43: Using AI for Innovating Global Enterprises with Sanjay Cherian

This podcast episode is sponsored by chiefaiofficer.com offering training and certification through the International association of Chief AI Officers. Interested in a new career or leveling up your value in the marketplace? Chiefaiofficer.com can help. Welcome to this episode with Sanjay Cherian. We had the opportunity to go deep on enterprise level generative AI deployments, which is not something that we normally address here. We're usually looking at the SMB, maybe mid market, but certainly not enterprise. Sanjay had the opportunity for the past two years to be the lead at a company that once he reveals it in the podcast episode, everyone will certainly know that company's name. We cover everything from how he got started, considerations that every business owner should be aware of before they hire quote, unquote AI experts to support their business, and the exact areas where you should be focused on, starting with the introduction of AI into your business. So get ready for a fantastic episode. Let's get started. Welcome to Using AI at Work. I'm your host, Chris Daigle. Each week we'll be learning how today's business owners, entrepreneurs and ambitious professionals are getting more done with smart use of tomorrow's tech. Let's get started. All right. Welcome to the episode. Today I'm excited to, literally, I'm excited to have Sanjay Cherian as our guest. He's the founder of 113 Labs. But as I think, as you'll find out through the rest of our discussion, his experience is applicable to a lot of us. Sanjay and I were introduced through a mutual friend we had at Chief AI Officer. We had done a collaborative product with the folks@growth hackers.com. i met up with the CEO of growth hackers.com recently at a party in Austin for private equity and venture funds that were, you know, just having a social hour. And I was introduced to Sanjay, and when he shared his experience with me, I was like, okay, nice to meet you. And then we further had the the privilege of being able to share some seats on a panel recently with the portfolio CEOs from a private equity firm in Austin, which was a fantastic chance for me to learn a little bit more about what he's been up to. But this is going to be a very interesting conversation that's going to address a level of generative AI in business that we. We really haven't talked about before. So I'm very excited. So, Sanjay, welcome to the call.

Thank you. I'm really glad to be here and excited to have this conversation with you.

Nice. So, you know, before the thing, I Said for sure. Listen, we gotta talk about what you've been doing for the past two years because it's sexy and it's, and it really does establish the credibility of what you're gonna be saying in the rest of the interview. So if you don't mind, like just kind of share what the past couple years, particularly with generative AI has looked like for you.

Yeah, the past couple years I've been at Nike and everybody kind of knows, knows Nike and I've, I specifically was in the tech technology strategy space as global head of technology strategy. And right when I got there, this whole boom happened around generative AI specifically like ChatGPT, you know, in the December of 22, if you will. And I had been playing around with it myself, being a little bit of an early adopter in that space. And interestingly enough at Nike, the CEO was being bombarded with questions from some of his peer CEOs through conferences around this generative AI and what to do in that space. And I took on the initiative of leading our generative AI strategy at charge in that space and, and figuring out now those early days, what do we do? How do we take advantage of this? Is this something that we could take advantage of particularly, you know, across our, our value?

So what was the catalyst for them to get, you know, to, to, to really want to do this? Was it the release of chat GPT 3.5 or had it been prior to that?

I think it, you know, I think it's the consumer, the release of a consumer grade GPT in ChatGPT definitely, you know, spurred it on, but you know, know, I think more so what that ended up doing at the kind of business level at large enterprise levels, CEOs talk to CEOs, boards talk to board members, talk to board members. And you know, there was a legitimately unlike some of the other hype cycles that we've seen with technology like blockchain or NFTs. I think a lot of, you know, operators were talking about how could we actually take advantage of this is something real, you know, and I think some of that came from, you know, can this help with our product innovation and drive, you know, IP drive, you know, greater, you know, value from that perspective. But I think what we quickly pivoted to is looked at is like, how could it drive productivity in enterprises? So we were asking questions around, you know, how does this make our people, our knowledge workers at Nike a force multiplier? At the same time looking at it to say, you know, does it help in our product Innovation process and looking at it from a perspective of productivity at an angle around being a force multiplier. We looked at various areas of the business, everything from product design to software development to supply chain in other areas, and started looking for use cases of where we could adopt this.

I have to ask, did it enhance, was it a force multiplier that you expected it to be?

I think so, very much so. I won't sort of go into like, you know, details around that, but I think that, you know, definitely challenges in that space because, you know, you know, lots of people were using it on their own. But I think, you know, we saw it particularly in areas like in the design space, leveraging design tools and you see it out there in that space. But you know, we very cautious about it too in certain respects, like how would that impact the design process, the innovation process, the ip, if you will, which is still kind of like open questions, I think in the general industry. But yeah. And then came the advent of business corporate functions. How do we use this for the knowledge worker? Are there co pilots that we could leverage from existing partners that Nike had, for example, at Microsoft and leveraging some of that. But regardless of the technologies, like what all companies are faced with is how do we use these technologies to drive more value in every day, how do we make things easier so that people could spend more time on more value added parts of their job? And so how to use AI in the design space to curate ideas but not lose the design and creativity, human creativity in designing a shoe or shirt, for example. Right. And so that's kind of like, I think the frame that, that, that we had and I think many companies are grappling with.

So let me ask, where did you go to learn this stuff?

You know, then thus started a, you know, 18 month dive deep into, you know, into everything. You know, I actually make, I actually draw the parallels to the pandemic. You know, I'm, I'm a, I'm actually funny enough, I've got a background in computer science, but also in molecular biology. And I'm, you know, I took virology courses in university and so I, I kind of like knew what was happening with the COVID pandemic a little bit more than the average person on the street. But at the same time, you know, we all flooded to Twitter, Covid Twitter, to figure out the latest data on, you know, things like that. So similarly, as this was sort of changing on a weekly basis in terms of the advancements of large language models and the accuracy and precision of Them and the new models that were being released by ChatGPT for the consumer. I was basically just enriching myself with teaching myself this right away a number of free courses came out from Google. Talking to people, talking to thought leaders in this space. Having, having been at Nike, we have a lot of access to kind of thought partners from the various consulting firms, but also the various venture capital organizations in that space. And I just, you know, basically steeped myself in everything about this from, from, from technical, as much as technical I could, I could. To kind of business transformation and the kind of results that you could achieve from this.

So, you know, I think anybody that's, that's messing around with generative AI in their business, they probably think that they're behind, right? There's just this assumption that oh, this is, you know, other people are doing, especially with a narrative in the marketplace. What has your experience been with, I guess comparing where you are And I would certainly consider you an expert at it at this point for sure. With the conversations that you're having with. You're more of an expert than you might think you are, trust me, with the conversations that you're having with peers at other businesses at the venture capital. Like, like where are you? Is, are they, are they with it? Are they hip to it? Are they, are they just discovering it? Like kind of where do you see the professionals at?

Yeah, it's a great question. I think, I think that there's, I think there's a, like, like many of these questions. Depends on where you are, what strata of company are you at? You know, where are you in the life cycle, what pressures you're getting from various stakeholders, whether that be your customers or whether that be your board, et cetera. But I think the story on value is very much still being developed. Right? I mean, I think there's a lot of bias and sense for urgency and I think it's important to start experimenting and learning. But by no means I think is the value case clear cut yet. I can sort of put it to, as a technology's changing. If you look at this from a step back and look at it at a macro level, Sequoia has a great white paper on the value of AI and their, their thesis is effectively like, you know, for the investment that we're putting in globally and from a macro level, it's not generating and it's not slated to model out in terms of generating the revenues yet. But you know, I actually think the value of generative AI is more on the productivity and cost saving side. Right now than it is on revenue generation. I think that will come and I think there's an opportunity there. Significant. And as we see the technology change and the models be much more precise and accurate and we move to even autonomous agents and what they call agentic AI, I think we may be seeing more value, but I think the opportunity for this technology in the business space is around driving automation, automation of tasks that we shouldn't be doing in the kind of as a knowledge worker things that should be at our fingertips rather and allowing us to move to more insight and more creativity and more strategy development in these, in these various businesses. And so I think that is the opportunity and I think there's some very clear cut use cases in that space.

So based on your experience and obviously the conversations you're having on a daily basis with others on the journey, what are some areas where you think that most companies should like? It's low risk, low resource requirement to deploy generative AI, high potential impact.

Yeah. So if any of your companies. And look, you have to take what I'm saying in terms of stratification of the company level and size in terms of the value. Right. The more you're spending in these areas, obviously the more value you can go. Some of this and you have to really look at the business case in terms of investments. And by the way, that's a big learning that I had. Like really we, we at Nike looked at across the value chain of Nike and came up with, you know, about a little bit over a hundred use cases. But we really prioritized down to 10 to 15 and then even prioritized more based on investments in business case at that time. And that's something that's dynamically changing in terms of the cost of compute, things like that when you factor that into business case. Yeah, but some areas that are kind of low hanging fruit. Software development seems to have empirical evidence now that you can actually drive savings with co pilots for software development. And we're seeing some of the data in general from the industry come out that senior software engineers are really liking these co pilots. They're helping with generic level 1 type of coding projects and making things go faster. And so that's an area I think right off the bat. A second area I think is I'll say kind of like general knowledge worker productivity and getting first drafts, particularly when you have writing use cases. And you can extend this out to more specialized writing in a business like legal drafting for example, or product briefing drafting. Right. Or marketing brief brief drafting. And the models are Getting quite good, especially when you couple them with training on proprietary data to that company. You can get some pretty good second and third level drafts of writing and that just makes things go a lot faster in a company in an enterprise setting. So those are some areas that I think are pretty low hanging fruit that companies can figure out how to implement some of these technologies. Even, even, you know, even smaller companies leveraging some of these best of breed tools off the, you know, that are coming out right now can, can I think generate some significant, you know, force multiplier type of productivity improvement?

We'll be back after a quick break.

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That's ownerops.com o w n r o p s.com. So obviously you're many steps ahead of somebody who's listening to this and is like, okay, we're ready to do something, we just don't know what. You mentioned that you guys went through a process to develop 100 plus potential pilot projects. What, what would you recommend to somebody who was at that stage to where they're like, we're, we're ready to go. Where do we start?

Yeah, you know, I think there's a couple things. Number one is this shouldn't be a technology led psychological discovery process. What do I mean by that? Typically in an organization, the tech, you know, even though this is very promising technology, in order to get the kind of adoption you need at the end of the stage, you need to really start with some people who understand the business and the, and particularly the business workflow that you're trying to like transform or you know, for lack of a better term, automate. And I think, you know, one of the things that you should do is think about having kind of a squad and empowering from the top kind of a business leader to talk to other business leaders in that space to figure out where to map their business workflows or processes and figure out where can AI or gender of AI really add a lot of value? Where are the hotspots that are kind of where you've got, where you can marry and match the use case if the technology that is coming out there right now. So that I think is a kind of important first step in terms of, and second step of identifying who those business champions are and then spending time with the business to map out what those use cases are. And then you end up coming to a use case and developing a bit of a library. And then it's a situation of understanding across kind of three categories, broad categories in my mind. How you analyze those use cases for value. Is this something where the technology can drive in the three years, can drive productivity improvement. So not necessarily taking costs out, but you know, it'll make that team faster and more productive and allow them to focus on value add. That's one. Is this a case where typically you could actually drive cost savings? You don't need as many people because you've taken that step, you've replaced it with the generative AI or AI and therefore you don't necessarily have that cost anymore. And I've seen this happen in third party costs, particularly as a company outsourcing something that's third party. You can actually drive some of the cost savings that way. And that's hard cost savings. And I think it matters a lot, especially to the company from a labor or SGA perspective. And the third area is revenue increase, right? Revenue opportunities. And this is where I think we're seeing a lot of promise and opportunity, particularly in the digital commerce side, e commerce side, where you've got conversational search that's driven by AI that can be implemented on commerce websites to have better and more accurate search functionality or more virtual conversations with a shopping assistant for example. And those are things that could increase the conversion of customers buying products, buying your products. And I think that's like the third area. It's a little bit, I think it's still immature and I think we're seeing more and more tech every day improve in that area. But I think those are the three things that you would look at and you would assess those use cases along those three dimensions.

Broadly speaking, you brought up a good point. And you know, reflects on my personal experience when I first heard about, I'd heard about chat GPT2 and things like that and I was like, I wasn't that interested, didn't see an application for it. I was a business development professional. I, you know, was operating at a strategic level, not at a tactical. And it seemed like there was a lot of tactical application. So chat GBT 3.5 comes out November 2022 and my first thought was this is going to be A huge opportunity for somebody else. I'm not technical. This is, you know, it's like it's artificial intelligence. And my exposure to it had been in the context of machine learning and data science and things like that and it just wasn't. And it took a minute before I realized that. And I think companies may be seeing that, but my concern would be that companies would say hey Chief Technology Officer, we need you to lead generative AI. And in my, my opinion that's the wrong move. And you kind of referenced that at the beginning of your three points just a moment ago where you said it's not a tech led situation, kind of share, share your perspective on the pros and cons of having a business expert versus a technology expert lead the efforts within a company.

Yeah, I feel pretty strongly on this. I sense, you know, you're in the same ballpark here in terms of thoughts. You know, the hard part in implementing this top technology is sustaining and getting the adoption and usage. And therefore in those three value categories I mentioned yesterday, capturing value, that's still going to require humans in the loop and it's going to require people who leverage and use this. I mean you talked about your, your, your case and as you were talking, I'm thinking around, I felt similarly, but I started using ChatGPT personally to like, you know, make just life more productive, regular life, not more productive. And, and then I, I see that now in my business life also as a, as a tool to make it much more productive. But, but I, but I, but I think that you know, in, in enterprises and companies depending on, despite the scale and size, this is not something that should be driven from, if you are really caring about the end to end value. It's not something that should be driven from the technology organization. I think the technology organization plays a very important part in this because what we haven't talked about is actually the technology to make it happen, integrating it in a safe way, in a secure way, having, choosing the right technologies. But to me it's not necessarily, it's going to be similar to the enterprise software. It's not about choosing the technologies but what outcome do you want for the business? And I think you really need to have something like a chief AI officer who is much more of a, you know, business, who understands business, who understands workflow transformation, who understands kind of value creation cases and a lot is able to mobilize the organization around an initiative that's really going to drive that value. The technology is just, I think that like you know, really accelerates the ability to do this much more so than Enterprise SaaS did for, you know, for enterprise over the last like two, three decades. I think that, you know, and I think if I can sort of expand just a little bit more on that SaaS did, was create, you know, large data sets or systems of record, but it didn't necessarily integrate it all together. And I think now with AI, particularly agentic AI, you have an ability to sort of take your HR data, your finance data, your, you know, services metric, services metrics data, you know, legal data, corporate functions, combine that and have agents communicate with each other. And actually the workflow that you had people, you know, talking to, that, especially that lower level order workflow where people may be in spreadsheets and you know, on emails, that is now, you know, automated. And now you can have different conversations, you know, you can have strategic conversations about finance, strategic conversations about talent management. And I think that's, that's what this technology is able to do. So you do need a technologist to make sure the tech stack is there and that's right. But this is very much a, an initiative that, you know, was going to really drive transformation in the organization that needs to be led by somebody who's a business leader who wonder who has depth of technology, who has, by the way, who has depth of technology, who can call, you know, who can understand quickly and figure out how to translate and work with the CTO's office or the CIO's office to implement that technology that's needed.

That makes sense. So if you were going to go into a company, what would be a department or a role that you might be like? These people tend to be very good at leading that deployment from that chief AI officer kind of paradigm.

Yeah, I think ops person.

Is it a. What is it?

You know, I'm, I'm a little bit of biased. Last 10 years I've been in operator roles. I think, you know, understanding deeply the operations of a particular business function is important. I think that typically what you see at larger organizations is you have transformation offices or transformation people that will be a, a fertile ground to hunt for these people and upskill these people. From an AI perspective, I think the other area is kind of like operations typically in other organizations. Maybe when you sort of go down the stack in strata you have chief operations groups or operations folks. Those are areas where I think they have some technical capability, but they also really know the business very well. They have good relationship management expertise. They're very metric driven and metric oriented and they live and die by those metrics. And I think those are people who can help and be leaders in this new space.

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Technical side of that. Are you saying.

Yeah, like, like what, what, what they would go like I'm not going to know crap on mit. They're fantastic institution. However, their, their generative AI course for, for leaders. My evaluation of it was that it was kind of technical.

Yeah.

And again for me that was a personal impediment. Like oh, I'm not technical, therefore I'm not going to be good at generative AI will come to find out. My understanding of how to, you know, scale businesses was the exact skill set that I needed to be able to learn these things. And, and I'm kind of wanting to address because again, we don't have people at your level on the podcast regularly. It tends to be SMB environment. And for those who are listening that are operating at that enterprise level, I want to do as much as I can to have them have that aha. About like, oh, I can do. Oh, this isn't a big lift kind of thing.

Yeah, I think that, you know, the, the adult learning programs that are coming out now, they're coming out in droves. UT Austin has one that actually is very much affordable compared to the other ones like Duke and mit. Look, I think that if you, you can get really deep into the technicalities of neural networks and large language models and Gan GAN technology and RAG architecture, but at the end of the day, from a business perspective, in terms of what you're trying to do, that level of depth is not necessary to understand when you're working in a team environment. You can lean on your technology colleagues around that. But more importantly, I think you have to understand what your role in that organization is. If your role is to leverage AI to pivot your product in a way that makes sense and, or helps with driving greater growth, you might need to go deep into that technology to figure out where that technology is at what stage, what's coming in order to integrate it into your product. And that's typically a lot of these tech organizations. If you're a digital product leveraging that I think is you may need to get technical. But if you're more on the operations side and you want to leverage this technology because of its promise in transforming your workflows, taking out costs, driving new revenue generation, that level of depth is a little bit shallower on the technology side. And you should really more focus around, well, how are you going to get the adoption, how are you going to, you know, what partner should you choose and what you know, how are you going to remap this process to figure out how you're, how you're going to get the technology to take over some of that process step versus versus the type of technology that you use, that's not really sort of, sort of necessary. So like, that's my take on it.

Yeah, yeah, we're very much aligned on that. Now. One of the things we talked about was that you've got the exciting opportunity in your career to be able to use the word pivot. Right. You're going from an internal role to somebody who is able to advise multiple companies from that more strategic environment. And I'd like to talk about that. I mentioned at the beginning of this, I introduced you as the founder of one one three Labs. Share with me kind of the direction that you want to take with that?

Yeah, absolutely. So 113 Labs 113 is a bit of a riff on the idea of one plus one equals three and that drives synergy. And I think that there's a lot of interest in this space, obviously generative AI, but at the same time I think that it's a bit scary for a lot of people and a lot of companies. And I think that there's a certain level of demystification. It feels like we've been talking for the last five, ten minutes. What kind of expertise do you need in this? And can I do this myself? And having lived this, I think that companies, regardless of your size, don't need to spend a lot of money on expensive management consultants or for that matter expensive technology in order to start getting results here. And one thing that I'm really interested in is how do you upskill people in your organization so that they can start taking advantage of, so that they can lead this? Because what I'm really concerned about about is like how this is going to impact the workforce. And I think that you know, as with many other technology transformations, what we want to kind of like avoid is people not like taking this technology and like looking at the next set of jobs. Because it's not just about reduction in forces that we're going to see. I think we're going to see a whole new type of job take place, humans in the loop. And we're going to be able to like benefit from this because we're not going to be spent time doing mundane tedious tasks. So how do you take the people in those organizations and upscale them and make them self sufficient. And so what we're trying to think about in 113Labs is how do you bring some of the strategic thinking, core sort of strategic skills, apply them in AI and co deliver projects to get squads in these companies actually rolling up their sleeves and doing this work and kind of empowering them with our experiences. And me and two other colleagues have started on the early adoption train in some big brands and we've got results and we're coming together and putting a methodology together to help teams at companies do this themselves and quite frankly not have them relying on us or any other external consultants to make this happen. And that's kind of like the general thesis that's forming right now. And we think we have a pretty unique co delivery approach that will help to sustain the adoption. Because a major part is once you've got the technologies implemented, how do you think about the New world and how do you think about these teams adopting it? And one thing that's really interesting that we've been talking about is this is probably a way to kind of create the next generation of leaders in these companies and, and then that will trickle down into the organization. And that's kind of like our vision. And I think it's a bit part of the purpose of what we're trying to do with 11 3, creating that synergy of 1 plus 1 equals 3.

So for you guys with 11 3, you walk in the door and the first focus is going to be on the strategic upskilling of the decision makers and the executive team.

I think it's first figuring out like, you know, where in the company can we actually deliver value, right? But equipping a set of leaders, much like a squad or a set of business oriented leaders with some technology technical savviness to lead that. Like show them how to create a use case library, show them how to derive an AI business case and make some difficult decisions or simple decisions right away and then help them with the kind of AI upskilling training and literacy and governance that you need. I think this is a new space and I think that there's a lot of early adopter benefit and learnings that these companies can actually implement. And the idea is like, how do you then upskill these individuals and get them into a level of depth in AI where to your point, it's not necessarily, you know, a deep technical knowledge set, but how do they use this and apply this in their everyday work and in the work of their colleagues?

You mentioned a number of times and we haven't talked about it and I know that it's very important, especially for the larger companies who are particularly risk averse, but it's the, the usage policies, the usage arrangements that they've got combined with that training. Do you have a particular, I don't know, perspective on how a company should approach that side of things?

Yeah, very much so. And this, this is where I think it can get technical but, but not necessarily an understanding of the technology in terms of how it works, but rather how it's applied. So for what do I mean by that? There's a lot of open source large language models that companies can avail of and their technology organizations can help get access to. What you want to make sure is that if you're a company that is developing IP and leverages that data or there's strategic IP around your business when you're interacting with these open source large language models, you don't want your data to be committed in the open source. A tenant of open source technology is, it's available to all. But there are ways architecturally, you know, from a technology perspective to create a, if you, a walled garden, if you will, where that, that data is still trained. You're using that data for training, but it's not committed to the open source. So you know, this is, this is, this is what happens technically. But effectively what you're trying to do here is like you trying to look at data and security around that data in a new way, particularly, you know, using these large language open source models. And I reference that because people, companies are not going to be able to invest in creating their own foundational models. Let me rephrase that. Most companies are not going to be able to invest in their own foundational models. And when you want to take advantage of that scale, you have to use something that is cost efficient. And so, you know, the security and creating the right kind of guardrails is important. That's the first thing. The second thing is, you know, teaching your people how to use AI and what data to use in training and whatnot, how to write prompt and what kind of data you should include in prompts and what you should not. And so having the right kind of literacy around AI for the company I think is important because that also will help in the implementation, the safe implementation of that along with some guardrails, but also in more effective use of, of the AI.

So is that something that you guys would assist a company with or do you kind of let them figure that out on their own on the, on the, the guardrail side of things?

Yeah, we, we have starter kits in that space that provide a good kind of starter kit around guardrail usage and prompt engineering, you know, that, that, that, but it's really, you know, empowering these individuals to sort of expand on that in their particular environment and, and build off of it.

And then as far as the AI literacy, what, what time commitment? Maybe not dollar amount for upskilling your, your team, but how much time would you recommend for a client team to spend on a daily, weekly, monthly basis getting the day to day users tuned up on how to use it day to day?

Yeah, this is a great question. I think that we're living in a time where this is not something that's going to be a lunch and learn at a company or necessarily kind of like dedicated training and that's it. This is really about experiential learning. And I think we're going to, if you release this technology in a way that is, you know, with the right kind of security and the right kind of guardrails that we were talking before, I think that the best way to use this is to alert and I think we're going to see new use cases pop up and iterations. And you know what, there's this product brief that I'm using, I'm using this technology to write this product brief. There's probably a better way as if we train this on, you know, the last five years of product briefs, for example, you know, and prompted it this way. So, so that experiential learning I think is much more powerful than didactic learning and training environments that we've seen for regular, for historically, for other software. And quite frankly the user interfaces on these new generative AI technologies and tools are so self learning accessible. Yeah, they're so accessible and self learning. I really, I tell people we're in this 1996 moment and that kind of dates me. I'm thinking about Netscape and the Internet and the accessibility through websites of information. But there's something different here. It's that information's already curated, it's already analyzed, it's already performed, put into a way that makes sense. And then with agents we're actually not doing the kind of lower level work. And so I think this is a very defining moment and I'm one that's very skeptical of a lot of technology hype cycles. But I've seen the value in this and I think that's the learning by doing is very much a tenant here.

I'm particularly interested in how there is some concern by the users about what they might have heard, some doom and gloom stuff. Right. And as somebody like you and I, we're very familiar with these tools and we know that at this stage AI is not marching in like the Terminator and taking jobs. However, people who are just hearing the, the Oprah special with Sam Altman or like, like those sorts of things, there is concern because it's, it's the unknown. It's a new paradigm and anytime there's, there's something like that that shows up, the uncertainty can be unsettling for the, the mass of the teams. Right. How are you suggesting that companies manage the narrative from a change, introducing this change without necessarily causing a lot of fear or resistance?

Yeah, it's a, it's a, it's a great, it's a great question. I think a big, a big issue in enterprises because you know, as I talked about so There, those three, those three concepts of value, productivity improvement, cost savings and revenue generation. There are going to be situations where the old way of doing things is no longer in existence. You're going to have the tech or in some cases robotics or robotics enhanced with AI going to take that over. That is inevitable. I think that that is something that the, that we're going to have to figure out how. I think I'm, I'm one who kind of looks at it from more of a optimistic perspective where you know, how can you use this to create new sources of value? This technology? New sources of value. So far we've been talking about like, you know, automating and taking existing workload and things like that. That's interesting. But I think what's even more interesting is can the technology be used to create new sources of value? New products, new ways of doing things like completely. And it's those areas that people have to kind of move to or migrate to in terms of leveraging and learning and using. And look, we've seen this, we've seen technology shifts and we've seen macro movements of jobs and labor markets move. I think this one has more promise. Let's look at healthcare for example. We are, we are in the midst of a labor shortage. There is not enough nurses to withstand or to be able to support rather the growing and longer living population. Voice, AI and AI navigators to help with navigating people through healthcare options. Voice and AI to help with, you know, answering questions and providing kind of first order level support that you would go to, to a pharmacist or a nurse or a nurse practitioner is a way, is a new source of value and it addresses labor shortages. And I think we're going to see more of that in the market. But you know, there is going to be a lot of change and we're going to see a lot of. It's how you, it's how you approach this, it's your mindset, it's you know, operating. I've got three kids and I'm constantly talking about growth mindsets versus fixed mindsets. And part of that is that mindset and we have to, we have to embody that in our, in our day to day work as well. When it comes to this technology.

Yeah, that's a part of the deployment framework that I think needs to get dialed in because you know, my concern would be that if I were going into a company and I didn't vet the individuals who are participating in the, at the pilot project level for their like comfort level or fear level of AI that I might get some false negatives on the results because an individual who didn't, didn't see it that way, had the fixed mindset versus the growth mindset, might. I mean, they're not incentivized to do what they think might be obviating their role. Right. Like, it's just not necessary anymore. So that's an area that I have a particular interest in. If there was some magic words or something, but I haven't found them yet.

I don't know if there is a silver bullet bullet for that, but in, you know, spending 25 years in the technology space and being involved in digital transformation, you know, one of the tricks, maybe it's an old trick now that I was taught, but one of my mentors is actually, you know, using those individuals and making them the leader of the initiative.

Love it.

You know, it's oftentimes the naysayers are the ones who are, you know, questioning said, well, how would you do it if you wanted to get this kind of outcome and engaging them.

Yes.

In that way and then putting them in that leadership position. Look, you're going to run this initiative or this squad right now and this is what you do. How would you do it? Here's the technology. But again, some of the technology, if we're trying to achieve this as some and you have these enablers, how would you do it? And you know, I've done that before in my career. In fact, I've been the naysayer who was, who was off like that on one of these initiatives. And it starts getting you thinking about in a different, in a different mindset. Right.

I think that's a fantastic. I hadn't even considered that and am eager to test it. So, Sanjay, as we get to the end of this episode, I want to make sure that here's what I know is that there's a lot of companies out there that are looking for advice for somebody just like, hey, just do it right at all levels. Everything from I just need a couple of questions answered to, we don't even want to get into this. You're the expert. Handle it. And they're not having an easy. There's. It's an inefficient marketplace. There's not, you know, an upwork where I can go and I can find the experts necessarily like I could with WordPress or, you know, product design or any of that stuff. What would you, how would you. If you were in a business owner's seat right now, who is interested in doing this at any level, whether it was a small to medium business, mid market, all the way up to enterprise. What would be, what would you look for in that quote unquote expert to come and help you?

Yeah, well, I would look for practical experience like have they done this already?

Sure.

Or you know, versus kind of a talking head and somebody who's kind of on, you know, on Twitter, looking at Twitter. Have they done this before? Have they been in the trenches? The second thing I would look at is, you know, are they proposing, you know, and value right away? Like are they giving you sort of some actionable, you know, ideas to go after? You know, I'm, I'm advising a couple of different boards right now that I'm on particularly on their product and like thinking about this and it's, it's interesting how even like CTOs and CEOs, this is new for them and trying to figure out the linkage of the business value. They're much more focused on like you know, talking to vendors and getting hearing what Microsoft is selling, an AI or Google or aws. But there isn't that sort of linkage. So I would sort of understand like how, how can you like drive operational business value? That was, that's the second thing I would look for. Those two things I think are kind of like for me, if I was in this, if I was a CEO or a board member, I'd be looking to, you know, some level of, of practical experience with this technology. So, so it builds some bona fide. And second, like time to valley. This is not something that should be, you know, a 16 week consulting engagement and you're given a report. Interesting. You need to like, you know, get in there and do this. I would add the third thing. You know, get somebody who is, if you're looking for this help and advice, somebody who can get your people engaged and get a squad or a team around this to start implementing this information is not something that should be kind of housed in some kind of subject matter expert. This is something that day to day and you as an organization have to kind of build this muscle internally and let it diffuse in the rest of the organization. And there's an argument to have a chief AI officer or a squad or a team that will help diffuse that. So I think that's important. Those are three things to look for.

So obviously if those aren't present, those are red flags. Would there be any other red flags that you would advise the business owners to be looking out for for that expert that they're Hiring?

Yeah. You know like red, red flags are kind of like, you know, deep investment in the technology. First I gotta buy the whole tech stack.

Good point.

You know, I've got to sign up for, you know, the, this, these kind of seats. You know, look, I think you're going to have to spend, you know, investment in technology, but I think it's you know, understanding the business value, the you know, having at least a high level case of if we invest this, this is the kind of return we want. And by the way, we got to have those business units on the hook. They got, they got to be bought in. Right? We're going to invest this, we got to be bought in. So you know, as opposed to like, okay, if we can set you up with, you know, a thousand seats with this license of co pilot for this, you're going to see value soon. I, I think that that's, that's a little bit of a, to me.

Yeah, yeah, I heard a, a very similar perspective. Somebody was saying, you know, do is it worth it for us to get all these co pilot licenses? And the answer that I heard that made the most sense was if you don't train the people how to use it, it's not worth it. If you train the people how to use it, heck yeah. It's worth the 20 or 30 bucks per month if they're using it. Right. So that was a fantastic answer. Now for individuals who are like, they like what they've heard and they're not necessarily wanting to, to strike out on their own and figure this stuff out and do what you had to do two years ago and, and you know, get the information wherever you could. If they wanted to reach out and connect with 113 about some solutions. What, what does that process look like for them?

Happy to, happy to connect with them. I can, I can, you know, leave, leave you my contact information and, and, and connect with folks, you know, as, as they, as they please. I'm happy. Look, this is not some kind of like, you know, knowledge that I want to sort of, you know, keep to myself. I think that the more and more people kind of like, and I'm happy to sort of share even my kind of like personal, you know, knowledge gating plan around this and the, the websites and the people podcasts and the, the, the papers that I, I started going deep on. I went, I went through some significant rabbit holes. But yeah, but, but it was very interesting and I think very necessary but happy to share my contact information with your, with your listeners and, and connect for sure.

I'd love to be able to give them that. And also any place on social where you seem to have your or you shine the brightest, I guess I'd love for them to be able to follow you because I think what you're doing, especially from where, where you're coming from with it, like you've been doing this for a couple of years in an environment where it really mattered if you did it right or wrong. Right. It wasn't just an experiment kind of thing, that it had major impact. And there's not a lot of people that, and I talked to a lot of people in this space at all levels. There's not a lot of people who have done that. And you referenced this earlier. I used to be one of those guys at those, at the big consulting firms that got paid a lot of money and I was a little surprised at how much they were charging for how little I knew. That's just the big secret. Right. And I think that it's important for companies to know that if they have the big budgets, you're at least not getting somewhere up that, up that chain of command in those consultancies. Somebody knows something. It may not necessarily be the person sitting in your office today, though, with you guys. That's not the case. It's. You are that person that it would have gotten rolled up to if there was a big situation because you've been there and you figured it out. And that's, that's a, like you said, I mean, the experience that an individual brings to the table on this makes it. The learning curve isn't discovered on the client.

Yes.

It's already been handled. And I think that that's, that's best case scenario.

That's a really important point, what you just said there, especially that learning curve is, is not at the client. And, and because it's so new, like that's, I mean, look, nothing to, I, I used to, I was at Accenture for over a decade and nothing to take away. They do have experts. Exactly. Really good. But this is so new that it really depends on, like you said, who's in front of you and how deep they are gone. And the, the, the, the end value of clients who are deep into this is still very small on the grand scale of things. And, you know, there's, there's ways that you can, you can benefit, you know, in this space without necessarily investing significant amounts in big brand consulting firms. I can tell you, like, this has.

Been a fantastic road.

When we were, you know, when we were deep into this. I, I actually called six or seven consulting partners to come and tell us what they knew about this space because I was developing a primer for on it and you know, it wasn't, it wasn't much different from our teams, you.

Know, at that point in time.

Now look, they've invested a lot. I'm not taking anything away from them. They're great firms, but this is in a new way democratized. The technology democratizes itself for you to use if you've ever used of any consumer grade, you know, AI technology, whether it be perplexity AI or chat GPT or the various now slew of, of software that's available to the students. We haven't even talked about that. But you know, to, to kind of high school and college students, it's becoming very mainstream. Right?

Crazy.

Yeah.

Yeah. The future is exciting.

Yes.

Sanjay, thank you so much for taking the time and I know that you're busy and I'm going to provide all the contact information, information for Sanjay and 113 in the show notes and I look forward to hearing big things about what you guys are up to.

Thanks.

Thank you, everybody.

Thank you.

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