The Future of AI in the Workplace | Special Episode (Mike Sullivan and Vinay Gidwaney)

20:43Vinay Gidwaney:It's kind of an interesting thought experiment because a lot of people, when they, you know, especially educators, they think about kids using AI. We think we're all getting dumber using AI, right? Because the AI is doing all the smart work for us. Well, another way to think about it is that, isn't it interesting that mathematical models behind large language models, amazing amount of compute and technology that goes into that, but basically can fool us into thinking that we're talking to another human? How dumb are we to be able to be fooled by this mathematical representation of language that fools us into thinking that we're conversing with a human being?

21:20Vinay Gidwaney:If you turn that thought experiment on its head, what we can do as humans is now boundless because we have the AI doing stuff that we thought we were really good at. But now we have more time to explore other things, to explore the boundaries of what we can do each individually. And I think that's the exciting view of this. It's a choice to view it that way. It's not to ignore the pitfalls and the dangerous elements of AI and what it means to our society. In reality, while we try to get to that utopia of human intellect and knowledge, it's going to be a really rocky road. And in that rocky road, we need to make sure we're making decisions that put humans first and not just accelerating the technology alone.

22:02Mike Sullivan:Mike, you had that moment of panic about the 25 % workforce reduction. You guys have thought about this a lot since then. And we didn't tell you we were going to ask you this question, but I'm just curious. And I know you're not labor economists. Do you guys still worry about massive workforce decline because of AI? My concern, Ben, is that the issue that Vinay and I are literally immersed in right now is that everyone's sort of looking at this with a lens of jobs. The issue is it's really about the tasks that make up each job. And you really have to deconstruct work and reconstruct work. So if you think about it, every single person that's listening to your podcast today, if they're employed with an employer, they have a job description.

22:50Mike Sullivan:They have a pre-AI job description. And I can almost guarantee you their post-AI job description, there are tasks in that job that would better be done by AI. So there's this pre-AI to post-AI mapping. Vene and I have kind of called it this work genome. We've got to map this genome that says it rotates over here with a different set of tasks. There is a bunch of work. We're just starting a pilot right now where we have four teams that we're looking at this pre and post and deconstructing and reconstructing. deconstructing. We actually have brought in three different academic institutions to really put some academic rigor around what we're doing.

23:31Mike Sullivan:But you know what? It is hard. And if we didn't start out by saying we are a pro-employment, see the faces, not headcount firm, it's easier to cut. It's just easier to say, let's just cut those jobs and then we'll figure out downstream what this looks like. And I think and I worry that many, many firms will take the easy task and that's going to challenge us and it's going to challenge society, I think.

23:57Vinay Gidwaney:In reality, I think it puts a greater emphasis on all of us to choose to believe that humans are capable of doing many different things, even if they didn't have the initial training or education to do that. Because what this is going to come down to at a societal level is a reskilling exercise. There will be more than enough value in the world or problems to solve in the world that we can apply human labor to. I don't think that there's going to be no need for human labor at some point, but we are going to have a very, very uneven way of doing that. And it's going to be very uneven in how we apply it.

24:35Vinay Gidwaney:And that's the key thing that we got to start to work on is where can we apply AI to make a difference? Where can we apply humans to make a difference? And let's actually be surgical about that instead of these blunt instruments that many companies are undergoing right now. So interesting.

24:51Mike Sullivan:So you're saying don't focus on headcount and looking at can we reduce headcount. Look at the tasks that people are actually doing. See where AI can add value and where humans are most valuable. So falling from that, do you think companies cutting headcount to save costs are putting themselves at a disadvantage long term as opposed to upskilling their employees?

25:11Vinay Gidwaney:A hundred percent. Because, again, realize what's going on with these AI companies. There's two things to keep in mind. One is they're very open about the fact that they want to make intelligence a utility, a utility that you have a tap and you just turn it on and off and you pay for the consumption of intelligence. And it's available to everybody like power and water is. If that's going to happen, then everybody's going to have access to the same intelligence. And I go back to the same question. What is your competitive differentiation? What is it that you are doing that's unique? Well, if you look at an established company like ours, been around for 26 years, we spent 26 years handpicking 6 ,000 people to work in our organization.

25:56Vinay Gidwaney:And we spent a lot of time and effort making sure that those people live our values, believe in what we're trying to do, are knowledgeable and have the expertise to serve our clients very well. That's a lot of value. That is the actual competitive differentiation. That is the thing that you need to hold on to. It's the last thing that you want to jettison is your humans, because that's what you spend all this time recruiting and managing and being successful with. You have to retrain them. Those human brains are so much more important and valuable to your organization than those tokens that you're consuming with AI.

26:29Mike Sullivan:I would add to that, Ben, we're not naive enough to say that the challenge that I think AI is going to accelerate is everybody knows there's A and B and C level talent out there. There just is. And we're not sitting there saying C-level talent that's in your company right now doing a C-level job is somehow going to get re-skilled into being an A player doing A things. There are going to be challenges and there's going to be friction in sort of the next five to 10 years as work evolves. But the thing we keep coming back to, and we are living it again today as a company, is that I think at times people are sort of thinking about this like, I'm going to take a snapshot of my company right now.

27:08Mike Sullivan:here's the work that's being done. And we sort of figure out how tasks get realigned. And our view is it's a movie. It's not a snapshot. It's a movie. And we're sitting there saying two years from now, as we redeploy people, and as we redefine and leverage AI in ways we never thought possible, we're going to look back and go, I can't believe that's all we could do for our company, for the people we serve two, three, five years ago. It's amazing what we can do. And it will be that reconfiguration of what people are doing and what AI is doing that's going to transform the way we add value for our clients.

27:46Mike Sullivan:So you got to think about it like a never ending movie where that reshaping and reformulating is going on all the time. And we're only dealing with the models that are out there today. What is this going to look like downstream? So you just have to understand that it's going to be a jagged edge for a long time, but just move, move in a direction and figure it out. Mike, what's human capital management to you? What's really interesting about this, we wrote a book about workforce intelligence. The genesis of that was that Vinay and I were participants to kind of a squabble that was going on inside our four walls with two different teams saying who owns human capital management.

28:26Mike Sullivan:And effectively, it is that organizational structure that every employer has out there that says, this is how we pay people, This is how we organize ourselves. These are the job descriptions. This is how we career path. Everything to do with managing human capital. Vinay and I are watching this email exchange. I sent him a note and I'm going, am I missing something? We are fully engaged with deploying AI into our organization. We're on our 14th coworker that is a fully AI deployed coworker. And we're arguing exclusively about human capital management. Literally, it sort of helped us frame this concept of, yeah, there's human capital management.

29:09Mike Sullivan:There are all the things you need to do to have a great culture and a thriving workplace and pay people and take care of people the way so that they take care of your clients. But in addition to that, there is this intelligence layer that is growing inside your company. And you need a way, you need a discipline to manage and measure that intelligence layer that is the combined intelligence of your human talent and your AI talent. And we have been involved in that exploration and the framing of that concept. But Cam, it came out of this kind of squabble about human capital management. And we're like, there needs to be a layer above that, that is probably the most important part of what is going to define success for companies in the future.

29:56Mike Sullivan:And you need to measure it. You need to manage it and you need to treat it like one of the most, if not the most important asset inside your four walls is this growing workforce intelligence.

30:06Vinay Gidwaney:And let me be clear on something that we have experienced very empirically within our organization and are doing our best to get other people to realize it as well, is that the people that need to lead that effort in your organization is not the IT department. It's the HR team. Because if you look at the evolution of HR over the last 100 years, it started off as people just doing administrative things, like we've got to pay people on time. And then in the 80s and 90s, HR got elevated to the C-suite because we started to realize that human talent was a big asset for an organization. It's where intellectual property came from, and you had to hire the right talent, all that kind of stuff.

30:44Vinay Gidwaney:So it elevated human talent into a strategy, not just a labor force. We got to take the next step of elevation, where it's not just about human talent. It's about artificial talent in your organization. But, and the but's really important, we don't care about the AI. The AI has no feelings. The AI doesn't need to put food on the table or live a happy life. Your humans do. And the people that need to make sure that we make that transition and that elevation from a human-only workforce to a human and AI workforce are the people in your company that are tasked to care about humans. And that, whether you believe it or not, are the people in HR.

31:25Vinay Gidwaney:They have the sole job, beyond the administrative things they do, to make sure that everybody in that company is having a successful career that matters to them and adds value to the company and whatever they're doing for their clients. Those human resources people are so critical to be the ones who transform us through this.

31:46Mike Sullivan:I think you guys both touched on this a little bit and what each of you were just speaking about, but can you talk about why you need a framework for managing that relationship between humans and AI?

31:54Vinay Gidwaney:What it comes down to, and Mike mentioned this, is that you have to sort of double click on what it means to have AI and humans work together and figure out what that actually means. And the structure and framework that we came up is this concept of irreducible and reducible skills. And what we found is that you can break down everything that people do. In fact, I would encourage your listeners to do this for themselves personally, is that if you just simply take inventory of here are the things that make up what I do in my job, frankly, also in your personal life, you can do the same thing. These are the things that are reducible, meaning that they can be broken down to a process.

32:33Vinay Gidwaney:They have clear, defined steps. They may have some magic logic going on or judgment or intuition. That's certainly possible, but I can break it down. Let AI do those things. And it'll only get better at doing those things. Elevate yourself to do more of the irreducible skills. The irreducible things are the squishy things. They're the things that you can't break down. They're the things that you don't know exactly how it works. It just kind of works and you do it. And it comes from your experience and your life and those signals that you're getting. Like Mike talked about, we are signal machines bringing in so much information from our environment and the magic that happens in our brain when we assess something or make judgment.

33:14Vinay Gidwaney:That's the irreducible skills. Find more time to do those. And the key thing is to recognize that if you were to take equal parts, the irreducible skills up here, the reducible skills down here, that the irreducible skills, they don't have a bound to it. They can have an unlimited ceiling. So you can find more things to do that are irreducible because you have more time in your life and in your job to explore that. And that is how we want people to think about this. And like I said, we are actively analyzing every single job in the company and breaking down the irreducible and reducible skills and helping people transition to having more time in their day for their irreducible skills.

33:58Mike Sullivan:I don't know if you guys have looked at our business yet, but to make it more tangible for listeners, if we think about a wealth manager or financial planner job, what would be some examples of irreducible and reducible skills?

34:10Vinay Gidwaney:The wealth manager is a great starting point, and we certainly have. If you think about what a wealth manager does, they're really doing things in two camps from our point of view. One camp is helping you understand where to invest your money, modeling it out, ensuring that the decisions that you make around investments are sound and result in your goals. AI can do that really, really well. It can do all the modeling. It's got access to all the information. It can do all of those things really well. And frankly, in the world of investments, machines have been doing that really well for a long time, quants and so on.

34:41Vinay Gidwaney:They augment human judgment. That's where you get to the irreducible side. It's human judgment that connects the dots between the information and the true desires of your client. What is that person really trying to achieve? How do I help them understand what's possible and not possible? How do I put myself in their shoes and really deeply connect with them as another human being and helping them achieve their mission? Every wealth advisor I've spoken to knows that they have a part of their job that is investments. And then the other part of the job is life coach. And that life coaching part of it is where the human relationships come from and it's frankly where the value comes from in many cases.

35:22Vinay Gidwaney:And so what we would encourage is let the AI help you on the investment side more and more. Spend more time with your clients on the life coaching, on the relationship side. That's the irreducible stuff. And it's by design hard to describe. It's hard to describe you connecting with somebody on where you went to college and talking. Like, that's hard to describe, but that's actually where the value is.

35:44Mike Sullivan:Almost everything that required preparing to engage with your client can be better done by AI and should be done by AI, which means your capacity to take care of more clients should dramatically expand. And in addition to that, if you set everything up the right way, there will be insight that is garnered that you simply did not pick up on your own. But the combination of probably doubling your capacity or tripling your capacity in terms of work and hours in the day that you have more time to be client facing, coupled with there should be insight created that you would not have picked up on your own.

36:25Mike Sullivan:Those two things, I think, end up to a dramatically better client experience. and a more expansive operation for any financial planner that is so inclined. We're living this. One of the reasons I'm excited to be talking to you guys is that we are doing it. Braden from our team was out there visiting you guys as part of this AI transformation program within OneDigital. And he's come back and he's building things that are doing exactly what you just described, Mike. Making it where what used to take hours to prepare for a client meeting can now be done in minutes and can generate new insights that we could not have generated before.

36:58Mike Sullivan:That's the key. Using a combination of basically proprietary database of financial planning strategy descriptions that we've created paired with transcripts from client meeting notes that can now spit out suggestions about what we should be doing for our client. So this is stuff that we were doing before, but now it can be done in minutes or seconds as opposed to hours. Exactly as you just described, it lets us focus on the stuff that matters. And this is what's so interesting. Focus on the stuff that matters to the client. Even pre-AI, we've been surveying clients, asking them about what they find to be valuable about their relationship with PWL.

37:32Mike Sullivan:There's some reducible stuff. It does save them time. They do get good advice. But the biggest thing that always comes up is trust and peace of mind. That's what I'm going to do. And those are irreducible. You can't replace it easily. We're living it. I'm seeing exactly what you guys are talking about in practice. I spent yesterday at an all-day client event. What keeps getting reinforced to me that is so interesting is the psychology of AI and understanding AI and the deployment of AI. But you just mentioned one thing, Ben, that is so interestingly important, trust, particularly in the workplace.

38:09Mike Sullivan:employees are trying to understand what are their employers thinking and can they trust that there is an effort being made to keep me in the game i met with our teams beforehand and then met with clients afterwards and there's almost a generational thing you have older folks that are just like yeah you know that's not my thing you have younger folks that have a complete distrust of AI and they're almost anti-AI. And the conversation was coming up. It had to do with the environment. It had to do with all kinds of things. And I was simply trying to basically say that, look, if you are an AI skeptic, I would basically say you need to read more about what is going on.

38:56Mike Sullivan:There's nothing that is going to change how AI is going to impact the way the world exists, the way work gets done, whatever the case may be. But I think understanding where does trust exist at every employer out there is going to be a really interesting dynamic. When I listen to some of this stuff going on where Square comes out and says we're eliminating 4 ,000 jobs at a time or Facebook comes out and it's 8 ,000 employees at a time, I always find myself thinking, well, what did the 100 ,000 people left there think? And is it simply a matter of time before this gets to me? There is such an interesting dynamic.

39:35Mike Sullivan:And so we spend so much time inside our four walls just trying to basically over communicate about what's going on over communicate about what we're trying to do, and basically get people on board with this whole thing. But it's not without its challenges. Vinay, can you talk about the WI score?

39:52Vinay Gidwaney:So WI referring to workforce intelligence. In deploying AI at OneDigital, as we sort of alluded to a little bit there, we treat AI as if it were talent. So we actually hire the AI into the organization. They have job titles. They have job descriptions. They even have resumes. All of our AI co-workers are managed by human supervisors. there's a whole HR process on how we actually support AI within the organization. And it mimics a lot of how we do that for humans. We even have some co-workers on PIPS, performance improvement plans. They're not quite doing what they were meant to do. And the reason I say all that is that when you take AI and you deploy it like that, and I use the word deploy lightly because it's not a piece of technology.

40:44Vinay Gidwaney:So really, when I say is when you hire AI into your organization like that, then the challenge is I have a lot of my AI doing things and I have a lot of my humans doing things. How do I understand what the best mix between human and AI talent is for the job at hand? And how do I measure whether I'm getting better at that while I'm realizing that what AI is capable of doing is constantly changing. And what humans are capable of doing is constantly changing too. So we sort of sat back and said, okay, well, being numbers-driven, goal-orientated people, let's figure out how to measure this and how to iterate this and get this better.

41:24Vinay Gidwaney:So that's where the workforce intelligence score came from, which is a little bit of a simplification on how to think about this. And we're deploying the use of that score throughout our organization. As Mike said, we've got some academic partners that are helping us understand it more deeply. and the methodology behind it. But we think it's just the beginning of a whole new way of measuring performance, measuring the output of an organization. I think that in the long run, you're gonna have things like NPS scores and predictive intelligence or all these other things that you use to measure how well your company is doing and how well your people are doing.

42:01Vinay Gidwaney:I think workforce intelligence is gonna be another way to measure that.

42:04Mike Sullivan:Can you talk a little bit more about what it's actually measuring and how you interpret the score?

42:08Vinay Gidwaney:The first thing that it's measuring is what we sort of think of as the amplification score. And really, this stems from a basic insight that we had very early on. And I think this actually will resonate a lot for your listeners is that there's really two different types of usage of AI. One type of usage is where it's transactional. It's an answer engine. It's a gopher. It's like, go do this for me. And I'm going to move on with my day. Write me that email that I don't want to write to my kid's teacher. Read this document that I don't want to read because it's too long. That's transactional. And what you're doing in that case is really undervaluing what AI can be doing for you.

42:49Vinay Gidwaney:And you're also undervaluing what you can be doing as a human. And what we try to move people to is the collaborative relationship with AI. A collaborative relationship with AI is where you are conversing with it. You are brainstorming with it. You have a shared goal and you're both working towards that shared goal. And you know it when you see it, where that relationship is there. And I think you can look at individuals in your own personal usage. If you use ChatGPT or Gemini or whatever, you can see whether you're transactional or collaborative. We can actually see it in the data. So we know all of the chat logs that our folks are doing with our AI co-workers.

43:26Vinay Gidwaney:There are hundreds of thousands of them at this point, so we don't look at them by hand anymore. We have another set of AI co-workers that analyze all of this, but they can identify when somebody moves from transactional to collaborative. And that amplification score, we call that, is what drives the calculation. So when you look at your workforce intelligence score, what you're trying to do is to define a work center. So this could be like your client service team or your sales team or whatever it may be. And every work center has output, the number of tickets closed, revenue generated, whatever your measurement is, and the cost of human labor to do that.

44:04Vinay Gidwaney:And what the workforce intelligence score is, it incorporates that amplification score, and it incorporates your token spend. And what it's trying to do is look at a blended view of both how much money you're spending on human labor and the output that you get out of your team versus how much you're spending in the collaborative value of human and AI and how much you're spending on both of those. And to be clear, all of our AI co-workers have a comp plan. They have a salary. I know exactly how much we spend to support any one of our co-workers month to month. And so we can map that out very similarly to how we think about human labor.

44:41Mike Sullivan:Is that just a token spend, the salary?

44:43Vinay Gidwaney:It is a combination of the token spend plus a few other salient points like workflows and other things that we're doing. Yeah. And vectoring of RAG and like all the technical stuff in the background. It's sort of like your benefits plan for your employee. They have a base compensation, but they have health plan, they have insurance, they have other things. It's the same sort of nature.

45:02Mike Sullivan:Wait, so it does include the vectoring of RAG? It does include that?

45:05Vinay Gidwaney:Yes, Michael, it does include the vectoring of RAG documents. It's Michael trying to be smart here.

45:10Mike Sullivan:I'm going to have to add Claude what that means when we get off.

45:13Vinay Gidwaney:Don't worry, don't worry. This language, although it's very technical, it's a little bit of what we all need to get versed in a little bit. And mind you, it's changing very fast. So I wouldn't imagine anybody to get like, oh, I'm going to go learn about RAG because who knows what's going to come out afterwards. But again, if you think about this as a type of labor and a type of talent, there's cost behind it. And you're going to have to understand the components of that cost, just like you understand the components of what it takes to pay a human being to be successful.

45:39Mike Sullivan:Vinay, let me ask you a question about that because you and I have been talking about this lately and I really haven't gotten a refresh from this week and there's been a lot going on. But there's been so much talked about over the past several months about token spend. And you read a headline every once in a while where a company has like an incredible spend in tokens because people were going wild with using AI. But I've kind of come to the conclusion that if you read the tea leaves, token spend based upon the potential introduction of these open source models, is it not everyone's thought that token spend is going to come down in unit cost dramatically over the next year or two?

46:17Mike Sullivan:Or am I not reading that right?

46:19Vinay Gidwaney:That's a really hard question to answer, because if you look at some of the news that came out, even I think it was this morning or yesterday, that some of these models that are being provided by the Chinese labs, they're going to start charging more for it. So if it's open source, you can run it on your own hardware and maybe it's cheaper. I don't think it's really a question of open source, closed source. There's different dynamics to that. The thing that we try to get organizations to realize is that you have to have a lot of agency and control over how you use large language models or any AI.

46:49Vinay Gidwaney:And you have to own the intelligence. In other words, the last thing that you want to do is sign a deal with Anthropic or OpenAI and say, okay, well, everybody in my company has Claude or ChatGPT. Because not only are you saying in that case, well, remember what they've talked about. They want to rent intelligence to you. What if they turn up the price in that rental of intelligence to you like a landlord would raise your rents? What if they say, unless you give me X, Y, and Z, I won't give you access to the smartest intelligence? Fast forward a few years from now where your humans are working with AI every day and the shared intelligence of your organization is between humans and AI.

47:32Vinay Gidwaney:And that company is now holding that over you. We talk a lot about vendor lock-in with data, like all my data is in a CRM and I'm now locked into that vendor. Intelligence lock-in is going to be a whole other problem. So you're going to need to own that intelligence. And part of owning your intelligence is saying, I get to choose which model I want to use for the task at hand, which is also going to help you optimize your spend. Because there's a lot of things that smaller models or cheaper models can do very, very well that you don't need to throw the big models at. So being able to hand select that and control that and orchestrate that is really important, which leads to this issue that if you're going to own your intelligence, you have to work at codifying your intelligence.

48:17What does it mean to write down your intelligence?

48:21Vinay Gidwaney:This is actually what's going on in our organization today, where we are building an intelligence layer, which is being fueled by our humans. Now, don't think of this in the matrix mode, right? All of our humans are just dumping their brain cells into it. It's actually a loop. Those humans get smarter and using the AI, which makes the intelligence better. Now we're in this amazing increasing of intelligence loop that makes our entire organization smarter.

48:47Mike Sullivan:We started this project three years ago. I was like, we need to write down every financial planning strategy that we might implement for a client at some point so we can make sure that we're aware of all those strategies. So we did that. But then more recently, what we've done now, because AI made this so much more interesting, is we wrote down the planning item, what specifically that means the advisor is reviewing. And then we wrote a paragraph explaining why it's important, how we review it, what it means for the clients and all that stuff. So now we have that for well over a hundred financial planning strategies or items.

49:19Mike Sullivan:And we're using that now in conjunction with AI to help synthesize client meeting information and give people advice. Is that kind of what you're talking about?

49:26Vinay Gidwaney:That is exactly what we're talking about. And the key point there is to make that not a thing that you do point in time, but a thing that you're doing always. The other value that comes from this is when you coalesce the intelligence that you've generated there with the data and intelligence about the client. Then the magic happens because now you're applying what you know about your client to that massive amount of intelligence that you've built internally to really personalize and make effective the advice that you're giving and the strategy that you're helping your clients achieve.

49:56Mike Sullivan:The thing that we've most recently created is a thing that takes meeting transcripts and it pairs up those defined strategies with what was discussed in the meeting. It asks the advisor to confirm whether that is the thing that was discussed and it assigns a probability. So it's like there's a 96 % chance this is what you talked about. And the advisor can click yes, goes through everything that was discussed in the meeting. And now we have exactly what you just talked about, the generic explanation and the client specific explanation, which can now be synthesized into financial planning advice.

50:23Vinay Gidwaney:And this is exactly what you're just going back to what your listeners hopefully take away from this is that is exactly what you should be doing for yourself personally. So when you're sitting in front of Claude or chat and you're saying, okay, well, what do I tell Claude about what I'm trying to do? Those are the instructions. You're giving it some intelligence. When Claude does that thing for you or does that thing with you, you're now freeing up time and space for you to expand your own intelligence. Now, the crazy thing is, is that you have the best possible tool known to human beings to expand your intelligence.

50:57Vinay Gidwaney:It's AI. So with that extra time that you have, that you now say, okay, well, AI was doing that for me. I'm going to go learn something deeper or different that's going to expand my career and my potential. AI is a wonderful coach and teacher as well to do that. That's what we call you're activated. And you know it when you see it. Somebody who's truly activated is when they think that way. And it's kind of funny. I have four children from a large age group of 1 to 15. The 15-year-old is kind of activated, but she sort of grew up when she was still Googling and Wikipedia and things. The 11 and 8-year-old, especially the 11-year-old, fully activated.

51:33Vinay Gidwaney:Doesn't even know what Google is. Just everything goes straight into AI every single time because he is constantly talking and collaborating with AI to expand his own mind. That's the new kind of workforce we're looking at. The one-year-old doesn't know what he's doing yet.

51:49Mike Sullivan:Give him a couple months. That's fun, Kurt.

51:52Vinay Gidwaney:What's his problem? Can't even walk yet.

51:56Mike Sullivan:To speak again to the human element of this, the other thing that we've been doing that's been really interesting is that because we have this database of financial planning strategies that we're using to build this AI system. We also have it in plain text form. And as we've been doing acquisitions, having new people join our teams, we're saying, hey, like, who do you think is your top financial planning thinker? And we're getting them to go through as a human, everything that we have there. And it just, it keeps getting better and stronger. Like you can see it's really cool.

52:22Vinay Gidwaney:Absolutely. So when you're hiring somebody, you're not just hiring labor, you're hiring centers of intelligence. Fast forward a little bit, when you get a new job, you're going to bring your intelligence center with you. You're going to bring your AI with you. I don't know how we're going to deal with that with privacy and intellectual property and all the other things you have to worry about, but that's coming. Frankly, it's already here in the software development industry.

52:43Mike Sullivan:Just think about as this continues to evolve, there will come a time when you set up different agents, AI agents, and all they do is read. They read everything that you digest, all your client transcripts, all this interaction, and it will in aggregate be able to come to you and say, you know, there's an interesting trend. Children of your clients under the age of this, we see a pattern where this is emerging, where credit card debt or whatever the case may be, but this is where we get into insight that you would not pick up, but AI is simply consuming data for you, both individually at the client and family level and then in aggregate across the entire block of business and you pick up patterns and trends that you probably wouldn't have otherwise.

53:30Mike Sullivan:That's where I think this is all going, that it's going to get really interesting in terms of taking care of clients.

53:35Vinay Gidwaney:We call that internally ambient AI. It's just there always observing, keeping track of things, and then popping up when it needs a human to do something valuable and irreducible.

53:46Mike Sullivan:We're not at the ambient stage, but we have built the database connectors where we do have a chatbot right now where you can ask it, what are the main concerns of our doctor clients in Ontario over the age of 42. And it'll pull from meeting transcripts and tell us what's coming up for them. The ambient idea makes that even more interesting. What did the Charlotte Denver redundancy teach you guys?

54:10Vinay Gidwaney:That ultimately taught us that there's this issue of like shared intelligence throughout an organization. And what we observed in that case between two different field offices is that there was something going on in one office that easily could be shared with another office. And AI was the great way to build that consistency and intelligence. So it kind of goes back to what I was saying earlier, where you have that intelligence layer and you have humans feeding that intelligence layer. When I say that, it feels a little sci-fi-ish and it feels like it doesn't elevate the human. So what I always wanna remind people is that that's a loop.

54:46Vinay Gidwaney:And then the AI is making the human smarter, doing more interesting, more irreducible things, which fuels the intelligence center. And what you're trying to do is to ensure that whether it's in Denver or in Charlotte or San Antonio or in Edmonton, every single person in your organization has access to the best intelligence possible. When combined with their unique lived experience results in something really special and valuable for your client. It's setting up an organization in that capacity that we are trying to tap into. And by the way, we've been trying to do that. Every company has been trying to do that for decades.

55:21Vinay Gidwaney:You know what it's called? Learning and development. We've institutionalized this idea of we need to train people on best practices. The problem with learning and development is not its intention or the people, is the tools kind of suck. Watching a video or a mandatory training that we all have to go through to learn something, like nobody does that. Or learning from a playbook that's a static PDF, nobody goes to do that. AI is just an amazing learning and development tool, which allows you to amplify the entire organization all at once.

55:48Mike Sullivan:The more I hear you guys talk about this and where you see it going, the more it's like, wow, that same financial planning strategy database, we're seeing advisors use that already to train new advisors on what we should be doing. And the way that I've described how it affects the financial planning process is that it puts the whole thing on rails, where we know the level of quality that any client is going to get from one of our financial planners is going to be at a bare minimum as good as this intelligence that we've built that everybody has access to. Absolutely. We get us all the time and I got it asked a bunch of times yesterday where I put myself in the shoes of your listeners basically saying one digital is a bigger company.

56:25Mike Sullivan:They've got somebody like Vinay that has really allowed us to shape this. but I'm a sole proprietor or I'm a 20 person company or an 80 person company. And I think what I keep coming back to is this, whether it is inside your four walls today or not, somewhere along the way, someone who understands technology and someone who understands the business needs to start having a conversation. I can remember at the very beginning, I didn't know how to do anything. I didn't know how to do prompts and clause or whatever I'd call it. And then all of a sudden you get a tech person and non-tech person having a conversation, figuring things out and anything can begin to cascade from there.

57:03Mike Sullivan:With only one or the other, I think it's challenging to sort of come so far so fast. But I would challenge everyone that some version of this partnership can cascade a long way on companies of any size if you put that pairing together. I'll let you guys decide who answers this one, but what is the five-tier fluency model?

57:26Vinay Gidwaney:This is, again, sort of looking at that issue of transactional to collaborative and how we think about the different levels of AI fluency that we expect within our organization. One level, the bottom, like think of it like a pyramid. So the bottom level of that is every single person in our organization needs to be trained on how to use AI co-workers effectively. And really what you're trying to deal with there is sort of the deer in the headlights problem, where people are faced with a blank prompt screen and they're like, what can I ask AI? I get that question, what can I ask AI? Anything you want, just type and it will tell you something.

58:05Vinay Gidwaney:It's sort of getting people over that initial hump. There's a lot of things we do. There's training, there's every coworker has a list of icebreakers, sort of like break the ice with the coworker and start chatting with it. Each of them have resumes. It's very clear what their background is, what they're trained to do and the knowledge that they have and so on. They all have like little personal Facebook type pages on our corporate internet. You can go in there and see what they do and start chatting with them. So that's at the bottom layer is that Fluency Grid. Once you go up, there are more layers behind this.

58:34Vinay Gidwaney:I'm not going to go through each one, but really what you're trying to do is to capture the folks that are what we sort of think of as AI fellows. They're the ones who are the advanced users. They're the ones fueling that intelligence layer. They're the ones who are constantly saying, here's something new for the AI coworker to know. Let's put it in there. And then you have the managers who have to think about this very differently. You're training managers on how to think about their workforce as human and AI talent. That's where the workforce intelligence scoring and the methodologies behind that come into play.

59:04Vinay Gidwaney:There are other layers. We have builders, too. We haven't gotten into this conversation, but we really believe in the democratization of technology development. Engineering is no longer the longest pole in the tent. It's not that precious, high-skilled, I come from a software engineering background, so I say this with all love and respect, but it's not that special thing that you have a bunch of prima donnas doing that you need to protect and pay a lot of money. AI can write the software. So if AI can write the software, how do you build a governance framework within your organization that allows for a proliferation of technology?

59:40Vinay Gidwaney:And how do you capture the innovation that's happening amongst your people and give them the tool to write the software. Writing software will be like using spreadsheets. There's some people that are really good at Excel, some people that are not so good at Excel, but we all kind of know how to use a spreadsheet. And somebody says, I need to come up with a table. You go into a spreadsheet and do that. Building software will be that ubiquitous. And we're embracing that in a big way and a lot of technology to figure out there to do that really scalably and securely. But we think there's so much potential to come from that.

1:00:13Mike Sullivan:You gotta have the governance though. So we've been in spreadsheet hell before where people did that. And it's like, wow, does a family trust make sense? And we end up with six different Excel spreadsheets to determine whether a family trust makes sense for a client. And it took us years to get back to simple, single tools for everybody.

1:00:29Vinay Gidwaney:The good thing is that we have AI helping us do the governance. So we can read all that software code and say, wait a second, two people are working on this. I got the same thing. Let's just combine that.

1:00:37Mike Sullivan:Yeah, that's cool.

1:00:38Vinay Gidwaney:It just shifts the problem set from one area to another. Now it's not an engineering challenge. It's a governance challenge. Interesting. Yeah. Pick what problem you want to solve. But we think there's a lot of value in solving that problem.

1:00:48Mike Sullivan:I want to drill down on something you mentioned earlier. Why do the AI coworkers have faces and profiles? I love this conversation, Ben, because honestly, I remember this moment visibly where we went pencils down on the book. We were done writing it. We sent it to the publisher. And I think 30 seconds after we did that, Bain came out with a study that basically said, you should not do that. to your AI co-workers. You should not anthropomorphize. I can't even say it. I still have PTSD from that coming out. But they came out and said, you should not do that. And when I say I could not disagree with that more, our people talk about our co-workers like they are literally part of the team.

1:01:32Mike Sullivan:Ben, Dex, it goes on and on. It works for us. The one thing I would say is, I can't speak for every other company and every other culture and every other vertical. But what I can say in an organization where teams drive what goes out to the clients you serve, I think it's perfectly logical to have names and faces and personas and things like that. I can't speak to it otherwise, but it works amazingly well for us.

1:02:01Vinay Gidwaney:In reality, there's some paradoxes here because on one hand, we say we want to elevate humans and make sure that we protect humans and what they can do. And the other hand, we're saying, let's have the AI show up in your company like they're humans, and you forget that you're working with a computer. There's a few realities here. One is that we all have to accept that we're well past the Turing test. We're well past the idea of knowing whether you're talking with a human or with AI. And with AI video now and all those real-time avatars, the lines are being blurred very, very much. Do we have to disclose, yeah, you were talking to an AI?

1:02:37Vinay Gidwaney:Of course. And, you know, there's a new legislation in California that got passed that requires companies to disclose that. But at the same time, though, the best way that AI can be incorporated into your organization is to make it so that the humans don't really need to change a lot. We all needed to change our behavior to use a CRM really well. Put that information there. Put that information there. Click there. Click, click. What did it end up with? Everybody hates their CRM because we had to adapt our behavior to match the software. Let's go the other way around. Why not just let the humans keep doing what they're doing and let the AI adapt?

1:03:11Vinay Gidwaney:And the great thing is the AI is very human-like, so it's actually kind of easy to adapt it to it. And we get this question all the time, like, why do you have different co-workers? Why don't you just have one co-worker that knows everything? Well, that's a human behavior thing. We all have that person into our office, Bob, who knows everything. You go to Bob, he pretends he knows the answer, but you're like, Bob, kind of BS. No, no, no, come on.

1:03:31Mike Sullivan:That's Cam. That's Cam.

1:03:32Vinay Gidwaney:Cam always says, you go to Susie because Susie knows. Susie's been doing that thing for 30 years. If I go to Susie, she's always right because she knows her shit. That's the way that we think about our coworkers. Like we need to adapt them to how an organization actually works. And that's why we name them, give them skillsets. That's why we even give them managers who are human because that's how people expect to work.

1:03:57Mike Sullivan:How does each of your belief systems guide your decision-making? I believe that there is a dignity in work And everything about that experience should be as elevated as it can be. I believe that people that bring the right attitude, energy, and intelligence to work every single day deserve a right to navigate in this new environment where there's so much uncertainty and so much reconfiguration about how work gets done. I think employers need to approach this with a level of humanity. I think employers need to see faces, not headcounts. And I think employers need to put in the work to help with this transitional period.

1:04:41Mike Sullivan:Because unlike in times past, this, I think, ends badly for all of us if everyone doesn't put in the work to help. It's a very hands-on transition we're going to go through. And I know there's been things before like outsourcing to other parts of the world and manufacturing went away or things like that. But there is a universal component of this, of all things, all industries where things are going to transition. And I think this needs to be handled with a significant amount of care and dignity. That's the way I'm approaching it.

1:05:11Vinay Gidwaney:I feel very similarly. There's a core belief that I personally have, and I think many people have as well. But unfortunately, another belief is predominating the zeitgeist. And I think the difference is, is that whether you believe in the reductive nature of labor, or whether you believe in the potential of humans. And if you look at like the, I heard this term the other day, the brologarchy, the tech brologarchy out there in Silicon Valley doing their thing, obviously in more places than just California. There's a predominant point of view, and this is a blanket statement, so I apologize for the generalism.

1:05:48Vinay Gidwaney:There's a predominant point of view that human labor needs to be reduced and replaced. And if we can move that labor from biology to tokens, then that is very good. There's an entire trillions of dollars being pushed in that direction. The direction that I believe in is certainly we're going to move biology to tokens and replace labor in some way like that. I have to be realistic about it. But I believe in the power of human potential. And the exercise that we need to go through is not prioritizing moving the labor to tokens, but rather prioritizing expanding the potential of people. And I believe that that's a choice.

1:06:30Vinay Gidwaney:And I believe that that's a belief that you have to put into action every single day. It's easy to say it, but every corporate leader is going to be faced with that humanity test every single day, whether they choose, all right, this can be done with AI. I'm going to get rid of those people. This can be done with AI. I'm going to find a way to amplify those people. And I think that's a simple choice. Yeah.

1:06:54Mike Sullivan:And particularly in a financial construct, I've had people throw back at me, like, look, it's your job to drive shareholder value. And I'm like, but that's calculated in a particular snapshot point of time. Just protect this out. If in fact, we think margin expansion, short term, absolutely, you can head in that direction. Longer term, I'm not exactly sure how that plays out. And I don't think anyone has visibility into that. There's more ambiguity about that, I think. I got to tell you one funny story. I got into this discussion with one of our investors, and after it, he sent me two books. One was Marc Andreessen's Technology Manifesto, like I needed to read more about technology.

1:07:40Mike Sullivan:But here's the interesting thing. It was written five years ago. I literally was probably halfway through the book, and one of the exact quotes was, you need to let technology run unchecked. Written five years ago, I don't think there's anyone in the world that would suggest you should let AI technology run unchecked. I suggested he reread the book, but the other book he sent me was Economics 101. I said to my wife, I go, he could have sent me Economics for a Dummy, so at least I got the 101 version. But he was making his point, and I understand it. We're not sitting here with some altruistic hat on, but we're basically saying there's a mapping exercise that can be done here that I think is actually better for the client, but it's harder to do than simply cut.

1:08:26Mike Sullivan:It's really interesting to think about because the history of labor economics is, I think, more in line with what you guys are talking about. Technology has progressed tremendously over the last, whatever, a couple hundred years. And there have been short-term periods of unemployment, but people find new jobs and they upskill and things get better. That is how the world has gone. Why would this be any different? But I think the interesting part and how you guys are thinking about this differently is that historically, some companies do get obliterated because of new technologies. But if you can pivot within a company to take advantage of new technology, you don't have to get left behind.

1:09:03Vinay Gidwaney:There's this constant debate like, is AI different than every other technology revolution? So it is in some ways, but we're going to go through the same human behavior exercise that you just described, which is that we're going to upskill and a whole bunch of other jobs are going to go away and a bunch of new things that we can't even predict are going to emerge. And that's that whole irreducible, reducible thing. The difference with AI is the scope and pace. The scope is every industry. We tend to think of AI just affecting knowledge, information industry. It's going to affect robotics and the physical world equally as fast soon.

1:09:36Vinay Gidwaney:It's every industry. And the pace is super fast, meaning that like what you thought you were doing just three months ago is totally different now. When the pace and the scope are so extreme, we have to not put pause and get ourselves organized. We just got to make sure that we're making good decisions along the way. It won't naturally end up good for everybody. When the Industrial Revolution happened, it took time for that change to happen, to move people from agriculture to the factory and all those things. It took a few decades. This is all going to happen really quickly. And so what we're saying is that while that's happening very quickly, have some principles about how you think it should be done and invest in the things that help people survive through this and thrive through this.

1:10:24Vinay Gidwaney:Because yes, in the long run, will it all get sorted out and we'll all be better for it? Sure. But in the meantime, we're all living here and we've all got things to do, families to raise and dreams to live that it's going to be very hard to do that unless we all get help doing it. And that's where I think it matters a lot, how we spend our time talking about this and what we focus on. I think at the end of the day, Mike and I really connect on a lot is we just need more voices at the table that actually care about people. And we can't get this whole crowded out by the polar opposites of the people who don't want to use AI because it's environmentally, but I get it.

1:11:03Vinay Gidwaney:And the other opposite of like AI is going to do everything. Let's just make money. Let's also have AI stock and that will be your universal basic income. Like, come on. Like, let's just think that out a little bit. It's not going to work. So there's some middle ground here that is more reasonable that we're hoping more people accept.

1:11:19Mike Sullivan:Makes a lot of sense. What's the word that you guys now use every day? Well, we've used it a lot now. It's this irreducibility issue that comes up all the time now. And it is such an interestingly nuanced word that honestly, until 2026, I've never used it in my entire life.

1:11:36Vinay Gidwaney:I still have trouble spelling it. I don't know.

1:11:39Mike Sullivan:Is that what it is for you, Vinay?

1:11:41Vinay Gidwaney:I think so. Yeah, like certainly irreducible, reducible. I think that matters. I think the humanity test matters. What I reflect on sometimes is that we're all people and language matters to people. And the words that you use to describe things really does matter a lot because it frames it and it conveys meaning. And we ought to be really, really thoughtful about the words that we use. And I think that thinking of things as irreducible and protectable and more human, I don't know if they're the perfect words, what we're using right now, but what it comes down to in the end is we need a way to value the things that are uniquely human.

1:12:19Vinay Gidwaney:We tend to only value the things that we can replace with AI. We can put a number on it and say, well, that can go away. We can get that for cheaper. Well, let's find a way to measure and value the things that are uniquely AI because those are the things that are actually protectable and actually interesting and actually valuable. So let's find a way to measure that. That's a lot of the language that we're trying to verse ourselves in.

1:12:41Mike Sullivan:What are the four questions for Monday morning?

1:12:43Vinay Gidwaney:We frame that in the book a little bit because we understand that when you sit around and talk about all this stuff, it all sounds great. But what you deal with is like, all right, I still have to go to work on Monday and figure out how to cut costs or grow my company. Like, what do I do? So those are some of the questions in the book that really break down into what is your amplification score? How do you even measure that? Do you even have any ability to look at that? If you don't, let's start there. What is workforce intelligence? What are those work centers? So there's these series of questions that I think it's really important for leaders to go through.

1:13:17Vinay Gidwaney:It doesn't have to be those four questions, but we have to do our job, Mike and I's job through the book and through the work that we're doing at One Digital is to break this down into something that people can actually consume. Because it's easy to read a book about how the world is changing and blah, blah, blah. But actionable stuff that I can wake up on Monday and put in place is the hard thing. And mind you, actionable stuff that frankly, I can implement with off-the-shelf technology. Because none of us need to be AI companies to do this. We don't need to spend millions of dollars in tokens or data centers or whatever.

1:13:48Vinay Gidwaney:There's other companies doing that. The layer that most of us are working at is at the human layer. And those are things that you can do right away.

1:13:54Mike Sullivan:I would add to that, and I've said it a little bit earlier, that every company needs to understand who's going to go on point for this task. I would suggest it is C-suite level people, and it is a tech and a non-tech person. If you simply start there and say, there are folks with authority that are going to help navigate this, what really I think got us going on writing this book was that while we were deploying, we were talking to hundreds and probably thousands of clients around the country. And we were realizing most were going to the IT department because they viewed it as a technology issue.

1:14:33Mike Sullivan:And they were saying, pick a vendor, sign a contract, and then help us understand how we're going to roll it out to our people. Our view is that the technology is almost secondary to the strategy of where and how is it going to fit? Who's going to lead it? How are you going to understand the organizational change that needs to take place? It is a different set of questions. But I think for me, the number one thing is that if you can get a tech and a non-tech person that truly understands your business, there is so much that can cascade. But invariably, I think it has come to a bottom up. It's got to be a top-down set of decisions that get this going.

1:15:16Mike Sullivan:Leadership needs to be activated. They need to have their moment where it's like, oh my God, this stuff is going to change our business. With activation and with a partnership of tech and non-tech, I think most companies can go through their version of a transformation because there are going to be more and more playbooks that are created to tell you and show you what best practices are, but someone's got to lead it.

1:15:39Vinay Gidwaney:I just want to highlight something Mike said there. In our data, we saw this, and we have a lot of data about this, is that if a manager is activated, if their amplification by AI score is high, their team immediately gets there. So what I would tell your listeners is that if you have a CEO or your boss who has not had their oh shit moment with AI yet, your organization is not going to change. It's got to happen with your CEO. It's got to happen with folks like Mike, who's founder of the organization. It's got to happen with the people who are in that position because then they will start to think about work very differently because they themselves have realized that their own personal work has changed.

1:16:18Vinay Gidwaney:It starts at a very personal level. That's why I say, like, you got to be able to do this for yourself because that's the only way that you'll get over that hump into a post-AI, AI-native way. And in what we found in organizations, unless leaders, it's not just about like, oh, I believe in AI. We deployed AI and I pat myself on the back. I put it in a shareholder letter and it's great. I get rewarded for it. But then I'm going to go back and do my job as a CEO in the same way I did it for the last 30 years. No, you actually have to change your job. If you change your job, it flows down the organization.

1:16:50Vinay Gidwaney:Why? Because it gives people permission. It takes the fear away. It makes it something that's acceptable and not a stigma. People are still stigmatized by using AI. Oh, I used AI for writing this and I don't know if it's a good thing or I better hide it or whatever. No, like it's just another way to think and we should be free to do that.

1:17:09Mike Sullivan:I keep coming back to that financial planning project that we did. One of the reasons we're able to go and flesh out all of the text for all the different financial planning strategies is that we used AI to write them. And then we had professional financial planners to make sure there were no errors review them, but that's a massive amount of work to write all that text. And anyway, it's a positive feedback loop. What do we still not know? You've obviously thought a ton about this. You've formalized your thinking into a book. You've got a vision for how it's going to affect OneDigital and how you think other companies and executives should be adopting AI, what do we still not know about how AI is going to change the future?

1:17:43Vinay Gidwaney:99.9 % of what there is to know. Everything.

1:17:47Mike Sullivan:I know. I was going to say, I'm not sure we know. It's very early innings, I think, Ben, in terms of how this plays out. And that's why I think as much as I'm amazed and so bullish about where we're going as a company and what we've done, it's all that I don't know that I still worry about. When I see these interviews with somebody like Elon Musk, who says by 2035, most employment will have gone away. AI will do better than humans, almost everything. I don't know how to process that because I would sit here and go, most of what he has said in the past, people have scoffed at. And yet, I think the track record would show I wouldn't bet against the guy with coming up with unbelievably innovative things.

1:18:30Mike Sullivan:So we as a society need to keep pace with what AI is going to be able to do. That probably is more than anything. The way I think about it now is we're not moving fast enough to evolve with the way in which AI is going to evolve. That may be our problem unless we get going faster towards what this blended workforce needs to be.

1:18:55Vinay Gidwaney:I want to give the analogy, and it'll lead to what we don't know, is look, think about people running a mile. When technology was at a certain state of development, the fastest that a human can run a mile was like 12 minutes or something. Now it's three and a half minutes. What's changed? Our biology hasn't changed. The technology to train our biology has changed. The technology has gotten better. It's optimized us as people. We are going through the same transformation with our minds. that AI is optimizing our minds to achieve greater things. I don't know what the next step is, what our minds are going to be capable of.

1:19:29Vinay Gidwaney:We're right now at the 12-minute mile. What does a three-minute mile actually look like? It is that belief in the potential of humans that we don't know yet and is very exciting about this that we have to start to put a lot of energy towards. So I think that the Elon Musks of the world or whatever have a different point of view. And unfortunately, they get to control a lot of what we talk about in this society and how we spend our time and money. But I think that there's an expansive view of human potential that has to be talked about more. And that's the exciting part.

1:20:00Mike Sullivan:Really interesting insights, guys. This has been great. The book was a great read. And as I've already mentioned, it really aligns with the way that I think is the right way to think about all this stuff. I really think you guys nailed it. I'm glad that you're leading the company that I work for.

1:20:15Vinay Gidwaney:Thanks, Ben.

1:20:16Mike Sullivan:Thank you, Ben. You guys are doing amazing things as well. And there's a whole bunch of people directly involved in this other than Vinay and myself. I am confident that we're going to continue to figure this out. We started this first step in a journey of just saying, let's get an alternative narrative out there that more people understand what we can do and get moving faster in a pro-humanity blended workforce, irreducible, reducible way, because we can figure this out. We just, I think, need to move faster. Great to see you guys. And thanks for the energy on this subject. Everyone's talking about it.

1:20:53Vinay Gidwaney:Thanks, Ken.

1:20:54Mike Sullivan:Pleasure, guys. Thank you very much.

1:21:17Vinay Gidwaney:and is a member of the Canadian Investor Protection Fund. Investment advisory services in the United States of America are offered exclusively by One Digital Investment Advisors, LLC. One Digital and PWL Capital are affiliated entities, and they mostly get on really well with each other. However, each company has financial responsibility for only its own products and services. Nothing herein constitutes an offer or solicitation to buy or sell any security. Occasionally, we tell you not to buy crappy investments in the first place, but that's not the same thing as telling you to sell them. This communication is distributed for informational purposes only.

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