked about, but are then purpose-built to address the nuances, the problems, the context of that domain, and there you have a lot more ability to be thoughtful about the bias, make sure to account for it to build an explainability, because these models have to be trustworthy to have them go back and reference sources for the answers that they give, so there’s in my mind I see a lot of exciting developments over there that also then allow us to go back and address the issues around the ethics that we just started talking about previously,

Maureen 18:05
just to test my understanding. An example of that could be financial services model, where I’m applying for a loan, and the initial bias maybe have men of a certain age, certain education, whatever it was, over time the systems will be trained to also integrate people who over the last 50 years haven’t been the primary wage earners, home buyers, fill in the blank.

Speaker 3 18:35
It’s a good point. So, what we are advising clients, for example, in that particular situation is that all of these models require training data sets. Those data sets are typically coming from historical loan applications, historical approval. So, by the way, if there is bias in the way organizations have operated, it will show, because whatever has been the precedent so far will reflect in the model as well, but with this technology crossover, and with this increased emphasis and visibility of issues like this, there is an opportunity to design guardrails that is actually watching out for things like biases being perpetuated, and to be able to counteract them, because you know you can change inputs into the model, you can influence the training data to be able to counteract the effect of these historical biases and ensure they don’t perpetuate into a new generation of technology that will power, you know, the next few decades of our human lives. And there is a lot of awareness. There’s been a lot of media spotlight on issues such as this, and that’s why we talk to our clients all the time about what is the framework for responsible AI? What are the elements of that? What does that look like specific to you if you’re a bank in the commercial sector, for example?

Maureen 19:48
In some ways, some of these biases may be cleaned up more quickly if they’re being attended to than without AI.

Speaker 3 19:55
That’s the promise, and that’s why it’s really important for leaders. Especially, we would say, you know, not just the C suite, but also all of the leaders throughout the organization who are responsible for implementing these systems, for being the human in the loop feedback providers to be able to be aware of these types of places for feedback and the guardrails that need to be implemented, because they can play their part, and that is the promise, though, because these systems are absolutely going to proliferate in our society, and therefore it’s a once in a lifetime opportunity, almost to not repeat the sins of our past, if I may use a term like that,

Maureen 20:35
Suchi, this circles back to me a little bit to where we started with the human side, we’ve talked about development, we’ve talked about business models, all of that is more on the enterprise side, and yet we still have humans, and these models are, we’re just getting started. I realize for some people this has been decades, for society as a whole this is new.

Speaker 3 20:59
Yes, absolutely, and that’s where we see most of the current enterprises trying to figure out the right operational model and the organizational model to be able to launch and derive value from these models, but to do so in a way that you know is trust-based and is explainable. So, what we see is that the human in the loop, as we call it, is very, very critical, right. The business processes do need to be redesigned to have a bit more resilience, but the human is still there in the loop. That’s very critical. That’s what we tell all our clients, and we help them with that redesign over there to be able to almost – I’ll use a simplistic word here, supervise to some degree the results from the model. We actually did some research here with 1000s of our consultants inside Boston Consulting Group, and we found that for certain business tasks there is a tendency to rely too much on the models, even when the model isn’t exactly giving the right answer, the usage of a model for creative tasks improves productivity significantly, but sometimes isn’t so great for the business test, and that’s why it’s important to have an experienced human in the loop who can course correct and guide as needed with the benefit of their expertise. The other thing that we’re seeing with humans here also is there are companies that previously were not tech-intensive, if you will. Yes, everybody has almost gone to the cloud now, more or less, but still there are industries that are not tech industries, and this latest wave of AI technology is absolutely sort of causing them to take a deep think about, well, what does an enterprise AI platform look like for me? Because I don’t want to develop these things in a way that is creating future tech debt and or is taking my operational costs through the roof, because I’m paying the cloud providers, you know, so much money every month, and so really deeply thinking about the talent that they need, and it’s creating a lot of new opportunity for technologists, as well as humans who know how to work with the technology, to be able to essentially build a new operating fabric for these companies, whether you’re a retailer, whether you’re, as you, as I said, you know, in manufacturing, it doesn’t matter, but everybody is now trying to figure out the right org model, the right operating model, and it is creating a lot of new needs up and down the organization in ways that are sometimes unexpected,

Maureen 23:31
and these I anticipate evolving and quickly over the next decade or decades as the models become more efficient. In our recent book, we talked about AI as an intern, so similar language that there were certainly things that it does, and it does better than I do. I was applying for some award, and it said I was the chief information officer of a local hospital. You know, there are just times it’s wrong, and if I had sent that application in, I would look like an idiot. You don’t even know who I am, and we’ve seen retractions from medical journals and research. We’ve seen legal cases, and I ask it to cite things, but if I don’t go check those citations, I am at legal fault when I publish something, I am by default saying I’ve done my diligence to ensure its accuracy. To me, that’s the biggest caution. We are not yet in a place where it is consistently accurate.

Speaker 3 24:35
That’s exactly right. And I love your description of AI as an intern. We also have AI as the high school student and as the college student, as we go deeper in the enterprise, and the way we think about it is those big, large general-purpose models that are today available to consumers are doing a great job producing the first draft of a marketing material. At a consumer level, we’ve got lovely stories of parents, you. Using it to plan summer camps for their kids, families using it to plan travel, vacation itineraries, you know, those kinds of things. It’s actually brought a tremendous amount of value with what I call the first draft of something in human lives, and you’re right, it’s an intern, it’s an assistant, as you go into more mission-critical workflows inside of the enterprise. I think two things happen. One, there’s more technology investment to fine tune these models with more reliable data, proprietary data, than the general purpose internet data, and that’s what makes it graduate from being an intern to a high school student or a college student, as it gets progressively more fine-tuned, to be of more utility, it’s still an assistant, but at the same time the role of the human, correspondingly, at least for now, is increasing to be able to review those first drafts and to be able to then get it to the place where it is suitable for launch, as you said, in mission-critical situations, it could be a legal brief, it could be an end customer deliverable, but along the way, it has indeed generated productivity savings that the human can put to good use with their uniquely human capabilities, whether it’s creativity or relationship skills or other things.

Maureen 26:18
I’m working with a client right now, a manufacturing company, and we’re talking about add-ins to the ERP system. What’s the seasonality for X products? And then what raw materials do I need, and what’s the seasonality for the inputs, and how do I optimize my supply chain and my scheduling? And that’s always been the role of the ERP, but where does the AI augment with the volume of everything that’s in my ERP and available in my industry, and what’s the weather prediction in my region this year? There’s just some optimization that’s now available. It seems like the large models in an ERP context can give me all kinds of data that just wasn’t possible before.

Speaker 3 27:06
Yeah, they’re actually able to see these patterns. See, this is what we are seeing as well. You know, when you talk about some of the client examples, fascinating examples, similar one around equipment, equipment failure patterns, which has been sort of the holy grail of any kind of a manufacturing shop floor, and there’s actually a plethora of data, there’s maintenance records, there’s historical records when things broke down, in addition to, of course, the actual sensor data that’s spewed out by the machine. What these models are uniquely doing in both these examples, the one that you provided, and the one that I just talked about, is they’re able to take data from a diversity of sources that are measuring different things, like weather with traffic patterns, with usage patterns, in your case, for example, right, and unstructured other data, like, you know, the port traffic blockages, etc. etc. and they’re able to go one level deeper than what a human possibly could to get at the pattern, that’s the magic of what is underlying happening is that these large models are able to uniquely get to patterns that are out of reach for humans, because you know our brains can’t process these three, four dimensional patterns, if you will, across time and space and manufacturer and whatever, so that’s the potential, that’s the excitement to be able to then apply this in combination with a lot of other commonly existing technology, including traditional AI. Once you determine the pattern, then you can forecast it, you can optimize it, you can do actions based on

Maureen 28:39
it. That circles back to the beginning of the conversation around new business models, so if I am the provider of that data, I can now integrate additional vectors to really help my clients see patterns and drive actions differently.

Speaker 3 28:56
That’s exactly right. You become uniquely available to deliver, actually, the pattern, not the underlying data. Actually, that’s the first jump. You’re now delivering something that is more readily usable than before, and in doing so, and then having a ringside seat to how your customer or your client is using the pattern intelligence, let’s say that you just deliver to them, you then start to observe what problems they are trying to solve with that pattern. Therefore, you have a ringside seat to your future innovation roadmap, because the genesis of the entire sort of business world as we know it is an interconnected ecosystem of solving your customers’ problems, so the more insight you have into what problems they are trying to solve is what fuels your next generation roadmap, and also then economically to be able to do this in a personalized way, so customer A solving different problems than customer B, and previously we had to be constrained by a product that could address as many of the common needs, so you were down to. The lowest common denominator of those needs, but now economically this technology scales at such low marginal cost that that’s the magic of the hyper personalization and the needs, so that’s that’s what’s exciting about the new business models. As you said,

Maureen 30:14
the thing that strikes me is say this manufacturing client is now able to be more efficient, which can then reduce the cost of goods for us as consumers. It doesn’t all go to the providers of capital, but that food may become more accessible, and just the products and services required for humans to live may be more accessible.

Speaker 3 30:39
I love where you’re taking that. I think that’s the big opportunity for humanity, right, and that’s what economists have been recently started talking about, is we may be on the cusp of the next productivity revolution because of this technology, and what that does for us is it releases these resources, this capital that we then uniquely have the opportunity as a species to decide how we want to use that, governments, politicians, you know, leading think tanks are of course figuring out what the new social equation is, because if you’re creating time, where should those human resources go, what’s the next sort of frontier of the civilization, and then, of course, there’s just the fear, as well, of course, of this technology, but if you step back and just purely look at it economically, it is releasing a whole bunch of resources now, and it’s an unprecedented opportunity for us to say, you know, what’s the best use of that? I don’t think we’ve seen anything like that, honestly. The industrial revolution followed by possibly the internet. I mean, this is a movement for sure. It feels as big as that when we talk on the ground to clients and their excitement about

Maureen 31:48
it. One of the mindsets we talked about in our recent book for leaders implementing AI is the distinction between abundance and fear mindset. I’m not saying be delusional and avoid the risks, because we also talk about having a risk management mindset. So, in the context of I’m an astute business person, there are opportunities to solve so many of our problems if we have the mindset that that’s our objective from a government perspective, from a nonprofit perspective, from a business perspective, as we look cross sector, we’ve got some challenges. There’s certainly food insecurity all over the globe.

Speaker 3 32:29
That’s right, but you know, hand in hand with food insecurity, I’ll just pick up on that example. Here is food wastage. The numbers are staggering and shocking, and it happens at every point in the supply chain, from the agricultural producer all the way to the consumer. Actually, inside the consumer’s fridge lets each of us look inside our own homes and say, what is it that went bad and didn’t get used, and so on. And so, what’s exciting to me, and see, again, that’s probably because, you know, why I’m an optimist as well, over here, is there is very much this possibility to solve these big problems that are also those so scattered because they reside in every consumer’s kitchen shelf, but then you can have a player like Samsung, they were in the news recently, a couple of weeks ago, and it caught my eye, where they’re building an ecosystem with these large language models, and they’re building those interfaces into their refrigerators, and so it’s easier to envision a world where you apply this technology in smart ways. The earth is only capable of generating so much, and there’ll be, you know, more technical advances there, but there’s also this huge problem of food wastage, and if we address that, that itself will take care of addressing at least a portion of the problem, and I think that’s very exciting.

Maureen 33:47
Do you have any guidance for leaders to be successful, not only to drive their businesses, but really to make significant societal changes like food insecurity? What are the few things you would advise people to be thinking about, and looking at

Speaker 3 34:04
the number one thing that I would advise is this is a moment for each of us to have an insane amount of curiosity to begin with. It has to fuel this journey, because there is much that we don’t know, and that we have to figure out, but also the technology itself, its almost sudden appearance on the world stage, to some degree, and its intense pace of evolution. I mean, it’s leaving all of us as business leaders very fatigued, very breathless. That’s the common sentiment I encounter amongst executives. So, I do think figuring out a way to get to the basics of the technology and get to what does this all mean. It’s okay if we don’t understand the 10 new vendors that appeared on the space yesterday morning, but just like this is the model coming up to these simple analogies, like it’s an intern, and I think then understanding the implication. On some of the biggest problems that are relevant to your business space, so that curiosity to drive that line of thought is probably the single biggest thing that we can start with as leaders. And then the second thing that I encourage leaders to do is to assemble teams of smart, curious people with complementary skill sets to go experiment, iterate, and learn, because I think it’s going to be very hard to build at scale systems right away. There has to be a period of intense experimentation with the tolerance for, I don’t want to say failure, but you know, with the tolerance for mixed outcomes, and the learning is the real outcome from it. I think doing those two things is going to set up organizations and set up leaders for success more than anything else.

Maureen 35:45
I love the curiosity and experimenting we have talked about innovative leaders having the mind of a scientist. I have a hypothesis, I bring together the smartest people I know in this thing, which may not be my peers at a senior level, it may be up and down the organization across functions.

Speaker 3 36:05
That’s right.

Maureen 36:06
If I am effective at extracting information from everyone, truly collaborating, developing a strong hypothesis, and creating a culture of experimentation, then we can more quickly move forward, understanding that a good experiment is never perfect. It is directionally correct. We iterate quickly, we move forward, and proving my hypothesis wrong quickly is as important as being right, or more important. If I have the wrong hypothesis, I need to know quickly.

Speaker 3 36:38
That’s exactly right. I think the multidisciplinary nature of these working teams is very important, because we don’t understand all of the impacts in all of the parts of the organization, and I think the speed to iteration, as you said, trying to be open-minded, and hence sort of the curiosity, right, not being guided by past biases, but just staying open to the possibility, is going to lay the groundwork for what will eventually then be much more successful deployments at scale, and the realization of value outcomes at scale.

Maureen 37:09
Very much to your point, since this stuff didn’t exist before, my past biases may or may not impact the success. I still have to come in with business acumen and leverage everything I’ve learned to this point, and also recognize some of what I’ve learned will be incredibly helpful, and some of what I’ve learned will derail me.

Speaker 3 37:30
I couldn’t have summarized it better than you.

Maureen 37:33
Thank you. So, let’s move to your podcast. Tell us a little bit about it, and as busy as you are, why this?

Speaker 3 37:40
As we move into a world that is more and more technology enabled, that’s just the fact of life here. We’ve got a huge percentage of our population that are women. From personal experience, you know, with over two decades deep in the heart of the tech industry, I can say that women still don’t have a seat at the table as much as they should have in proportion to how much of the population they are. I still way too often find myself as still the only woman in the room or in a conversation. Sure, Maureen, you can relate to that as well. And we hear that story over and over again, of course, from my podcast guests as well. What is a passion project for me, something that I care about deeply, is how can we pass on these hard-earned lessons, the practical tips, the practical guidance, and the role modeling of the courageous steps that women who are today in senior leadership positions, when they were younger in their career, they had access to perhaps certain opportunities, or they took certain leaps of faith, took certain risks, chose a certain stance, made choices. If we don’t expose the playbook for women leaders, we’re not going to get more of them. There just aren’t enough of us out there to personally apprentice, and so my goal with the podcast is to expose that in an authentic way, these journeys of these courageous women leaders and how they’ve gotten there to validate what you know younger tenured women colleagues might be going through, to give them a playbook and apply it, and at the end of the day to hopefully have at least a small impact in increasing the representation and the voice of women in the technology sector, because it’s really important. We’re solving global problems that affect all of humanity, and we need diversity in the ranks of the problem solvers.

Maureen 39:36
Suji, I’m going to turn the tables a little bit on you, because you’re usually the interviewer asking other women their playbook. Do you have a few things in your playbook? Because you’ve made the point, you and I are often still in 2023 the only women in the room, or in the small percentage of women in the room. What have you done to get there, and for. People who, when they walk in the room, feel disempowered because I’m the only one. What was it that allowed you to say I’m the only one, and I’m going to kick the door down, or I’m going to be polite and go in the room, even though people don’t think I belong here? That takes a level of personal grit, or just putzfa.

Speaker 3 40:20
Yeah, you’re right, and I didn’t probably do this enough, actually, earlier in my career as well, which is why it’s important that you know for listeners of the podcast that I host in her element that were sharing these tips and tricks, right, but for me it became vital to get comfortable taking risks. Okay, my secret tactic is I kind of mentally visualize what’s the worst that can happen. What’s like, you know, just give voice and shape and reality, at least mentally, to the worst possible scenario that can play out. And once you see it, your mind just has had that moment to adjust to it, and then you feel a bit more comfortable taking that risk. I was also very influenced by a mentor I had earlier in my career. She said this very elegantly. She said, “Learn to distinguish the fear that you have. There is the good fear, which is sort of a signal that there is growth at the end of this risk, and there is the bad, bad feeling at the gut, which is that either you know it’s a super toxic environment or this is just not a good step, it’s a mistake. Your gut actually knows a lot, and I’ve learned to tune into my gut a little bit before these big decisions. And then, lastly, I think the importance of role models can’t be overstated. Right, as I started going through, I was lucky enough to have a few really good role models who did exemplify what it meant to be the voice at the table and influencing those decisions, and I think I craved to become like them, and so I hope now to be able to provide that, because it’s become very clear to me that our younger generation needs it. It’s not a complete list, but those are some of the themes we talk about again in the podcast, in her element hosted by BCG as well.

Maureen 42:09
I’m going to turn the tables now on myself and see if I could answer those same questions. What is it that allowed me to stay in those rooms early in my career? I remember a meeting where someone asked me to go get them coffee. Hey, honey, go get me coffee. Now, that was a long time ago, and I was probably called honey, and I was perceived as the errand girl, not an equal peer in the conversation. And in that case, one of my male colleagues said, I’ll go with you. We were peers, and he allowed me to retain a level of dignity, and kind of made the point we don’t do that here to our much more senior male leaders who actually did do that there, so he was showing the aspiration, not the not the reality, and I think just a level of stubbornness that said I’m not going to allow their bad behavior to cause me to lose my dreams. We could now call it grit. I would just say I was headstrong. I was a headstrong kid who climbed trees and fell down and went hiking and came back bruised. That same I’m not gonna let it beat me. Showed up in the workplace,

Speaker 3 43:23
and I think that’s so important. Right, you do need that level of persistence and stamina, you know? You can call it grit. Some of us are fueled by passion for the topic at hand. Some of us think about it quite deliberately and say, you know, how dare this happen? We can’t stand for it, and the other theme we do uncover in the podcast is absolutely the role of male allies, exactly what you just described, the role of male role models. We do see a theme around fathers being very, very influential role models for young girls and setting the path early on, certainly very helpful male allies at work, who can play the role of sponsors and colleagues, and create opportunities, open doors, encourage women to take risks. So, it does take a whole village to get this done. And my only sadness at this is that here we are in 2023 and we’re still talking about this, unfortunately. But you know, I’m an optimist at heart, and we all have to do our part on this, and the podcast is a small step, hopefully, in that direction, contributing.

Maureen 44:28
Thank you for investing the time and energy hosting a podcast. I realize this is not a small undertaking. You don’t do it successfully unless you really have a passion for it, because otherwise it just falls away with we’re too busy.

Speaker 3 44:44
That’s right. Well, time is the limited currency that we all have, a finite time on this planet, and many, many commitments. So we have to pick and choose, as you said, Maureen, right? The passion has to guide us, because that’s when you know we can bring out who we really are. So I’m. Very, very fortunate, very fortunate in the clients that I serve, in the topics that I get to engage my intellectual muscles on, and in the places where I get to exercise my passion, and also very fortunate in seeing the impact of it. I’ve had a number of people come back and tell me how it touched them, and I take those with a lot of gratitude to be able to have that opportunity,

Maureen 45:22
Suchi, you’ve shared so much valuable information. Where would people find more about your AI services at BCG, your podcast, and where would they connect with you?

Speaker 3 45:35
You can find all of the AI work that we do simply by going to bcg.com www.bcg.com and there should be a lot of pointers for AI, as well as generative AI. For the podcast, you should be able to go to your favorite podcast platform and search for the podcast name in her element. Element is spelt as e l l e m e n t, so it’s got a double L, and you should be able to pull up the podcast. We’ve got a couple of seasons already with a number of wonderful speakers and guests that listeners should be able to enjoy.

Maureen 46:11
Where are you posting personally on social media?

Speaker 3 46:15
I’m very active on LinkedIn. I try to share insights that are relevant to all of the current developments on AI, things that are likely to be interesting to enterprise leaders, articles, as well as, of course, links to current episodes of the podcast as they get released. You can go to linkedin.com and search for me by name, Suchi Srinivasan, and be able to find me from the Boston Consulting Group,

Maureen 46:41
Suchi, thank you so much for joining us and sharing your wisdom.

Speaker 3 46:45
And thank you, Maureen, for having me on the show again. It’s been so enjoyable to share these observations and have this conversation.

Maureen 46:52
Thank you. And to our listeners, we hope that the conversation with Suchi helps you remain future ready. And please like and share our podcast. Pepsi

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Speaker 6 48:16
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