The Challenger Sale and What the Market is Telling Advisors about AI

[00:00:10] Tom West: This is going to be part two, everybody, of an extended conversation that we had starting last time, when we really were talking a little bit about The Challenger Sale, a book that came out that was hugely impactful on the early years on my career in terms of framing advisor archetypes and what we did last time, coming off of a discussion about The Challenger Sale, the Lone Wolf Path to Extinction. This is part two, everybody, where we're going to be expanding on that conversation about what the market is really telling advisors about the role of artificial intelligence and the mashup of the ideas that we have is, what kind of roles do financial advisors want to, or need to take in their clients' lives?

When we're looking at an overwhelming amount of data that clients want a broader experience, clients want more holistic communication on a wider number of different topics in order to be able to get those psychosocial buttons sort of pressed, well beyond just something as pedestrian as portfolio management.

And I will start this conversation with a bit of a story if I could. So in March of this year. I was asked to be one of the lead presenters at the Global FinTech Conference in Manhattan, and my presentation was going to be on things that I've talked about on The Family Financial Conversation a number of different times, which was age-weighted revenue, demographics are shifting. Advisors need to think about changing needs of Boomers, how do we connect with them emotionally and so on. And I had a lot of data behind why advisors need to shift. But, I mean, it was the Global FinTech Conference and like, and I'm not a technologist, and what ended up happening in terms of the sequence of speakers, whoever it was, sort of the headliner to like start the whole conference off, I don't know what happened in the conference, but they were late and I got asked to bump up my late morning presentation to be the first one in the kickoff. I'm like, okay, I'm ready to roll. So I do the kickoff while all of the other technologists are coming in and the whole conference really had AI as sort of a center of gravity, but here's this advisor, aging expert guy kicking us off.

And it was sort of, I got everybody's attention, but it was a little bit out of tune. What was interesting, everybody in the audience, is everything that came after that happened to be sort of accidentally reframed and given all of these demographic pushes. Now how do we start thinking about AI?

In a weird way that I was kind of proud of how it worked is a lot of the questions that came from the audience to the speakers that followed me had more to do with changing needs of the market and what do we do with all the psychosocial stuff, which might not have been what they wanted to talk about.

So that was a roundabout way of how we're sort of backing in, I'm backing into some of these conversations. And Suzanne, where do you want to start? When we're thinking about clients signaling that they want broader engagement, artificial intelligence we know is going to be omnipresent in all of our lives and our professional lives too.

Where do you think is a good place for advisors to start thinking about this with client engagement?

[00:03:34] Suzanne Schmitt: So I would love, if you wouldn't mind, Tom, you mentioned a phrase that's, a concept rather, that's very familiar to us. But I think starting by, just telling the audience what age-weighed revenue is.

[00:03:45] Tom West: Sure.

[00:03:46] Suzanne Schmitt: And what it means could be a great jumping off point because I think that in a way sets the table for why the market is shifting so quickly and how advisors can think about, expansion of their practice.

[00:03:57] Tom West: Sure. All right. So, this is a good opportunity to highlight and promote our new project that's coming up, Longevity Strategists, where we're going to come back to this concept of age-weighted revenue, and we'll have graphs and things like that, and sort of a more robust presentation.

But for the audience right now, to answer Suzanne's question, age-weighted revenue is the way that I look at my practice. And increasingly ownership and management of large wealth management firms are looking at net flows. And what we're really, and in my mind, success in the industry only has to do with net flows, is what's coming in greater or less than what's going out, net of everything else.

What age-weighted revenue is, is tracking by head of household, which clients are paying what kind of fees. And the reason age-weighted revenue is important is, trying to anticipate how much of your fee revenue is going to be dropping off as your clients age. Like right now, for example, my median age-weighted revenue in my practice is something like age 72.

So it means half of all my practice revenue is for head of households north of 72 and half is under 72. The reason that this concept is really important is the second-generation retention rate for advisors keeping their clients, we know it's like approaching zero. It's terrible. And if you know, for example, that like I've got 20% of my client revenues from head of households age 80 or older. If I'm like the rest of the industry, and I don't keep the overwhelming majority amount of that revenue in my practice after the 80 plus year old households die off, I'm looking at a very different business model going forward. That's the framing behind it. We're going to go into a lot more detail on age-weighted revenue in this Longevity Strategists project that we're going to be telling you more about.

But Suzanne, I'm putting it back to you. Given that, where do we want advisors to start thinking about artificial intelligence as a new disruptor in client engagement?

[00:06:15] Suzanne Schmitt: Yeah, so doubling down, I would argue age-weighted revenue and artificial intelligence have a direct connect in that the age-weighted revenue of all advisors books is the best predictor of the long-term health of their practice.

And it is a metric that virtually no one is tracking right now. So we're both, I think, passionate for similar and dissimilar reasons about that metric. But backing out into AI and even connecting it back to The Challenger model, I think one of the biggest opportunities for application of AI, specific to an aging population is really around two things.

One is the health and wealth connection, and the second is preferred engagement style. That is not to suggest that all of the AI and very sophisticated AI that is available to advisors around, for example, risk management portfolio modeling isn't important, but I think we both believe the secret sauce is in getting ahead of those issues that are rapidly changing for those clients and using AI as a way to kind of hack the conversation and bring better EQ and better awareness of what is happening in the client's life outside of the pure portfolio management, into the conversation as a way not just to retain that client, but to connect with their kids around issues that matter to them too.

[00:07:32] Tom West: Yeah. Yeah. So, harkening back to the podcast that we just recorded, the imminent extinction of the lone wolf. I think that the idea of if, Schwab and Fidelity and their systems and their team making, and together we go far, if that's not enough of a death knell for lone wolf, I think AI is probably going to sort of take it over the top, but everybody, let's remember we're using Challenger Sale in the title of this podcast.

What's the market telling us about AI? Remember that there are five archetypes in The Challenger Sale. This is sort of some framing that was impactful to me and a lot of advisors early in their career. There's the challenger, which obviously the title of the book. Seems to say is the most effective business development technique that we sort of adapt, our, net flow success to.

And the concept once more is to be disruptive in terms of the client's perspective or the perspective client's perspective on problems, and then offer a path to a solution that's sort of the challenger bucket. But other than the lone wolf that we've talked about, we also have relationship builders, and hard workers, and reactive problem solvers.

Suzanne, let me put it back to you. For advisors that are falling into these five categories, challenger, relationship builder, lone wolf, hard worker, reactive problem solver. Talk a little bit about reactive problem solving as an archetype for an advisor and how AI might be able to impact that style of advice.

[00:09:02] Suzanne Schmitt: Yeah, so just leaning into the name. For people that are not as familiar with the concept or the archetypes, the reactive problem solver is super responsive. They are very detail oriented, and interestingly, they are crisis focused. So in sales terminology, reactive problem solvers make great account managers because they're always ahead of what can go wrong.

And, I would argue that's a tremendous value add in managing complex relationships and complex situations. Some specific AI implications for the reactive problem solver is really, how do you take less of your mental energy and put that into predicting what could potentially go wrong, and lean on AI so that it frees up a little bit of your mental energy and your time.

So, as examples, there are some great resources in AI. One specifically is called True Value, but that is a risk mitigation AI tool, as is generative AI. And that combination, regardless of whether you're a ChatGPT fan, or Perplexity or a Claude fan, that combination, we believe based on market data can be a great resource for somebody who falls into that reactive problem solving to free up some time that they can then reinvest in building deeper relationships and getting better ahead of their own data, so that they start to shift the way they show up in their relationships and in their work. Yes, they're still going to be great problem solvers and there's a place for them, but they're actually showing up in ways that help them build more relational capital. And they start to be able to introduce, much like the challenger, more data and insights as a way to shift their practice. So they're leaning into their strengths, they're freeing up time, and they're doubling down on relationships. So those are simple resources in the AI world that could be a great hack for the problem solver.

[00:10:55] Tom West: And let me try to, as an advisor that sometimes isn't like, on this podcast not being the smartest person on this podcast, let me kind of de-wonk some of it.

I want to give practical examples. If you've got a practice where, like my practice, like a lot of people's practice, everybody's getting older and your age-weighted revenue is getting older. I want you to anticipate if you've got a big book of 70 pluses, it is a near certainty that there is going to be, let's say for example, things that are coming from your client base about death, about disability, about power of attorney, about retitling stuff, about successor decision makers, and so on.

And you might not know exactly, reactively when any of this stuff's going to happen. But the probability of that being sort of a service requirement and an opportunity to engage in deeper relationships. Like, I'm going to say, it's like a 100%. And this is something that we do very good in my practice and I'll give you some insights on how we do it.

When somebody calls in that like, "Listen, mom had a fall, dad had, my husband's got some diagnosis. This particular chronic illness has sort of shown up. We're not going to be able to stay in our house anymore." Like, all that stuff, we always treat it like. This is our opportunity to be on stage. What are our lines?

What do we do? Who do we call? What's our short list? Like, the last thing that we think advisors need to be doing is, "Okay, let me, think about it and we'll call you back." Or, "Just tell us how much money you need to withdraw." Like all of that, that's sort of foreshadowing you're going to get canned by the kids because you're not offering any value in that critical moment.

But anyway, I wanted to give you that to frame some of Suzanne's comments about AI. What if that whole protocol, what if that whole process is automated? If artificial intelligence enables advisors to push the power of attorney button, and then the cascade of the communications and all of that. That's something that I'm building right now for my practice.

We'll talk about that more in Longevity Strategists, our new project on Advisorpedia, in short order. But that gives you some idea on how AI might be impacting folks that are reactive problem solvers. But there are two others, Suzanne. We've also got advisors that might fall under the archetype of relationship builders.

And their claim to fame. The impact of their career is, listen, they know everybody. They're the connectors out of Malcolm Gladwell's Tipping Point. Let's talk a little bit about what AI, what the market should be telling advisors about the implication of AI and automation through the prism of relationship builders.

[00:13:48] Suzanne Schmitt: Yeah, so again, AI tells us that, independent of what archetype you fall into, the best and highest purpose that AI can serve for you is to free up time to build relationships, and to lean into those soft skills that you were just talking about, independent of having to spend a lot of time on them.

So, for a relationship builder who's great at building rapport, maybe not so great at handling conflict, some of the best things that AI can do for that relationship builder are to help them personalize automatically so that they don't have a heavy lift with that. Again, a tool like anyword can be a great resource to help tailor client communications without relinquishing control, as one example.

Advisor Engine is another AI tool that can help a relationship builder kind of automate and handle a routine inquiry. So again, I think the name of the game here is really freeing up time for that relationship builder to lean into the strength of their relationships and demonstrate proficiency in managing, simple but complex tasks more easily in terms of automation.

So I think those are two key takeaways for the relationship builder.

[00:14:59] Tom West: I think there's also one additional element that I might add. Because I think if I was going to say, I got asked a handful of times, like, what's my archetype in The Challenger Sale? I'm somewhere in between a challenger model and a reactive problem solver.

I tend to not always be the advisor that follows through in my share of a professional relationship. Like, I'll meet everybody and then I'll forget about what's going on because I'm thinking about different things. I think artificial intelligence and automation can help keep me on track of the, if you go back to, what's my Russ Prince book here?

The Ultimate Rainmaker, this is what I'm going back to, like getting better referrals out of attorneys and accountants and whatnot. Automation and AI can help me, as a relationship builder remember, to ask stuff like, "What's my attorney's spouse’s name?" Like, to round out a more disciplined discovery that can enable deeper connection with those that refer, or some of our clients.

So I think it empowers relationship builders to make sure that they have all of the information they need to be able to go deep and sincerely bring to mind things that are important to whoever it is that you're working with. So that's an important piece too. And then I think there's one other archetype that we haven't really talked about so much, which is the hard worker.

I think the hard worker is going to have a little bit of a challenge in the world of AI if they don't really get their act together and understand how the environment that we're living in is changing. Now, everybody remember that in The Challenger Sale, the hard worker, this is the 60-hour-a-week guy.

This is the grinder. This is the, how many nos do I get before I get a yes, like those sorts of pieces. I think the hard worker kind of next to the lone wolf, if that's your particular claim to fame in terms of how you're chasing success, I think that hard workers are going to have to do some pretty significant adapting in the age of AI. Suzanne, what are your comments?

[00:17:22] Suzanne Schmitt: Yeah, I think that's right because you could argue what AI does is it gives us back time because it is doing the task for us, and that really takes away from the hard workers claim to fame to just grind it out. So, for the hard worker, really getting proficient quickly at automating repetitive tasks. As an example, Ocrolus for document processing, Zapier for summarization, can be great ways for the hard worker to automate the lowest value work that they are doing, show up a little bit differently for their clients, and reinvest some of that extra energy that they have in new skills. And that could run the gamut from relationship building to personalization, to digging into frankly, new areas of expertise and new relationships to your previous point. We firmly believe that expanding into medical, into housing, into a much different variety of longevity planning, are going to be critical.

So for the hard worker, they probably don't want to work 120 hours a week. But at least if they can claw back 10 to 20 hours a week, reinvest that, they can really get a new lease on life and their practice and show up very differently for their clients and frankly, much more strategically.

[00:18:38] Tom West: Yeah, I think that makes a lot of sense. I think the other thing with hard workers, like if we subscribe to the idea that the hard worker isn't anti-team, they're not like the lone wolf sort of charting their own course. Like maybe the hard worker in the financial advice universe, maybe they go in the train-the-trainer route, like maybe their role is changing a little bit.

Maybe they're the ones that are making, that are taking some ownership in the development of appropriate processes. Because somebody still has to tell the artificial intelligence like where to start and how to go. And those are typically grinder type personalities to make sure with your Boolean logic of your decision trees, somebody's got to grind all that stuff out.

So I'd like, even though I think there's some threats to hard workers being automated out of existence. I do think that there are opportunities because somebody still needs to train the trainer. Somebody still needs to tell the AI what to do. So that's sort of a path to potential success. And then lastly, the highlight of The Challenger Sale, is the challenger archetype themselves.

And everybody, remember that the challenger, basically the conceit on the challenger salesperson is they introduced to their prospective client problems that they didn't know that they had, sort of thin sliced with additional complications, perhaps a bit of complexity, and sort of present to the client through a concept that the book talks about, rational drowning, of trying to introduce, "Well how are you going to figure your way out of this pickle?" It would be an example of, "I know you want to retire and you want to move to this particular place, but your wife has this health condition and you're still responsible for your mom and college is still coming up and you have no insurance."

How do you put all that stuff together? That's a really quick way to establish a value proposition. Then you say, "I can help you break it into smaller pieces and keep all of those things straight." That's kind of The Challenger Sale mentality. The pushback that a lot of advisors and clients sometimes have on The Challenger Sale is, sometimes people don't like the idea of jamming prospects with that many problems. Sometimes if you're not doing it really well, it's coming off as a bit aggressive. But I do think that part of the role of advisors is to help clients anticipate, see around corners, problems that they might not be paying attention to.

Maybe there's a blind spot. I think that's part of our job. But in my mind, a challenger in particular also has to be conceptually adaptive in the role of artificial intelligence. So, Suzanne, on the challenger in particular, what would you say to the challenger archetypes that find their success as advisors there?

Where do they need to start thinking about artificial intelligence?

[00:21:36] Suzanne Schmitt: Yes, I think first, Tom, getting proficient in the basics. So starting to use ChatGPT, Claude, or Perplexity to do more of, not just the market analysis, but actually diving deeper into the issues that are affecting their clients around tradeoffs that are going to have to be made. For example, helping kids fund college versus saving for retirement. Tradeoffs around health and housing, and tradeoffs around funding long-term care, as examples. All of those, I'll call them intelligence engines, are great resources for the challenger to get proficient, but also to generate really helpful actionable insights and graphics, because we know that the challenger likes to use images with his or her clients.

The second place that challengers can really leverage AI is around specifically building their own. I know it sounds out there, but it's not so much anymore. Building their own tools and using niche financial data sets to deliver hyper-personalized insights. So training those ChatGPTs to actually do some work that otherwise the challenger would have to invest in him or herself, or leverage home offices if one is available.

And lastly, I think specifically around the data visualization. And you and I have talked about this, all of these archetypes, but especially the challenger and the relationship manager could do a tremendous amount using video, and I'll say interpersonal relationship management, like in touch some of the really interesting and new AI to really stretch and extend their reach for clients.

And I'd be curious, I know you are starting to investigate some of this for your own practice, but what are your thoughts about the role of AI in relationship management, specifically for challengers and otherwise?

[00:23:27] Tom West: Sure. I mean, and this is going to be, that's good foreshadowing to our Longevity Strategists project that's coming.

So everybody, come summertime, we have got a great new point of view that we're going to be sharing on a platform called Longevity Strategists here at Advisorpedia that are going to be tackling exactly Suzanne's question to me right there. Like, what are some of the applications of technology and practice management that I'm going to let everybody look over their shoulder and you're going to see me trying to work it in real time and what's working and what's not working.

And I'll give you a good x-ray in terms of how my practice is going. My attitude, to your question, is I think that there's a space for artificial intelligence to really help out an advisor's discoverability skills. To anticipate in advance what the client is going to need or want before the client needs or wants it.

If you go back and you think about this reactive problem solver. In my practice, for example, where we do a lot of specialization in housing and healthcare transitions and best practices there, I want you to imagine that, can artificial intelligence start telling us that somebody's down a particular path before they might be aware of it themselves?

And there's this whole acceptance/denial thing, but, and when they want to engage a client. But if I'm the advisor, and I know T minus X period of time there's going to be a housing change. T minus Y period of time, somebody's got to get the daughter in California on the power of attorney and knowing what's going on. Those sorts of things.

So I can see artificial intelligence helping me anticipate the challenges that clients are going to be going through, and have all of the ready resources right there, when it happens, so that when I feel like I'm on stage, we're like this. I know the “who to call” and “how to work” and things like that.

And in Longevity Strategists, our future project, we're going to be talking about how artificial intelligence might be able to bring some of those ready resources into the dashboard of not just my practice, but applied more broadly. So I want you to look forward to that. I think the other piece with the artificial intelligence that I'm looking at is.

You mentioned a little bit about video. Like here, I am at a podcast. I've been trying to develop skills to complement some talents that I have for being able to talk pretty well and read pretty well. But I want to develop more skills that I think can be leveraged with technology. And around this video medium, how can something like a podcast, how can something like recorded video market updates or video emails, how can artificial intelligence help me record one evergreen, just in time, ready resource for folks that might be in this particular position that can automatically drop it in a sequence of the third thing out of 10 once somebody cascades there.

And the last piece that I think is important with artificial intelligence is it's another way to get real-time data from your client to be able to inform you about what are the pain points. What are the things that people are hopeful for? How has it changed? So maybe I'd be a little bit more forearmed going into some of these conversations as an advisor with a better setup that I'm already kind of pre-debriefed.

I think that's going to make for a better client experience. I think it's going to continue to positively affect my second-generation retention. So that's some of the ways that I'm thinking about it. But everybody we are at time. Thank you very much, Suzanne, for, what I hope the audience is thinking has been an enriching conversation.

Sort of two things, if you like this podcast, that's great. Make sure that you subscribe, and also look forward to our new project. I'm foreshadowing it coming out in the summer. Longevity Strategists, where these are the kinds of conversations that we're looking forward to expanding on.

But appreciate everybody's attention and we'll talk to you next time. Bye-bye.