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Hosted by Financial Advisor Coach, Ray Sclafani, "Building The Billion Dollar Business" is the ultimate podcast for financial advisors seeking to elevate their practice. Each episode features deep dives into actionable advice and exclusive interviews with top professionals in the financial services industry. Tune in to unlock your potential and build a successful, enduring financial advisory practice.
Ray Sclafani (00:00.142)
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Welcome to Building the Billion Dollar Business, the podcast where we dive deep into the strategies, insights and stories behind the world's most successful financial advisors and introduce content and actionable ideas to fuel your growth. Together, we'll unlock the methods, tactics and mindset shifts that set the top 1 % apart from the rest. I'm Ray Sclafani and I'll be your host.
Sir John Templeton is widely credited with saying the four most dangerous words and investing are this time it's different. In markets that line still has real wisdom. In technology, however, it can often become a trap. Because when it comes to artificial intelligence, this time is actually different. Not because every headline is right, not because every tool will matter, not because every advisor needs to become a technologist.
but because for the first time, intelligence itself is becoming cheap and accessible and embedded into the daily workflow of work. Now that changes the economics of advice, the speed of planning, the cost of execution, and the expectations clients will bring to each and every relationship. Some will say we've heard this before. Discount brokerage, well, that didn't kill advice. No fee trading, that didn't kill advice.
Even robo advisors didn't kill advice. Fair point. Human advice did not disappear. But that does not mean the operating system of advice stayed the same. robo push digital onboarding, no fee trading, compress perceived product value. And now AI is going after something more material. It's going after time, cognition, workflow, planning speed, and the client's expectation of what responsive and
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personalized should feel like. Just think about how Amazon Prime has changed our perception and speed of delivery. Schwab's latest RIA study found 63 % of RAs are already using AI in some capacity, but most are still in the early innings, using it mainly for administrative tasks like note taking and drafting of emails, etc. In other words, the industry has started moving, but most firms have not yet made the jump from experimentation to real
redesign. Over the last 60 to 90 days, I've had multiple conversations with advisors, CEOs and leadership teams about AI. And in more than one meeting, someone has quietly pulled out their phone or their watch and said something like, well, I've already run this through AI, not in a year, not in five years, like right now. And that exact same time, what's really interesting is I've had other advisors look at me straight in the eye and say, Hey, Ray, this is all hype. We've seen it before. Well,
Both are happening right now in the same industry. So which one's right? Here's what we know. A recent McKinsey estimate suggests generative AI could add between 2.6 trillion and 4.4 trillion annually the global economy. More than half of people today in the US are already using AI tools in some form. And over 30 % of US adults are now using wearable devices that continuously track health
behavior, all kinds of patterns. So just pause on that for a moment. Your clients are walking around with real time data about how they live their lives. They have tools that can interpret that data instantly. And they're also getting more comfortable using these tools each and every day, which leads to a different question. What does it actually mean to know your client going forward? Because if data expands and intelligence becomes embedded in everything,
Well, then this time really is different. Let's talk about how we should be thinking about this. I think there are three audiences listening to this episode right now. First, there are the leaders inside these institutions who are already sponsoring and deploying large scale AI projects. Second, there are independent entrepreneurs trying to figure out how to deploy limited resources without wasting money. And third, there are advisors and team members whose heads are still stuck in the sand hoping this becomes
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one more overhyped technology cycle they can safely ignore. In somebody recently said to me, hey, remember when 1999, know, Y2K was going to blow things up? Well, that didn't happen either. I kind of chuckled when I heard somebody say that to me. That group needs to hear this plainly. Denial is not a strategy. You don't need to become an AI expert tomorrow, but you want to become AI literate.
Because your clients are already getting exposed to these tools, your competitors are already testing and deploying them, and the people on your team with initiative, they're already finding their own way to use them. The issue is no longer whether AI enters the business, it already has. The issue is whether you lead its adoption, or get dragged into it later on under some kind of pressure. Here's a larger framework and way I'm thinking about it. Data will sit at the center of this story.
For broker dealers FINRA rule 2090, that's the KYC rule requires firms to use reasonable diligence to know and retain the essential facts concerning each customer. For retail recommendations under RegBI, the customer's investment profile includes age, tax status, objectives, time horizon, liquidity needs and risk tolerance. And for RIAs, the SEC and the FINCEN, that's the US Department of Treasury's Financial Crimes Enforcement Network,
proposed customer identification program requirements in 2024, that would require advisors to identify and verify customer identity if adopted. That means KYC is not just a compliance language, it's becoming strategic language. Why? Because in an AI driven world, your most valuable proprietary asset is not generic market commentary, that's becoming common. Your advantage lies in the quality, depth and structure of what you know about your client.
their goals, family dynamics, their tax situation, their business interests, their timing issues, held away complexities, fears, blind spots, the community they live in, and actual behavior under stress. This is the raw material from which better advice is built. The firms that capture this information, organize it effectively, permission it appropriately, and activate it efficiently will deliver better planning
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improve service, stronger client loyalty, and ultimately better advice. That is why data hygiene is becoming leadership hygiene. And I also think building community is going to matter more than ever. As information becomes more accessible and the tools to analyze that data also become more available, the role of the advisor will shift. Less of a gatekeeper, more of a guide. Less about explaining products, more about making sense of them.
less of an isolated expert, more of a builder of trust, accountability, and community around a client's financial life. Cerulli found that only 38 % of affluent investors are at least somewhat comfortable with AI and financial advice. And this comfort markedly decreases among older investors. Meanwhile, Cerulli also found that human advice remains clearly preferred over online
only advice, particularly with these older clients. Therefore, the future isn't about choosing between human and AI. It's about enhancing humanity with AI. Now, let me make this really practical. I want to share with you a three part roadmap, a way of thinking about how to approach this AI that's moving really quickly. Part one, you've got to put that learning hat on that lifetime learner. Your first job is not to buy more software.
It's to actually build fluency. Every leadership team should have a shared vocabulary for prompts, models, hallucinations, grounding, data permissions and agents. OpenEye says ChatGPT can still be wrong and can express confidence even when incorrect. Anthropic says the same thing plainly about Claude. These systems are powerful, but they still hallucinate. So the first step is education, not blind trust.
For free or low cost learning, I would point teams to official programs first. Google AI Essentials offers a beginner friendly certificate path. Microsoft Learn has a large free AI learning hub. AWS has free learn about AI training and a broader AI learning center. DeepLearning.ai has strong short courses, including aogenic AI and multi-agent systems. Anthropic now offers AI fluency.
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framework and foundations and open AI Academy has practical workplace learning content as well. For technical team members, both Anthropic and deeplearning.ai have hands on agent courses that move beyond theory. You'll find a list of these courses and also some newsletters that I personally follow in the show notes. Okay, part two, once you've got your learning down and you've built out a pretty cool training program for teams, part two would be let's apply some of this knowledge.
start with narrow use cases that save time and improve quality. My guess is that if you're listening right now, you're probably already doing some of this. So consider AI tools for meeting prep, note summarization, drafting follow-up emails, CRM cleanup, task extraction, pre-meeting briefing packets that can be sent to clients, client segmentation analysis, internal knowledge search, the first draft of some planning observations.
that's the right starting point because the risk is lower and the payoff is easy to see. Microsoft Co-Pilot, Jump.ai or Zox. Okay, then move to planning adjacent tools that point to where the puck is going here. There are two I'd point out. TaxStatus.com provides IRS sourced client data to advisors and tax professionals. Advice.ai is positioning itself around AI powered analysis for complex multi-generational wealth
and has now partnered with tax status to build tax planning strategies on verified financial data. When I look at that combination, I've seen some demos, it's not a gimmick. I actually see the future of planning flashing before my eyes, structured data, verified data, AI driven analysis, tied together in a workflow that compresses time and expands insight and puts the advisor in the first chair, leveraging tools, data and technology to provide better advice for clients. This is
kind of where the healthcare industry is ahead of us. In healthcare, AI adoption has moved way faster from pilots to real use caseloos. Menlo Ventures reported 22 % of healthcare organizations had implemented domain-specific AI tools last year. That's up sharply from the previous three years. KLAS reported 70 % of providers and 80 % of payers had AI strategies already underway. So wealth management is moving
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But much of our sector is still experimenting around the edges. Before you go too far down this path, I just want to talk about risk for a moment because this is where many advisors either freeze. Hey, Ray, what's the risk to all of this? Or they move too fast. If you're using public AI tools, you've got to be thoughtful about what information you're putting into them. Client data, personally identifiable information, anything confidential. None of that should be going into tools that are not already
approved by your firm or compliance team, but are gated. The US SEC has already issued guidance making it clear that advisors are responsible for how they use AI, including how client information is handled, how outputs are supervised, and how advice is delivered. This ties directly back to your fiduciary duty. I mean, we all know this to be true, but we've got to bring it forward and make it part of the conversations, part of the learning. also part of the application.
So don't overcomplicate this, but don't ignore it either. Understand where your data is stored. Know what is being retained. Always have a human reviewing the output before it touches the client. This is not about slowing down. It's about being smart while you move. When I get on a Zoom meeting or a Teams meeting and I can see somebody's recording the meeting, I always simply ask, hey, where is this being recorded? Where is it being stored? How long is it being stored? Do I have access to that information?
And it's shocking to see how many people don't actually have answers to those kind of questions. Be prepared for those questions if you're recording a meeting. Okay, part three, redesign. Well, this is where most firms fall short and frankly are avoiding. AI is not just a new tool, it's a new way of working. That means change management matters. People have built careers on doing things a certain way. Some of those were pretty smart.
Some are now very expensive and outdated. You cannot lead this like a software rollout. You have to lead it like a shift in behavior. So what should leaders do? Set policy, decide what data can and cannot be used, define approved tools, train the team, pick two or three workflows to redesign, measure not only our saved or quality improved, but also the client impact that client experience.
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talk openly about what is changing and why. The goal is not to tell people the old way was dumb. The goal is to show them a better way to create capacity, reduce friction, spend more time where humans still matter most. This is a conversation that should be happening in your boardroom and with your team. Now let's demystify one phrase you're going to keep hearing. It's a genic AI. An agent is not just a chat bot that answers questions. An agent is software.
It's a robot that can reason through a goal. They can use tools, they can take actions and sometimes coordinate steps with limited supervision. IBM describes a genetic AI as systems that can accomplish goals with limited supervision. Open AI describes ChetGPT's agent as something that can reason, research and take actions on your behalf. Anthropics work on effective agents makes the point that the best systems usually use simple
composable patterns, not magic. So think of an agent as a digital worker assigned to a job with rules, tools and guardrails. In the future, we're going to start seeing multiple agents, multiple agents interact with each other. And then you'll start seeing a convergence of some of these agents. So what are the limits right now? Well, these agents, they can still make things up. They can miss read text, they can overstate confidence. That's that hallucination that I talked about earlier, they can fail across long chains of work.
Some estimate that much of the information up to 30 or 40 % could be hallucinogenic. Anthropic has written that long running agents, you what takes a long time to build an answer still struggle across many context windows. So if you ask it too much, well, it's not ready to reply so quickly. It depends upon your prompt. Open AI states that knowledge limits and false confidence are still real issues. So you don't want to delegate judgment, delegate drafts.
pattern finding, synthesis, research support, and process steps, things that are very repeatable, they're very good at keep the human in charge of advice, context of that advice, ethics and accountability. And yes, all of this is going to evolve very quickly. Open AI and anthropic are both moving from chat to action. Now that really matters because one of these systems can not only answer
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but also operate tools and workflows and forms and files and systems way more reliably. So the conversation changes from how do I use AI to which parts of our team and our firm should still be doing things manually? That's the bigger question. Here are three stories that I think help frame the future. There's a movie that's more than 10 years old called Her. It showed us that emotionally intelligent digital companionship is real.
That matters because clients do not want answers. They want to feel known. There's a show on Netflix called Black Mirror. They've spent years warning us what happens when powerful technology outruns governance and human wisdom. And that matters because not every use case should be built just because it can be built. And Minority Report imagined a world where data, prediction, and personalization blend together into the background. You don't even notice it. And that matters because the most powerful AI
will often feel less like technology and more like an invisible operating layer around decisions and service and experience. That future is not fully here, maybe just in the movies and in episodes on Netflix, but parts of it are already sitting on your desktop and on your wrist in some wearable device. Google's new glasses technology that are coming out will actually revolutionize and move this whole process forward very quickly.
So AI is going to become an ongoing story in wealth management right alongside the same kind of conversations we've been having for decades, organic growth, enterprise value, the drivers of value for clients, what clients really want. This is all, however, going to also help shape margins. It's going to help shape client experience. It's going to shape who you hire, when you hire, what skills of the people, the competencies you're going to
be looking for in the people that you seek to hire. It's going to shape how you're doing training. In fact, I hope out of this episode, you're hearing, hey, we need as a team to maybe take a couple of certificate courses together. This is all going to shape what gets delegated, what gets eliminated, and what gets automated, and what still commands a premium because it remains deeply human. For advisors, this is not a moment to panic. It's just a moment to jump into the game, learn the tools.
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clean up your data, protect the trust, build the community, train the team, start small, but move fast enough to learn quickly and stay grounded enough not to do something reckless. Because advice is not becoming less human, the human side, it's about to be amplified. And the firms that understand that first will not just keep up, they're going to pull away. With each of these episodes, there are always a couple of open ended coaching questions for reflection. Today, there are two first,
What is the one workflow in your business today that is inefficient, repetitive, or dependent upon one person? And how could AI improve that in the next 30 days? And the second question, where are you and your team under invested in learning? And what would change over the next 12 weeks if you committed as a team to one course or certificate program together? I've included a list of those courses and some of the newsletters that I'm reading in the show notes.
And I do hope that today's episode is helping you think about how to build this muscle, not because AI is going away, but because the best in the business are leaning in right now. They're learning, they're applying, and they're evolving while keeping the advice human. Well, thanks for tuning in. And that's a wrap. Until next time, this is Ray Sclafani. Keep building, growing and striving for greatness. Together, we'll redefine what's possible in the world of wealth management.
be sure to check back for our latest episode and article.
Ray Sclafani (20:26.607)
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