Utilizing Tech - Season 7: AI Data Infrastructure Presented by Solidigm - 3x22: The State of AI and the Enterprise

Episode Date: February 15, 2022

With AI technology changing so quickly, we often need to step back and take a big picture look at the market. In this episode, Manoj Suvarna of Deloitte joins Frederic Van Haren and Stephen Foskett to discuss the many products and applications of AI in the enterprise. It is important for business executives to survey the many ways that AI and data science are being applied across the enterprise and try to find ways to leverage this work in other areas. Deloitte recently conducted a survey of companies around the world to get a sense of the many ways AI is being adopted. 92% of those surveyed said that AI is a competitive area these days, and 83% realize the value of multiple ecosystems. A majority of businesses see AI as a strategic differentiator that they want to invest in, while most of the rest are trying to determine the value of AI to the business. Links: "State of AI and the Enterprise, Fourth Edition"  Three Questions: Frederic: Do you believe AI products and models should be regulated by an independent organization? Stephen: Are there any jobs that will be completely eliminated by AI in the next five years? Amanda Kelly of Streamlit: What is a tool that you were personally using a few years ago but you find you are not using anymore? Gests and Hosts Manoj Suvarna, Managing Director, AI Ecosystems, Deloitte. Connect with Manoj on LinkedIn or on Twitter at @MSuvarna Frederic Van Haren, Founder at HighFens Inc., Consultancy & Services. Connect with Frederic on Highfens.com or on Twitter at @FredericVHaren. Stephen Foskett, Publisher of Gestalt IT and Organizer of Tech Field Day. Find Stephen’s writing at GestaltIT.com and on Twitter at @SFoskett. Date: 2/15/2022 Tags: @SFoskett, @FredericVHaren, @MSuvarna

I'm Stephen Foskett. I'm Frederik van Herren. And this is the Utilizing AI podcast. Welcome to another episode of Utilizing AI, the podcast about enterprise applications for machine learning, deep learning, data science, and other artificial intelligence topics. If you've been listening to the podcast for a while, you've heard a variety of perspectives, a lot of which come from folks who are making hardware and software in the AI ecosystem. But of course, we also like to invite in folks who are implementing AI, or even folks who want to challenge our audience a little bit with questions about ethics and so on. The idea is to give our audience a overview, a high-level perspective of AI in the enterprise today. And that's one of the challenges I think that a lot of people have is, you know, you see the applications that you are trying to implement, but you're not sure how that fits in with how other companies are doing it or what's going on in the industry. But yet it's interesting to hear about that, right, Frederick?

Yeah, I agree. I mean, it's always good to hear from somebody who has done the research and looked at the state of enterprise AI to understand what the market is doing. I mean, everybody we talk to probably has IDs and might be working in a niche market. And having an overview would be a great topic. Yeah, and that's why we've invited on a friend of the podcast today, Manoj Sivarna, who manages the AI ecosystems at Deloitte, to give us that kind of high-level perspective.

Thanks, Stephen. And thanks, Frederick. Happy to be here. My name is Manoj Sivarna, and I lead the ecosystems for AI at Deloitte. And when I talk about ecosystems, I'm specifically focused on the emerging vendors and emerging partners. Clearly, there is a lot of growth in the industry as the market is continuing to evolve. And we see that there are hundreds and thousands of startups out there that are bringing in

new technologies and new innovations into the field. And so my role is primarily incubating some of these partnerships, some of these engagements, and potentially commercializing them along with Deloitte offerings and taking them to our market to help our clients realize their business outcomes with AI much faster. Yeah. Are there any particular emerging markets you see sticking out regarding to AI? I mean, are we talking about the traditional pharmaceuticals and life sciences, or is there something else going on in the AI market? Yeah, actually, believe it or not, AI is becoming more and more prevalent across a variety of different industries and industry sectors. You know, quite noticeably, when you think about some of the initial adopters, or let's say the fast adopters, or pioneers, you see a lot of them in

the, you know, in the healthcare space and the financial industry space. But what we're also starting to see is an evolution of adoption of AI in consumer and retail, potentially in energy. Government, obviously, has been taking a fairly significant role in this space. And so we're continuing to see some of these industries evolve. And clearly, there is more of an understanding as well as a recognition that, you know, solutions can be automated, AI can be used in order to accelerate some of the business transformation

that a lot of clients are driving. Yeah, it must be really exciting to talk to incubating companies, right? It's trying to figure out where it's all going. What are the typical challenges you see with the incubating AI companies? Yeah, quite noticeably, what we have seen is,

obviously, if you look at some historical data, the amount of investments, VC investments that have gone into startups, specifically in the AI space, has significantly grown. You know, there are statistics of, you know, as high as $75 billion invested through 2020 in startups, right? So we're talking about, you know, thousands of startups out there. There are, you know, depending on where they are at the maturity level of the startup, you know, whether they have continued through, you know, their pre-seed stage or, you know,

gone through all the way to series F and maybe pre-IPO, a lot of them have built their technologies, you know, and are bringing in new innovations for different domains of AI, whether it is things like data science or robotics or conversation AI or, you know, related to computer vision. And as they continue to build on those use cases and they start building on their client, you know, client engagements, what we start realizing is how they continue to expand their scope, but at the same time are challenged when it comes to scale. So for example, a small startup may go and engage on a particular use case in a company,

medium to large size company, but given the fact that they're more focused on delivering that technology and being able to implement that, they're not able to look at the big picture view, right? The big picture view could be, this is one project in a department, you know, that has been used by a particular group, but there might be potential applications for other departments in the organization.

And maybe they're connecting to just say, let's say a data scientist or just a data engineer who may or may not be directly tied in the IT group or the business, the line of business. And so I think in those areas are areas that we foresee as potential complements for us as Deloitte because we are engaged at the C-suite, we're engaged at the line of business all the way down to the data teams.

And so we are able to look at that big picture view and being able to plug in the right technology and the right solution at the appropriate level so that we can deliver those business outcomes. And as a result of which, these startup solutions have a higher propensity to adopt and also permeate across the organization. So clearly, you know, we do see a value add that we can bring in. And again, you know, a lot of these startups are trying to prove, you know, the early MVP candidates and building their, you know, their engagements and penetration as they go out for growing their business, right? Going out and getting the next series of funding and growth. But a lot of them have expressed interest that they're focused more on the technology versus the professional services, and that is the value add that we bring into the equation. Yeah, I think talking to C executives is a great way to fully understand

the business behind AI. Now, do you see the incubators kind of working on solving existing problems? Or do you feel that there is a lot more innovation going on where new problems are being addressed by AI? It's a combination of both. And it really depends on, again, as I mentioned earlier, the maturity level of the startup, right? You know, what we're seeing is on the business side. So, you know, the survey that, Stephen, you were alluding to earlier, you know, so Deloitte does this annual survey, and we just recently completed the fourth edition of it just late last year

where we published the results and you know this is a survey that goes out to you know almost about 2,900 business you know line of business as well as IT executives and it's on a global basis you know we we talk to companies all the way from a thousand employees to ten thousand plus you know that are you know maybe you know five hundred000 in revenues all the way up to 10 billion plus. And so we have a wide view of where AI is becoming more adopted. And so when you looked at that, what we realized was that almost about 93% of those surveyed indicated that AI is a competitive area for them, at least for the next five years. And so clearly there is a recognition that AI is becoming more and more important in terms of their business decisions and their day-to-day practices. On the other hand, what we

also recognize is that about 83% of them realize the value of multiple ecosystems, in most cases, two or more ecosystems. And what I mean by that is the partnerships that are being brought together in order to help solve the business problem. A lot of these companies are not doing AI for the sake of AI. They're looking at it for how do they bring in multiple different, you know, technologies and services in order to able, you know, to deliver, you know, the benefits of AI. So when you think about things like cost reduction or complexity reduction or transformation

of a business or some kind of an innovation, that is where AI comes into play. And so, you know, the ecosystem component is encouraging because that is essentially what comes into play. And so, you know, the ecosystem component is encouraging because that is essentially what we're trying to build here as we go connect with these clients, right? Where we're in a position to help them not only identify the path to that outcome, but also be able to be enforced that with the stack that we bring to the table. A lot of these stacks are incubated through existing partnerships or new partnerships that we're bringing to help address that specific problem. So, for example, a solution that we might be putting forward for something like you know, like a, you know, a self-checkout, right, at a retail environment will be totally different than a solution that we will bring to

the contact center of that same company that is associated with customer service, right? And so, you know, getting that right level of, you know, technology, the solution that is vetted by, you know, our practitioners, and then being able to deliver that to the client all the way through implementation, right? Not just the advisory, but the implementation and the operationalization of that solution is where we spend a lot of time. We've certainly heard about shadow AI, and I guess, I don't know what the right way, marooned AI resources where one group will spend their money and buy their own hardware and software and basically hoard it for their own use, even though it's not in use all the time. But of course, it's interesting to hear as

well that the models and the data can also be marooned in a similar way within companies, and that CIOs are starting to take a look at this and try to figure out where this data can be used. But then you bring up another interesting point here, which is that not all of it is useful. So just because it's AI doesn't necessarily mean that it's useful in a different area. So if a company has multiple call centers, then sure, the call center solution is going to be very valuable. But if one of those is internal for employees, and one of them is external for customers, well, maybe the data set might not be as useful, maybe they might have completely different topics. And similarly, if you're doing like point of sale, well, that's kind of hard to use in other areas of the business. So I imagine

that that goes into it a lot as well, where you're trying hard to use in other areas of the business. So I imagine that that goes into it a lot as well, where you're trying to find areas that you can absolutely leverage the same hardware, software, models, data, all that kind of stuff. And yet, try to find areas where that really doesn't make sense. And I imagine too, that sometimes you see maybe inappropriate combinations where somebody is just trying to wedge it in there because they've got it, right? Yeah, no, absolutely spot on, right? So again, depending on, you know, where clients are in their journey of adopting AI, we see a variety of different behaviors or parameters, right? You know, the survey also found out that, you know, roughly about, you know, close to about 54% of these executives surveyed indicated that they're, you know, using AI in multiple

different environments, right? So they have two things going for them. They have multiple use cases, multiple projects, right, that they're being driven using AI, but they're also making significant investments into that effort. And so there is a top-down recognition, right, at the business level, at the company level, that this is something that we want to invest in, in order to help fuel our business, right? What's different in the other, you know, 46%, and even more so in the bottom list that is close to about 30%, 29%, 30%, these are folks that are just getting started in AI. And so they're probably, to your point, they may have a shadow AI organization somewhere, and they're trying to prove out value. value, right? And I think that is the time when, you know, companies have to ascertain

how do they envision, you know, taking some of these projects, which again, a lot of them don't see the light of the day because they don't necessarily get into a productization mode, but how does it benefit and drive those business outcomes? And that's why collaboration between data scientists, between the IT teams and the line of businesses is absolutely critical because, you know, if you go to take on, you know, multiple different projects altogether, you are surely going to fail because, you know, AI has to prove its value in the organization. And a lot of that starts with data, right? Where data is housed, you housed, what kind of data do you have?

What type of the data can be leveraged in order to help drive the business outcome? And what data, like you said, gets archived, right? The challenge that a lot of companies are facing right now is they're suing with data today, right? But while they're doing that, they haven't even tapped into what data that they have, right? Because it's all in multiple different silos.

It might be in legacy data warehouses. It might be in the cloud. It might be sitting in a warehouse somewhere. All of that data, and again, it's different by department. So data modernization itself is a path, right? Cloud is an enabler or an accelerator towards that path. And then once you have all of this data, then looking at what business outcomes you want to drive

and what insights are you going to drive through that data, right? Some of those insights are, you know, based on tools that you can apply and immediately get like a graph database, right? Or a those insights are, you know, based on tools that you can apply and immediately get like a graph database, right? Or a search engine that you can plug in. Others might be things that, you know, as you peel the onion

and as you start asking more questions, now you start digging into, okay, well, why is that pattern that, you know, coming out in that format? And so I think there's a lot of, you know, back and forth that needs to happen as you kind of wrangle the data, if you know, coming out in that format. And so, so I think there's a lot of, you know, back and forth that needs to happen as you kind of wrangle the data, if you will. And then also making sure that the solution, you know, or the project that you're embarking on is not a bespoke project, right? That you can, you're able to scale, you know, take 80% of that tooling and apply it to another business problem

so that you can go, you know, repeat the process and scale across multiple different projects. And at the end of the day, that's when you move from, you know, the so-called starters or the underachievers in our study, as we say, to the transformers or path seekers, you know, the 54% of the people that are further advanced in not only their deployment, but also recognizing the benefits that AI has to offer for their business. Yeah, you talked a little bit about the survey.

I mean, I'm not sure if I got it right. Did you say that 95% of the people interviewed look at AI from a competitive perspective? It is a competitive differentiator. At least that's what they claim to have, that having AI or AI-enabled solutions that drive the business outcomes leads towards competitive differentiation and positioning. And they're certainly looking at kind of increasing that over a period of time.

Yeah. Would you then say that competition is more of a reason for AI than innovation? Or is that a wrong and true statement? I would say it is one of the factors, right? There are multiple factors on why companies go into adopting AI. Part of it could be the fact that, you know, so if you just reflect back on the last two years, with what the pandemic has driven and the fact that multiple companies,

especially, and they're actually across every line of business, not just in healthcare or financial, but also looking at retail and consumer on how they had to retool themselves to get the online commerce going, right? To get enough, you know, capability of chatbots and voice assistants going, because now they are getting an onslaught of it online versus having an omni-channel presence of in-store as well as online.

And so when you think about that transformation that has to happen, that now becomes a competitive differentiator because consumers are going to look at who's giving me the best online experience or e-commerce shopping experience vis-a-vis a retail store or an outlet, right? And who has the best availability, right, in terms of, you know, supply and associated logistics with that. And so when you look at all those characteristics, those tend to be more in a competitive environment of there are five retailers going after the same type of consumer, you know, you obviously know who's going to win that game, because the one who has the closest experience of just as an in-person shopping is going to definitely carry a lot of that weight and so so i think from that perspective you know disruptive environments disruptive markets help fuel that competition and then that accelerates the transformation that these companies have to go through yeah Yeah. Another question I have regarding to the incubators.

We talk a lot about billions of parameters and hardware to process all that data. Do you see incubators kind of coming up with innovative solutions to process large amounts of data with billions of parameters? Or do you see a lot more innovation where millions of parameters will do the trick? Yeah, I think there are areas that we see, let's say, in terms of computer imaging, for example, where in some cases, depending on the complexity of the use case that you're going after. You know, in some cases you need, let's say thousands of images,

but in others where there are predefined models, perhaps a couple of hundred images would help at least get the learning started. And then as you infer more and more data points, you're able to build that knowledge base in order to drive inference, right? There's also limitations in terms of how much data is of label in the domain out there and how much additional data needs to be brought into play

because obviously when you think about AI models without data there is no model. So the more data you feed it helps refine the model further. There are a lot of areas where, you know, some of these incubators and also some of the established players are looking at synthetic data, right? Synthetic data that can be added, that can augment the, you know, the data in real life in order to help reach or achieve a close to the same result, right? And in order to help the accuracy of what AI can deliver. And so there are different incubators that are looking at kind of how do you bring together

real-time data as well as synthetic data in order to help accelerate the outcomes for those AI projects. Yeah, regarding to the incubators, when you talk to incubators, and sometimes you don't see a lot of value, do you recommend them to talk to other incubators where they kind of merge their ideas to create more value? Is that something that's happening or even sharing? Yeah, I think, you know, when we talk about, you know, some of these startups, right, they work with multiple different teams themselves, right, whether it's the academics, whether it is VCs, you know, or perhaps even some of our other partners. And so to a certain extent, there is that guidance coaching that we continue to provide.

But again, we do a fairly extensive process of vetting these startups early on to make sure that they're able to bring differentiation and value add to both of us, right? So that I would say it's a win-win environment because at the end of the day it's all about how do we create value and and help drive the transformation you know in a client project rather than just about introducing new technology right there might be a cool new AI technology out there but if it if it addresses less than one percent

of the business outcome that we are trying to drive, we don't want to wedge that technology in just for the sake of bringing some coolness factor, if you will. And so oftentimes, we would do our fairly due diligence in terms of the ROI that these solutions are able to deliver, the complexity involved in terms of implementing those solutions and how they're able to scale. Because again, as I pointed out, we're not looking for a bespoke solution,

but something that can be industrialized, that can be scaled over a period of time. And a lot of them do realize the flexibility that's needed as we get into, let's say, proof of concepts or early builds so that we can tweak them as we go along in order to deliver the end result for clients. It is certainly a win-win solution when we think about how do we partner and engage with these vendors. On that note, I'd like to kind of follow that thread a little bit more. One of the challenges or concerns, I think, that maybe some data scientists and machine learning experts and so on have is that the business people may not have a realistic view of what AI can bring to the business.

Unfortunately, we've seen that there's a lot of mythologizing and just a lot of science fiction with the general consensus of AI. And I worry, and maybe they worry, that you go to the CIO or the CEO or the CFO or whatever and say, hey, we want to do a machine learning based algorithm or an AI system that can help to improve this function, retail sales or whatever, they might envision some kind of talking robot or they might think that the system can take the place of employees and so that they can cut headcount. There head counter. You know, there's a lot of that mythologizing out there. And I think that you are in an interesting position because

you have spoken with these people directly. So I'll just ask you, how realistic is the understanding of the capabilities of machine learning within the executive community? I think it is, to a certain extent, I think it again comes down to where the client is in terms of the adoption of AI. us here, right, that have invested tens of millions of dollars in order to drive those projects and are well-versed because they're involved in some of the co-innovation, if you will, right, where they exactly know the direction of where the solution could help them solve the business problem. And they're working collaboratively with us, with other technology partners to say, okay, let's build this together. I'm willing to be the guinea pig in order to kind of be the

pioneer, if you will, to lead this technology and lead the solution and be industries first, right, to get there. So we have some of those, and that's what we call them, you know, to be the true transformers in an AI field enterprise. I think when you think about the broad spectrum you know of executives it really depends on you know what business outcome that they're trying to drive with in the short term versus the long run right because a lot of these projects you know once you start going beyond the shiny twice syndrome, you have to start looking at it holistically and seeing, am I doing AI just for the sake of AI because I want to show it to my board?

Or am I truly adding value to the business? Because it's not just about just bringing an AI solution. It does require the underlying elements required, right? There's data involved. There the underlying elements required, right? There's data involved, there's investment in talent, right? That is a huge challenge that a lot of organizations are facing today, which is, yes, they want to achieve AI, they want to do, you know, 100 different projects. But then when you look at, okay, how much investment do they have? How much data do they have? What talent pool they have, right? Because it requires

a different type of different breed of skill sets, whether it is data science, whether it is machine learning, you know, engineers, admins, DevOps teams, a lot of them have to work hand in hand in order to deliver to those business outcomes, right? Because not just a cookie cutter approach where, okay, I'm buying a shelf solution and I'm going to apply it and voila, here comes the data, you know, the results of the other end. And so we try to, you know, work through a lot of those clients, especially in that early phase and what we go through as a strategy advisory factors, right? Where we coach them on what are the elements that are you know required um you know what are the technologies that you know they currently have or or what we can do to or to augment it

you know what are the skill sets that they currently have maybe they might have just one or two data scientists and you know and they want to build this huge um you know ai center of excellence but don't have't have all the necessary people. And so we can certainly come and augment, provide advice, be able to help them implement and also build a center of expertise for them. And so I think it really depends on what is the ultimate direction that a client wants to take. And AI may or may not be a part of the solution, right?

There have been situations where we would go in and maybe it's just a simple analytics exercise, right? Where it doesn't need, you know, deep science or machine learning tools to be applied and we're able to coach, you know, our clients accordingly. Whereas in other cases, it might be more complex than what they had envisioned. And so they might have to tweak their objectives or reset their expectations on what is real and

what is achievable, right? Versus something that will have to be built over a period of time to get there. Yeah. And there certainly is a skill shortage as well and a challenge in hiring. So I wonder to what extent, maybe your survey included this, to what extent practical reality collides with their goals in terms of investing in strategic AI approaches, and yet they can't find people to actually build those for them. Yeah. And in fact, we're seeing a lot of that where, you know, there are multiple, you know, areas that they're driving from a business perspective, right? It could be business transformation. It could be data modernization. It could be, you know, movement towards the cloud. It might be new innovative business models.

And so when you look at those big projects, we start looking at areas where AI could help, right, in order to further accelerate that. And when that happens, that's when we look at, you know, what are some of the existing skill set that our clients have, and what are skill sets that we can bring to the table because we have thousands of practitioners that we have trained internally in order to be able to support a certain technology, a certain solution, or being able to get towards those business outcomes. And then as we continue to build that, we are able to also then partner with other vendors

in order to bring that skill set to our client environments so that we are able to also then partner with other vendors in order to bring that skill set to our client environments, right? So that we are able to help drive that pipeline as the client continues to, you know, not only adopt AI, but also look for a long-term solution on how they address the skill gaps, whether it is upskilling their existing, you know, employees that they have or hiring new talent, right, that they can bring in. But we can certainly help fill in some of those gaps as they plan for that future. So how does the startup become a client of yours? Are you looking and finding incubators or incubators coming to you or is it by referral how does that work and how do

you how do how don't you miss really interesting incubators right it's it's that's missing the opportunity yeah so so actually um you know believe it or not given the fact that you know again Deloitte is well regarded in the industry we have you know thousands of practitioners, we have lots, hundreds of clients that we are working with. When you think about the startup industry itself, there are a lot of different ways in which we engage. tools that we have externally facing where, you know, we publish reports, we publish, you know, blogs, as well as some of the surveys that I talked about, right, I alluded to, where we do have conversations that we drive with, you know, several executives and their board members. And so some of this, you know, is through the Deloitte Institute. In addition to that, we also have relationships with academics as well as the VC community,

where we partner with them. And some of those VCs bring the startups to us, right? To help validate. And we're constantly in that whole sensing, scanning, sourcing mode of looking for new innovative technologies that we can bring to the table. The other two ways are through referrals

that we get through our own practitioners. And part of that is also because they are engaged in a client environment where this particular solution is of use, right? And on occasions we do get our clients referring vendors to us because they have already started to see the benefits and they would like to see more of that deployment and implementation in their environment.

So we clearly have a lot of network engagements with multiple of these parties to make sure that we don't miss out on the next wave of innovation coming in this space. Wow. Thank you. Thank you so much for that. And frankly, I think that this was a really enlightening discussion of the practical situation in business when it comes to AI. So we've now reached the part of our podcast, which when we ask the guests three questions, this is one of our traditional elements where we surprise them with something they don't know they're going to be asked and we get an off-the-cuff answer. We're changing it up a little bit as well. I'll ask a question as well as Frederick and

a third question will come from one of our previous podcast guests. So let's kick this off. Frederick, go ahead. So do you believe AI products and models should be regulated by an independent organization? You know, think do not harm ethical, religion, and reasons, and so on. So good question. I think, you know, when you think about ethics and regulations, you know, clearly we are starting to see that, you know, being played out in the market. You know, when you think about traditional regulated industries, obviously, as they're becoming more and more adopting of AI solutions, there will be some regulations that will come down, right? So that, you know, they prevent bias and fairness, you know, making sure that there's an explainability factor

associated with it. But what we're also seeing is that some of those regulations could then also spill over into unregulated industries, like retail, as I mentioned earlier, where self-checkout is becoming more common. And then how do you make sure that you're catching the bad elements

versus the good ones and making sure that there are minimal the bad elements versus the good ones and making sure that there are minimal false positives in that space. And so I think regulations is a matter of time. It's not if, but when. And clearly there are different approaches where clients are starting to build their own regulatory bodies internally

and others are looking for best practices in the industry to engage. clients are starting to build their own regulatory bodies internally, and others are looking for best practices in the industry to engage. So my question gets back to the question of employment and personnel. Are there any jobs that you think will be completely eliminated by AI in the next five years? Interesting. Yeah, I think I heard about it in one of the recent podcasts that I was listening to, that there's a stack rank of multiple different jobs, which are, you know, more prone to be replaced by machines, you know, versus, you know, humans. I think when you think about, you know, jobs that are more, you know, in the general day-to-day life that are more automated, if you will, right, that, you know, picking up the phone and answering. So think about

voicemail, for example, right, where voicemail is able to do the job of what a receptionist would do back in the days. I think that's the evolution of technology that is continuing to permeate across, you know, across the different, you know, job roles or job types. job types, there will be certain things that we're already seeing out on the horizon that are starting to get automated. Call centers, obviously, we're seeing as some of those jobs get upscaled. So what is changing, though, is that rather than jobs get entirely eliminated, they're morphing into the next evolution of those roles, right? So, for example, you know, again, you know,

talk about grocery stores, right, where you used to have 15 different lines of, you know, checkouts. And now instead of 15, maybe it's a combination of, you know, five in person and 10 self-checkout. That doesn't mean that those 10, you know, checkout jobs went away. They just got repurposed to other things where they're monitoring those checkout lanes. And then perhaps also helping in, you know, shelving or stocking of other things around the store. And so I think what we're going to see is less of job elimination, more automation,

obviously, of certain repetitive tasks, but then also an elevation of those roles or upskill of those roles that will end up helping the business and helping grow the profitability of that business versus continuing job loss or anything of that sort. I think that is what we're seeing out in the market. Thank you for that. And now, as promised, we're going to use a question from a previous podcast guest. Amanda Kelly, the co-founder of Streamlit, has a question. Amanda, take it away. Hi, I'm Amanda Kelly. I'm one of the co-founders of Streamlit. I would like to know, what is a tool that you were personally using a few years ago, maybe you were very hot on, but you find you're not using anymore, and why? Yeah, I think one tool, you know, I can think about, you know,

clearly in my personal life is where I used to be, you know, doing, you know, spreadsheets management of my own personal finances. And, you know, a couple of years ago, I kind of did away with it because now there are automated tools, you know, and apps that you can actually go and put in even to help calculate your network. And it continuously keeps updated where you don't have to manage it. So managing monthly budgets and all those challenges, you don't have to end up because it's fairly automated. And now you're able to spend more time on, you know, less on, you know, getting the right, you know, the receipts and all those

things put together, but more focused on how do you now make those investment choices or where the budgets are going to be applied. So personally, I think it was a valuable transition for me to moving from actual physical work and go towards automation. Well, thank you so much for that. We look forward to hearing what your question might be for a future guest. And if our listeners want to join in,

you can just send an email to host at utilizing-ai.com and we'll record your question. So Manoj, it was a great, great discussion. Where can people follow you and connect with you and where can they find more about this survey? Yeah, sure. So I'm on LinkedIn. You can do a search on me or my name.

You know, happy to connect there. Also on Twitter at msavarna. And in addition to that, you know, we are continuing to publish results of, you know, not only surveys, but also other materials. We also have recently launched an AI dossier, which has up to about 70 different use cases, depending on the type of industry that you're in and the type of applications of use cases that we have gathered or curated based on our engagements in the industry itself. All of this information can be found at the Deloitte AI Institute.

So if you just go into Deloitte and just type in AI Institute, you'll be able to have access to it. Well, thanks a lot for that. And how about you, Frederik? What's new with you? Well, it seems like this podcast is the right time to announce

that I've been working on a stealth startup around data management, obviously driven by AI. So you can find me deep down PowerPoints coming up with pitches. You can find me on LinkedIn and Twitter as Frederick V. Heron, but you won't find anything about the stealth startup on LinkedIn or Twitter yet. And as for me, I'm pretty excited to be preparing for our AI Field Day event, which is coming up May 18th through 20th. If you'd like to learn more about that, go to techfieldday.com. We would love it if some folks in our audience would join us either as presenters or as delegates, or just to tune in and watch. So please do reach out to

at S Foskett on Twitter. Looking forward to hearing from you. Thank you for listening to the Utilizing AI podcast. If you enjoyed this discussion, please do give us a rating on your favorite podcast platform and share the episode with your friends and colleagues. This podcast is brought to you by gestaltit.com,

your home for IT coverage from across the enterprise. For show notes and more episodes, go to utilizing-ai.com or find us on Twitter at utilizing underscore AI. Thanks and we'll see you next time.