This episode features an interview with Frank Farrall, AI Ecosystems and Snowflake Alliance Leader at Deloitte. Frank has been at Deloitte for over 12 years and has previously been the Global Leader of Consulting Assets and Solutions and the Chief Strategy Officer of Consulting. In this episode Frank talks about how to help your clients become data-driven, AIs impact on streamlining clinical trials, the benefits of moving data to the cloud and much more.
This episode features an interview with Frank Farrall, AI Ecosystems and Snowflake Alliance Leader at Deloitte. Frank has been at Deloitte for over 12 years and has previously been the Global Leader of Consulting Assets and Solutions and the Chief Strategy Officer of Consulting.
In this episode, Frank talks about how to help your clients become data-driven, AIs impact on streamlining clinical trials, the benefits of moving data to the cloud, and much more.
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Steve Hamm: [00:00:00] So Frank Deloitte is a sprawling global organization. And as I understand, it's a global network of firms, wide variety of services, audit, consulting, financial advisory, risk management tax services. It would be great if you could describe the dimensions of the business and how it operates.
Frank Farrall: [00:00:22] Well, so we started as an accounting firm and that's how we help our clients for many, many years. But then as technology became more pervasive and more of a requirement that our clients needed to address roughly about 30 to 40 years ago, we started to focus on providing services that involves consulting and technology. We're now, you know, 400,000 people and in
over a hundred countries. And so what that does is it gives us significant scale. To be able to tackle our clients most complex and challenging problems are their biggest opportunities. And these tend to be, they have a business angle to them. And so we work with
our clients to understand their marketplace, their strategy. There may be a merger or acquisition situation. They may need to understand their tax position, so really helping them with that business piece. But then when it comes to architecting, And implementing technology, then we're able to do that at significant scale. And we think that that's a real differentiator for us because we have the business advisory elements at scale, but then we're also able to implement the technology that we recommend to really be able to holistically solve the client's problems. We think that really differentiates us.
Steve Hamm: [00:01:35] Now it seems like you, you really have a mission of helping clients become data driven companies at the highest level. How do you do that?
Frank Farrall: [00:01:45] Well kind of like what we've been talking about around their business strategy. You've got to understand what is their competitive situation in the marketplace, or if they're a government public sector organization, what are they trying to do with their citizens or programs within their country or state? And. So we actually, because we have such breadth and understanding of strategy, we can determine what is it then that you would do with data. And then what kind of data do you need and what structure does it need to be in? And so a lot of the topics around the business use of data, we, we get them anchored on and it's not, technology's not the tail wagging the dog is what do you need to do with the data to achieve your organizational objectives? Then once you answer that, It's. What is the current state? What data do you have? What kind of systems and technology do you have? Is it moderate? Is it very dated? Is it siloed? Have you moved to the cloud yet? And what we're finding more and more is that. Almost all of our clients. In fact, we did a survey last year. It showed that like 85% of major organizations, organizations that have more than a thousand people are actively involved in a data modernization program. Almost 100% of those programs involve moving to the cloud in some way or another. And so. Once we understand that strategy and what needs to be done with the data. It's what is the appropriate then new architecture. And again, it might be in a merger and acquisition scenario. It may be in a growth, maybe a divestiture type of a situation. So what is the right architecture and getting that right. And then what are the technologies that need to then come into play?
Almost always as a cloud hyperscaler, you know, there's data technology, artificial intelligence technology, and this is where Snowflake comes. And this is why our partnership with Snowflake is growing quite a lot because it, Snowflake is increasing the answer to what does that modern capability that you need as a data platform as you transition your organization?
Steve Hamm: [00:03:49] Now I know you wear several hats there you were the AI ecosystems leader and also the Snowflake Alliance leader. first I want to talk about AI. I mean, it really seems like in the past half decade, AI has just exploded in the corporation. I mean, a decade before that, just the little things around the edges, I think. But in the, in the past five or more years, it's really exploding, lots of different ways, lots of different technologies to, what do you attribute this explosion of use of AI technologies and how are you as a leader at Deloitte? How are you exploiting those.
Frank Farrall: [00:04:29] That's a really good question. And in fact, we've just launched, our third annual state of AI in the enterprise survey. And one of the metrics that we saw is that in the last three years, there's been a 300% growth. And the adoption of AI. And if you look back, when we started the survey, roughly four years ago, only 37% or so of organizations were actively using AI. And we believe that within two years time, that's going to be in the 80, 90% range. So you're seeing. Mass adoption. And in fact, if you look at all the different elements there, that the main elements of artificial intelligence right now, so machine learning, deep learning, computer vision, natural language processing. So many of the organizations that we surveyed in this, it was about, 500 organizations, 3000 executives that participated, you know, roughly 50, because 60% of the organizations were using those technologies right now. But as they look ahead to two years time, it's going to be around 95% of those organizations were using all four technologies. There wasn't one that was, I was falling behind there. And so I believe the explosion it's kind of the, um, it's the next iteration of digital transformation. So if you think about 10 years ago as the mobile phone was new, social networks were new cloud was pretty new. There was this sense that there was a kind of a structural movement or shift in technology around digital that became almost digital transformation, I think became an overused concept, but organizations made big investments. They saw really big results that became, and then now if you look at the current situation with Kobe, Digital transformation is being accelerated, but the way that people work and, and you, um, serve as customers and things like that. So I think what's happening is you're seeing a confluence of technologies. Again, new technologies, like machine learning, natural language processing, but then also, no. Yup. Fantastic CPU GPU growth. So just, you know, your core chips and processors, your image recognition and, language, understanding the software has improved to the extent, to where this technology is now better than what humans can do. And there's, there's probably about 12 technologies like that. The cloud has continued to advance in terms of young native services and the ability to store enormous amounts of data at quite a low cost. The confluence of these technologies and innovations has now enabled very complex. Calculations and algorithms that the things that you need in order to execute on an operational basis around artificial intelligence, those things are now in place. And so, and they're becoming less expensive. And as the technology providers understand how. Customers are using these. They become simpler to use a better interfaces. They have a lower code approach to, to implementation. And so they're much easier to adopt. And then as they are adopted, it creates competitive tension and a requirement for if your organization is going to succeed and thrive. Then you will have to adopt AI too, is the same type of thing with digital, whether it was e-commerce or, you know, back office efficiency or things like that, you know, five, 10 years ago. So I think it's this combination of factors that is really driving investments in the adoption of artificial intelligence.
Steve Hamm: [00:08:05] Now you're Deloitte AI ecosystems leader. What exactly is the AI eco ecosystem and when, when and why was it set up?
Frank Farrall: [00:08:14] well, I actually get a lot of questions internally around what is an ecosystem. And again, I started by talking about the evolution of our firm. We do a lot of work. In fact, the majority of our client projects now are done with a strategic Alliance partner. So somebody likes Snowflake and, it's, it's really only been in the last probably 15 or so years. Where it's it's been like that. And I think we've gotten quite good at managing alliances, but it was something that was not. Comfortable for us starting out and an ecosystem. We talk a lot in the internally as we're doing our strategy and our planning and thinking about our offerings. We talked about ecosystems and ecosystems are basically, it's a, a collection of individual entities that are. Coming together in an environment. In fact, if you, if you Google the term ecosystem, you get a lot of bio biological references, and it talks about a set of species interacting in a habitat. And, it was a lot of tech players. And software providers or services providers, you know, there definitely are a lot of species and we interact in an environment which is the marketplace. And so you've got, some of these really big, complex questions, problems, opportunities that we need to address for clients. They require software that require hardware or access to capability via the cloud requires some of the business smarts we were talking about earlier and the change, you know, a lot of the, how do we bring the organization forward to restructure and to deploy technology. And so. An ecosystem is a collection of all of these different participants. Are actors working together and ideally towards a common goal and common cause I mean, a lot of cases competing against each other, but then also really pushing forward innovation and advancement with the ideally a customer focus.
Steve Hamm: [00:10:10] so let's think about this as a diagram. Is it something you can talk about in terms of layers of technology piling on top of each other? Or is it better to kind of think about it as kind of a hub and spoke and be liquid the hub being the key player in an ecosystem, or does either one of those make sense?
Frank Farrall: [00:10:31] You can actually look at it in, in both of those ways, really effective. When in fact I've, I've seen. You know, architecture is presentations, where that, where that's, what we've done. And, you know, you can almost build up a, a layer cake. I'll use a kind of an analogy to a cake. And a lot of times when I'm explaining you've got your underlying infrastructure need. You've gotta be able to compute and store data. And that's where we've seen the huge growth of the hyperscalers and cloud providers, but by providing mats, and then you've got your, your data storage and, you know, your, your data platforms and stuff like, um, fits in that category within your application providers, which
have business logic. And are are meant to execute a certain functional task or something that might be oriented towards a, a sector of life sciences, healthcare, or banking or something like that. And then kind of wrapped around this is the experience layer. So how does this technology then interface or interact with a person? Is it through an app? Is it through a browser? Is it through some other device now with IOT is that's coming through. And then what are the services around that? And, and you can actually have an architecture. A call center is a really good example where you might have a very large cloud provider hyperscaler involved, and then you might have a somewhat large application provider.
And then a dedicated telepany phone specialist technology provider, and then some very small, and some of the businesses that we work with, they have less than 50 people. And, you know, we've got 400,000 people and some of our cloud partners are just enormous in terms of market cap. And here you got a 50 person business. That is really good at something. So, you know, translating the conversation with the customer in the text to then be able to flag risks or compliance requirements or additional business opportunities like upsell or cross sell, or maybe able to take that text and be able to interact or take that voice conversation and interact with a customer in, through WhatsApp or through a text message or something like that. So, The you get that layer cake and providers, that should be very large to very small, but then also there's a kind of Venn diagram or so where you might have an initiative, it might be an artificial intelligence research or, innovation initiative. And you might have a prestigious university. You might have an open source community. He might have a very large hyperscaler cloud provider services business like Deloitte, a set of startups, like in the previous example. So, Oh, I think both are a really good way to visualize it. And do you think about the diversity of who's involved in it? It makes it really exciting around what you can do from an innovation standpoint, but it's also very complex and tricky to manage
Steve Hamm: [00:13:30] is the cloud really the key to being able to bring all these players together with much less friction.
Frank Farrall: [00:13:38] I believe so. we found that 10 years ago, we started tolook at cloud and thoughts. Maybe it was just going to be for divisional it, maybe or proof of concepts. science projects almost, you know, who would ever put a banking application on the cloud and we'd government ever cloud. And they answer that's comprehensively right now. Absolutely would. And in fact, the, the number one reason that we found in our recent research for organizations to do a cloud migration is actually for enhanced security and data protection. And then that might seem counterintuitive because. Yeah, there's nervousness around, yeah. Keeping data within a country's boundaries and, you know, being worried about your data going off prem, but actually these very large cloud providers, this is the core of their business and their brand. So the investment insecurity and data protection is extremely high. And so what we found is that. A lot of organizations were moving to get that better protection from a cloud provider. And then there are so many now services that are offered in the cloud. As you put your data in the cloud, it comes out of the silos that have existed there for the past, however many decades. So what that does is that opens up a set of possibilities, particularly through application programming, interfaces or API. It allows you then to connect to so many other. Capabilities, whether it's outside your company or, you know, within your cloud instance that, you can really do much more with less cost and integration. And that just opens up a whole new world of innovation and flexibility.
Steve Hamm: [00:15:25] You mentioned a moment ago that ecosystems, one of the, there's a challenge there, which is managing them because very often they're different entities, different businesses or universities or whatever. So you're an AI ecosystems leader for Deloitte. So you got gotta. Both. You both have an internal coordination management role, but also an external role. So if you would, I think it'd be really, it'd be really interesting to hear you talk about kind of your day to day job and how you do it.
Frank Farrall: [00:15:57] Yeah. And what's really interesting in, in my role as the internal piece is completely guided by the external requirements. And I think the way you pull a good ecosystem together is. You've gotta be mission oriented. And so I gave that example of very large providers, very small providers, academia. If you're trying to do something like cure cancer or certain type of cancer or heart disease, you know, that's, that's something that you can really focus on and. What will be required is some research around the disease. There then will need to be some very specific technology application around managing things like clinical trials and, um, understanding the results of, different pharmaceuticals as
they're evolved. And. In order to do this, you have to crunch enormous amounts of data. And so you've got technology and cloud providers and things like that. And so what I've found is that if you can work out what is the market demand and what are the client needs, you then can assemble. If you know what you need. It's almost like a shopping list of things that you need. Now, what I have found to be. Hugely important. This is why we love working with Snowflake because the collaboration requirement is really important and also having an aligned culture. So there's gotta be an ability to not always be the smartest person in the room and to listen. As well as have a point of view and to speak, and then also to share a roadmap and, you know, so you're sharing your intellectual property and that's the basis of your organization's value in a lot of cases. And some of the most tense meetings I've ever been in in my career is where we're talking through a five year roadmap with a really important technology partner. And we're showing our IP and they're showing their IP. We're trying to. Figure out. How do we agree on who's going to build one? And if I give you a secret, something that we think has competitive advantage for our market position, will you share your secrets? And also will we take that forward? So there's, there's a lot of things around governance, culture aligned objectives, and that can be a mission. You know,
something that is important is curing cancer, or it can just be, we want to really drive our business growth. We want to grow our business by. Double digit growth or by a billion dollars or something. And so if there's alignment around that objective, the market space, you're going to be in you're much more likely to have a successful outcome around an ecosystem.
Steve Hamm: [00:18:31] when talking about AI technologies, I mean, there, you talked about the fact that there's so many being used in so many different ways. Can you drill down on an example of a really cool applic AI application that you've been helping? Somebody with that maybe the listeners haven't heard of anything like this before?
Frank Farrall: [00:18:49] Well, one of my favorite examples, and we have this as a case study in our, in our state of AI in the enterprise survey is a company called recursion pharma. And, and what they do is, they're tackling somewhat today's most tricky health problems. So looking at. Cancer treatments, heart disease, lung disease, and things like that. And what they've done is they've built a platform that they basically, they know that they're going to be running hundreds of clinical trials around potential medicines at any given time. And these, these trials are complicated. They're expensive. If they go, well, really great medicines can go out the door and help patients. They don't go well, then that disease continues to be a problem things. And then there's the business, the profitability requirements around, you know, we've made big investments. We need to get the returns. And so what, what recursion has done is worked out that, they can use artificial intelligence. So image recognition to determine if a cell is healthy or not. So few and cells are healthy or not. And so what they're doing is they're taking massive amounts of data from all of the various trials that they're running. And they've got a database of 5 million images of human cells. And what they're able to do is use machine learning to determine the results of the different trials, the cells that are being tested. Are they seeing positive outcomes or negative outcomes, and basically bringing that together and summarizing that. So on a much more rapid basis on a much more economical, so saving millions of dollars on an annual basis, they can run these trials in a way that is relatively automated. It's very intelligent. And I think it's just one of the best examples of. The practical applications of artificial intelligence that I've seen today.
Steve Hamm: [00:20:50] Hopefully they're working on COVID-19.
Frank Farrall: [00:20:53] I don't know if they are, but, but actually more broadly. So in the, in the industry, the various pharmaceutical companies that are working on treatments in vaccines absolutely are using artificial intelligence. And, you know, basically you would not be able if you look at what the news is, reporting. Around the results of potential treatments and vaccines, the timelines that are being considered would not be able, you would not be able to develop those treatments in a timeframe like what we're seeing, if you did not have artificial intelligence and cloud Plex. So the ability to really crunch the data in an expansive way at a relatively low cost. With the deep learning and the machine learning and the computer vision and the artificial intelligence tools to be able to assess that automatically you just, you just wouldn't be able to do that. And so I actually think if you step out of the quite negative pandemic situation that we're in and you look ahead to some of the more chronic diseases we've had in the past, you know, in cancer and, and the, like, I'm actually quite excited about the potential of applying technology. To those problems. And also more broadly, like my colleagues, it's always really fun to see an engagement where you're tackling a topic like this that you know, is really going to do some societal good and it's really going to help people. So, I think that that's a real win-win from a business perspective, but also from a human outcomes perspective.
Steve Hamm: [00:22:21] No that's wonderfully encouraging. It is amazing when you look at the situation and we, and we do have this real crisis around the world with terrible impacts, but just think of this had happened before the internet. I think, I think if we didn't have the internet thing, if we didn't have video calls and also obviously with, with some of these, these cure and vaccine issues, You know, the ability to do something perhaps twice as fast, you know, a lot. I mean, I think part of it is. You know, it takes about, I think it still takes about 10 years to develop a molecule into an approved drug by the FDA. But with, with machine learning, you can find other drugs that are already approved for one thing that actually might have an impact. That's something else that would have been absolutely impossible to do in three months, you know?
Frank Farrall: [00:23:16] Yeah. It may have attributes that, you know, while it isn't 100% the answer, it may have attributes that are 50% the answer. So you effectively start with a significant headstart and you're able to more rapidly do this. And then also. As technology has become more prolific and globalized. You have many more geographies, many more types of organizations. Do you think about the current situation? You have governmental organizations, you have commercial pharma, you have a lot of different companies that are working on this and I'm, without the pervasiveness of technology, you just wouldn't be able to do it. I mean, it would be like 1918 and the pandemic situation that you had there, where you just have to kind of. Push through it at great cost to human life and health, or you'd have to completely shut your economy down, which that's a dramatic impact as well.
Steve Hamm: [00:24:09] You've mentioned Snowflake a couple of times in our conversation today, and I want to get back to that now. when did Deloitte start working closely with Snowflake? What are the kind of the, the initial engagements and how has it developed.
Frank Farrall: [00:24:24] So we've been working with Snowflake for well over a year, and we're really excited about the partnership and what we see with stuff. Like we love the innovation and the fact that, you know, here is a data platform that's born in the cloud, but it's also multicloud. So it sits on top of, you know, the largest providers, infrastructure. And it's got some in terms of the performance and the query speed, the really smart way that, you know, there's the per second meter pricing. The fact that there's so many native integrations, some big announcements that were made several weeks ago. And, you know, w we see in the business intelligence area and artificial intelligence area, no. Announcements and things that are happening, partnerships where, and in fact, the Snowflakes recently moved its positioning from cloud data warehouse to cloud data platform. And we really liked that because we know that it alludes to hopefully be becoming a more pervasive capability. This is why I'm personally
excited with my artificial intelligence role, because in order to enable the artificial intelligence, you've got to have the data modernize. And in fact, over 60% of the people that we surveyed around the state of artificial intelligence said that I've got an issue with data and that's preventing me from adopting AI. And so, you know, with Snowflake, you get the data out of the silos. query speed is really, really fast. So you can. You can use it for things that have almost real time requirements, or where, you know, some of these data sets that we're talking about, there's really expansive data sets, but you don't need to be working with them, you know, on an ongoing basis, you have like a particular problem that you need to solve, so you can run it and then you can shut it down. And we really liked that flexibility. And so, you know, we think that Snowflake is going to be one of our top partners as a artificial intelligence enabler. And also as an important part of cloud modernizations and migrations that our clients are doing and the help with. And so we think that that's going to be the basis for, for quite a lot of growth and that the projects that we're doing right now, they tend to be focused in the life sciences, healthcare and the technology and the banking sectors where. These organizations have a lot of data and they tend to be stuck or not stuck, but they tend to reside in, in legacy databases that are now quite age. They're 12, 15, 18 years old, and they're on prem are not as flexible and very high maintenance costs with that because they tend to be oriented towards a hardware application and things. And so. We really liked the fact that in these business sectors, which are really high priority for us, that's where a lot of our clients are out. There's the ability to move this data, to modernize the capability, and then to enable the transformation that we talked about earlier around artificial intelligence and other things. And so the projects that we're doing right now are mostly helping our clients to do that modernization, move that data out of an old world. Into a new cloud enabled world. And we're really excited about them. What happens next with, you know, basically being able to enable customer data platforms, artificial intelligence capabilities, and, and really help our clients to become more sophisticated about how they use your data.
Steve Hamm: [00:27:51] so what does Snowflake cloud data platform enable your clients to do that they couldn't do or couldn't do as well previously,
Frank Farrall: [00:28:00] Well it's, basically being able to take large volumes of data and get them out of silos in the organization, but at a relatively low cost. So, you know, you already, so the clients that are adopting Snowflake and are modernizing. They've moved to the cloud. And so the, the storage and the compute capability is separate. So you're already paying for that in the cloud provider. You don't pay for that again with Snowflake. And so what, what the clients are able to do once they migrate across the Snowflake is to run really big, expansive queries that they wouldn't otherwise be able to, to do with the constraints of their. Previous system, but again, like only run it when they need to. So if, if you're looking at assessing clinical trial data for a cancer treatment, you don't have to run that every hour. You need to run the assessments and then, you know, feed that back into the trial and things. And so, so if it gives you the ability to do that massive data sets. With very flexible pricing and then the integrations into the business intelligence, the artificial intelligence tools, really give it a lot of flexibility and richness around the analysis that can be done after you've run that. And you have that data.
Steve Hamm: [00:29:13] Hey, what about the data sharing capability either within an organization or between, you know, within a, an ecosystem or you, is that something that's really kind of unique to these kinds of cloud data platforms or.
Frank Farrall: [00:29:28] It is unique and it's, it's fairly early stages. you know, they say that, you know, data is the new oil in the economy. And so, well we're looking at is how data is going to be shared. And in particular, some of these ecosystems that we've talked about and we've, we've tended, I think, to anchor on life sciences, healthcare in this conversation. But if you are a. Governmental or quasi governmental research institution, academia, and then commercial companies and things. Being able to share that data back and forth, particularly around like really complicated and difficult topics is, is really, really important. So being able to do that at speed and flexibility and low cost is something that's really important. And we think that Snowflakes having that ability to do that as a real differentiator, how that. Evolves in an ecosystem standpoint, how you govern that, who owns the data and that circumstance. These are things that are quite difficult and being worked out, but the, the underlying capability is there. And we think that that's a really unique feature Snowflake.
Steve Hamm: [00:30:29] Hey, I do a lot of reading of LinkedIn profiles before these podcasts, and I noticed that you two things, you have an interest in environmental prism. And also I noticed that. He spent a lot of time in Australia before he came back to America here. Now Australia, obviously on the front lines of climate change, fires, the droughts, all that kind of stuff. Do you see a potential for AI to help out slowing or adapting to climate change?
Frank Farrall: [00:31:00] Yeah, and I think it's probably more broadly some of the, scientific topics in the future that we're going to have to deal with. Absolutely. And, and I think to that, the more you can get data-driven. So we've seen this in. In the companies and in business, the more you can align on a single source of truth and focus on the facts and the data, the more effective you can be in addressing a situation. And so if I looked at, I mean, even improvements in agriculture, you think about the innovations that we talked about earlier around. Image recognition. Also, there are now very high resolution satellites that just bring the earth. So you have access to a lot of very good visual data, geospatial data, climates, you know, heat, water, the different migration patterns and things in animals and populations. So what we're going to be, what we're able to do right now, and what we'll be able to do in the future is. Look at these patterns. And, and again, like being able to crunch enormous amounts of data, like weather data is unbelievably intensive. So being able to have the systems and technologies to handle those kinds of use cases means that you will be able to better predict what potential, you know, fire flood. Even, you know, disease related type, um, patterns and topics would be, and you
can break it down so that as you test what potential responses are, you can then more accurately predict what the likely outcome is. And so I think the more that you can apply. Data and science to some of the challenges that we have in the world, the more effective that you're going to be in.
And in some of the personal circumstances I've been in around conservation, that's what we've looked to do is we've looked at images of what is happening to an environment. We've looked at readings around availability of water and, and trends and patterns over time. So then be able to say, okay, well, If we have this type of outcome that we're looking to drive, what are the investments or interventions that we need to make And so I actually, I see data and technology being honest, so really crucial to that.
Steve Hamm: [00:33:23] I want to ask you to be a visionary right here near the end. if you would looking ahead five years or more. How do you see AI transforming business? Because you've done such a great job of kind of describing how what's going on now with the capabilities are now, but look ahead, five years, the economy, even society, how could it be transformative?
Frank Farrall: [00:33:47] So I think you've got two areas where it's going to matter. Most one is efficiency and the other is value add. And I think that people tend to jump in shadows around, AI is going to be disruptive to employment and it's going to take people's jobs. And actually there were predictions about that in the 1950s. That proved not to be true and things. And so what actually think is likely to happen is that a lot of the rote repetitive tasks are going to be automated away. And the result of the work will be as good as it currently is or better. And what it's actually going to do is free up people to do less repetitive, less roadwork and more creative, more satisfying work. And I think, you know, as we transitioned from industrial revolution and people working in, in mass and factories, and then you saw automation, you didn't actually see a net reduction in employment. Well, you didn't have people doing very rote and repetitive tasks anymore and things. And so I think one of the best if I had to five years, which isn't really that far out in the scheme of things, I think that that's going to be, you know, real impact. And then if you think about the. Capability that your phone has now that it didn't have five years ago, whether it's like mapping and location or understanding you communicating with it and serving up ideas and like the photo capability and images. And so the value and, you know, a lot of those capabilities are added for free you know, whether it's entertainment or that type of, you know, support for how you want to operate as a person. I think there's going to be a lot of value add that's going to come through. I think that's, that's probably the, the two biggest angles. I don't know that there'll be flying cars by then. They may be in trials. but I think fundamentally how people operate at work in their personal life. Will be pretty dramatically impacted by AI. Well, I'm more of an optimist. I think that's going to be for the better
Steve Hamm: [00:36:01] I hope you're right. Hey, this has been a great conversation, Frank, really enjoy talking to you. I found your conversation in your description of recursion pharma, what they're doing with the clinical trials and AI. I think that's really fascinating when you think about what they're doing and then the other kinds of things that the pharma, the biotech industry could do or. No. I mean, you look at healthcare and you just see an industry that is ripe for transformation and we need it. so I, I think that's just a wonderful example. And I'm, I'm going to go out and get a, and read your state of AI in the enterprise survey. Now, is that something that anybody can download from the Deloitte?
Frank Farrall: [00:36:46] Yeah, that's on deloitte.com. There's some infographics and there's a executive executive summary of that. So we could, we could get that to you, but, um, yeah, that's a, that's very publicly available
Steve Hamm: [00:36:56] I think a lot of the listeners are going to rush over there and get that because I think that's just the kind of thing, you know? I mean, it's something that business leaders and also technology leaders, I think they all want to know that stuff
Frank Farrall: [00:37:10] That's right there. There's a business and a technology angle and it's written, you don't have to have a computer science degree or to consume it. And I get value from it. So it's, it's written for both business and technology, but we think that, you know, a lot of the public would benefit.
Steve Hamm: [00:37:23] no, that sounds great. Yeah. I think thought leadership, I mean, Deloitte has a reputation for thought leadership. I think it's well known. I think it burnishes the brand. I'm sure it helps with marketing, you know, when you, when you show what you, what you can do, but I think it's a brilliant thing for you guys to share it in this way.
Frank Farrall: [00:37:43] It's the marketing that we like to do. We, we like to have a point of view it's important for our clients and we need to be able to express that. In a way that can be understood. And so that the vast majority of our marketing is focused on that because we think that it's just that important for our clients.
Steve Hamm: [00:38:00] Well, very cool. Well, thanks again so much for being on today. It's been, it's been fascinating talking to you.
Frank Farrall: [00:38:06] Yeah. Thanks for having me appreciate it.