Advertise on podcast: AI for Advisors

Most of the AI conversation in wealth management is about capability: which model, which connectors, which platform can do the most. But advisors using these tools keep running into a different wall. They don't know what to ask. When Advyzon launched its AI, a handful of users asked for a list of everything they could ask it. The honest answer was that the list would be a hundred pages long. That changed how the team builds. Less focus on what the AI can do, more on how advisors find their way to it. James Cantwell and Mark Heynen sit down with Dirk Pearson, COO of Advyzon, and Kelly Fitzpatrick, VP of Product, to talk about what that means for an all-in-one platform in a world of MCPs, Claude for Financial Advisors, and agents that can navigate almost any system on their own. WHAT YOU'LL LEARN - What Dirk, Kelly, and James expect to change most for advisors in the next 12 months, from how firms are staffed to firms that run around the clock - Why Advyzon's decade-old bet on one platform and one data model looks different now that AI depends on context - How Kelly's background as a CFP shapes the way she models advisor workflows, and why "simple" actions are hard to automate - Why Advyzon built its AI around the client record first, starting with meeting prep - How Advyzon thinks about Claude for Financial Advisors, data security, and whether to become a connector - Whether any platform can stay closed when agents can work through a browser - Dirk and Kelly's prediction for how advisors will split time between chat and traditional screens in two years - The one question every RIA should ask an AI vendor before handing over client data KEY TAKEAWAYS The bottleneck is discoverability, not capability → Advisors who don't know what to ask won't get value from even the best AI. Advyzon saw more adoption by building prompts into familiar workflows than by adding capabilities. Context matters more than model choice → Kelly calls it overrated to assume a better model means a better experience. In advice, the value comes from understanding the client, the firm, and the data underneath. Simple-looking actions carry hidden context → Sending one email can depend on what the client said last time, their risk profile, an RMD coming due, and a change in their plan. That's what makes advisor work hard to automate well. Chat won't fully replace the screen → Dirk and Kelly expect a 70/30 split between chat and UI in two years. Visual tools still matter, partly because heavy AI users already feel chat fatigue. Openness may no longer be a choice → If agents can operate a browser, people will reach the data whether a vendor offers an API or not. That changes the open-versus-closed debate for every platform. CHAPTERS 00:00 Will AI kill us all? An SNL detour 01:10 Welcome Dirk Pearson and Kelly Fitzpatrick 02:29 What changes most for advisors in the next 12 months 04:35 One platform, one data model: the all-in-one bet in the AI era 12:05 From CFP to product: why simple advisor actions are hard to model 14:14 Giving AI the right context, starting with meeting prep 20:31 The all-in-one vs. Claude for Financial Advisors 25:50 How open should a platform be? 32:05 Beyond the usual integration map 36:00 Will Claude for Financial Advisors succeed? 38:31 Tasks advisors should never have to do again 41:51 Chat vs. point-and-click: the 70/30 bet 45:00 Personal AI: fantasy football, kids' schedules, and agents that sell 52:55 Rapid fire: vendor questions, overrated vs. underrated

AI has made building software nearly free. For most industries, that's the whole story — ship faster, iterate faster, done. In wealth management, it isn't. The moment AI touches anything client-facing, the question stops being "what can this do" and becomes "can you show a regulator exactly what it did, and why." Ian Karnell has spent the last few years living on both sides of that line. He's co-founder and CEO of VastAdvisor, which he built after co-founding and selling Truelytics to Envestnet. But this conversation isn't really about VastAdvisor's product — by the hosts' own admission, they barely get to it. It's about what Ian has built inside his own company: a roughly 40-agent internal AI system, segmented into its own board of directors, executive functions, and departmental "guilds," handling everything from live code fixes to compliance review. Along the way, Ian makes the case for why governance and telemetry — not features — are becoming the real differentiator for anyone deploying AI in a regulated industry, and why he thinks loyalty to any single AI model won't mean much in a couple of years. What You'll Learn How VastAdvisor's holding-company structure (VastAssembly) separates core IP ownership from the operating businesses built on top of itWhy Ian moved off month-to-month contracts once he had proof of product-market fit, and how his earliest customers got locked into legacy pricing for staying earlyThe internal structure of VastMind, VastAdvisor's ~40-agent system, including its "board of directors" and function-specific guildsHow the team catches and fixes production code errors in real time, with human review calibrated to the severity of the fixWhy Ian considers AI telemetry and observability — not marketing-content compliance — the harder unsolved problem for regulated AI deploymentsHow VastAdvisor manages token costs across enterprise and individual AI subscription plansIan's take on whether loyalty to any one AI lab or model will still matter in two to three yearsKey Takeaways Governance is becoming the product.→ In a regulated industry, proving what your AI did — and why — is starting to matter as much as what the AI can do. Product-market fit is a spending decision, not a talking decision.→ Ian's advice to founders is blunt: paying customers, not user interviews, are the real signal that something works. The agent swarm isn't a demo — it's the org chart.→ VastAdvisor runs internal functions like compliance review and code fixes through a segmented system of dozens of AI agents, not a single assistant. Token spend is a strategic line item, not overhead to minimize.→ Ian treats AI costs the way most companies treat payroll: something to lean into, not shrink. Nobody is fully loyal to one model anymore.→ VastAdvisor is built to be LLM-agnostic on purpose, because the labs themselves are moving too fast to bet the company on just one. Why This Episode Matters Most AI conversations in wealth management are about customer-facing tools — chatbots, portfolio commentary, marketing copy. This episode is a reminder that the bigger shift may be happening behind the scenes, inside the companies building those tools. When a 25-year veteran of the space says his own company runs largely through an agent swarm, and that the harder problem isn't building the AI but proving it behaved correctly, that's a signal about where scrutiny — from regulators and from clients — is heading next. For advisors and wealthtech leaders evaluating AI vendors, that reframes the diligence question. It's less "what can this do" and more "can you show me that it did that."

AI note-takers promised to fix advisor meetings. Adoption is nearly universal — and yet client retention hasn't meaningfully improved, and most advisors still walk into reviews without a real game plan. So what did the industry actually solve? Aaron Klein, who spent 12 years building Riskalyze into a category before starting over, argues the note-taker wave started at the wrong end of the problem: capturing conversations is easy, turning them into decisions and follow-through is the actual job. His new company, Contio, is built as an operating system layer rather than a standalone app — meant for partners to build on top of, not to lock advisors into. That's exactly what Tom Fields did. As the first partner built on Contio's MeetingOS, his company Fynancial takes that meeting data and turns it into the always-on mobile experience advisors' clients already expect from every other app on their phone. Together, they make the case that the real unlock isn't smarter transcription — it's open data, faster meeting prep, and giving clients a channel that isn't buried in email. What You'll Learn: Why Aaron Klein calls AI note-taking "starting at the wrong end of the problem"The difference between an "operating system" and a "platform" — and why Contio deliberately chose OSHow Tom Fields built Fynancial as the first partner on Contio's MeetingOS, and what that partnership actually does for advisorsWhy locking up client data is a losing long-term strategy, even if it's tempting short-termWhy older clients default to their phone as their primary computer — and what that means for advisor tech decisionsWhy meeting prep should happen a week or two out, not 30 minutes beforeThe rapid-fire debate on the most overrated and underrated AI technology in wealth management right nowKey Takeaways: AI note-takers solved the wrong problem.→ Recording a meeting was never the bottleneck — turning what gets said into what gets done was. An operating system beats a platform.→ Contio isn't trying to maximize time in its own app. It's built so partners like Fynancial can build entire products on top of its data layer. Locking up data is a losing strategy.→ Firms that don't expose clear APIs or MCP access will lose customers to those that do — full stop. Speed is a relationship advantage, not just an efficiency one.→ In a trust-driven business, how fast you respond to a client is itself part of the service. Meeting prep should happen days before the meeting, not minutes before.→ The easier prep gets, the more tempting it is to do it last-minute — which defeats the point. Why This Episode Matters:The AI note-taker wave made it easy to believe the meeting problem was already solved. This episode makes the opposite case: recording a meeting was always the easy part. The advisors and platforms that win from here will be the ones that treat meeting data as fuel for faster decisions and open ecosystems — not another walled garden. Aaron Klein and Tom Fields are building from opposite ends of that same idea, and the partnership between their companies is a live example of what an open, decision-first approach to meeting AI can look like in practice.

Every advisor who's changed firms knows the drill: a blank intake form, a client asked to re-explain a hundred data points they've already given someone else, and a 90-day scramble that leaks 10-20% of assets out the door along the way. The tools to fix this have existed for a while. What's actually been missing is someone willing to own the entire mess — not just the data pipes, but the paperwork, the tracking, and the hundred spreadsheets nobody can find. This week, Mark and James talk to two founders tackling that exact problem from opposite directions. Vineet Mohan spent 14 years at HSBC before building FastTrackr AI, which handles advisor transitions end-to-end rather than just connecting data sources. Frantz Widmaier inherited a 45-year-old consulting firm, Bill Good Marketing, and is turning it into Altitude, an AI-native CRM built around an assistant called Pathfinder. The conversation keeps circling back to the same tension: the technology to move faster is already here. What's slow is getting advisors — and their clients — to trust it. What You'll Learn: - Why advisor transitions still take up to 90 days and why that delay costs firms real assets - The three distinct reasons advisors change firms — demographics, M&A, and breakaways — and why the "unlock" AI offers differs for each - Why "plug and play" is a myth for multi-step, edge-case-heavy workflows, even if it works fine for something like meeting assistants - What happened when Altitude's users pushed back against a better, but different, CRM interface - Why building AI features fast can outpace your users' ability to accept the change - How Salesforce going headless and MCP-friendly changes the build-vs-integrate calculus for CRM startups - Why the "harness layer" — the logic sitting between the model and the system of record — may be the more durable asset than either the model or the CRM - How advisor and client attitudes toward AI security are shifting Key Takeaways: Adoption fails on change management, not capability. → The tools already exist. The bottleneck is trust. Advisor transitions leak real money, not just time. → 10-20% of assets fall away during a firm change due to a slow, impersonal process. Plug and play works for narrow tasks, not messy workflows. → A meeting assistant can be plug and play; a multi-step transition still needs a human in the loop. Speed of building doesn't equal speed of adoption. → Shipping fast can backfire without walking users through the change in phases. The application layer is becoming the durable asset. → As models and CRMs commoditize, the "harness layer" between them may hold the real advantage. Why This Episode Matters: Most AI-in-wealth-management conversations focus on what the technology can now do. This one is more useful because it focuses on what still gets in the way after: users resisting a redesigned CRM, clients quietly more comfortable with AI than advisors assume, and a transition process that's slow less because of missing data than a poor experience. The practical takeaway for advisors: ask less about what a tool can do, and more about how a vendor plans to walk your team and clients through the change.

Most firms think AI changes software. The bigger shift is that AI changes workflows. As AI becomes the primary interface for getting work done, it's forcing firms to rethink a fundamental question: does the CRM still matter—or is it becoming obsolete? In this episode of AI for Advisors, James Cantwell and Mark Heynen sit down with Thomas Clawson, Co-Founder of Slant, to explore why AI-native software looks fundamentally different from legacy CRM platforms, what advisors actually need from a system of record, and why the future may be less about adding more technology and more about making existing workflows disappear. Why AI-native CRM isn't just legacy software with a chatbot attached Whether AI agents will replace CRMs—or make them even more important Why advisors still need a trusted system of record How AI is changing client expectations before they ever meet an advisor The tradeoffs between building specialized tools versus all-in-one platforms Why customer experience has become a competitive advantage for advisor technology How Slant went from marketing automation to one of wealth management's fastest-growing AI-native CRMs The CRM isn't disappearing → AI changes how advisors interact with software, but firms still need a trusted source of truth. AI shifts the focus from software to workflows → The goal isn't more tools—it's fewer manual processes. Great advisor technology reduces operational complexity → The best platforms eliminate spreadsheets, disconnected apps, and repetitive work. Clients are arriving more informed than ever → AI isn't replacing advisors—it changes the conversations clients expect to have. Relationships remain the competitive advantage → AI can automate tasks, but trust is still built between people. The real question isn't whether AI replaces the CRM. It's whether today's CRM is designed for the way advisors will work tomorrow. As AI takes over more operational work, the firms that succeed won't necessarily have the biggest technology stack. They'll have systems that connect information, automate routine tasks, and help advisors spend more time where they create the most value: with clients. That's the conversation this episode explores.

AI is quickly becoming more than just a productivity tool—it's becoming a teammate. Mark Heynen and James Cantwell welcome Freedom Dumlao, CTO and Chief AI Officer at Vestmark, to discuss how AI agents are reshaping wealth management. Drawing on his experience at Amazon and Vestmark, Freedom shares how firms can safely deploy AI, why governance matters as much as capability, and what the future looks like as AI begins taking on real operational work. From AI employees and local models to advisor workflows and infrastructure, this episode explores what it takes to bring AI into regulated financial services responsibly.

What if advisors have been approaching financial planning the wrong way all along? In this episode of AI for Advisors, James Cantwell and Mark Heynen are joined by Jeff Coyle, Founder & CEO of Libretto, to discuss why advisors should be managing total wealth—not just investment portfolios. Jeff explains how his experience serving ultra-high-net-worth families led to a new planning methodology that goes beyond traditional risk questionnaires and Monte Carlo simulations. The conversation also explores how AI is streamlining client onboarding, enhancing advisor expertise, and making more sophisticated financial planning accessible at scale. If you're curious about where financial planning is headed—and how AI can help advisors deliver more meaningful advice—this episode is for you.

AI is reshaping financial advice—but not in the way many expected. Mike Barrasso of WealthReach and Matt Halloran of Zocks join Mark Heynen and James Cantwell to discuss what advisors are actually doing with AI today, why branding matters more than ever, and how AI assistants are moving beyond simple note taking. Midway through the episode, Derrick Kinney makes a surprise appearance to share his perspective on authentic communication, client trust, and why human connection remains an advisor's greatest advantage. A practical conversation about AI adoption, advisor growth, and where the industry is headed next.

Private markets are becoming more accessible—but the infrastructure supporting them hasn't always kept pace. James Cantwell and Mark Heynen are joined by Logan Henderson, Founder & CEO of Gridline, to discuss how AI and modern technology are transforming alternatives operations, due diligence, and reporting. They explore the challenges RIAs face when implementing private market programs, where AI can create real efficiencies, and why the future of alternatives may depend more on infrastructure than access.

In this episode of AI for Advisors, Mark Heynen and James Cantwell sit down with Jason Quinn @ Advisor360° and Dr. Joshua Wilson @ Neubefi to talk about where AI is really taking advisor technology.The conversation explores how AI may reshape the wealthtech stack, why firms may move away from fragmented point solutions, and how AI-native operating systems could change the way advisors work.Jason shares how Advisor360° is thinking about unified data, workflows, and infrastructure, while Dr. Joshua Wilson breaks down why trust, identity, emotional resonance, and human connection may become even more important as AI tools become more common.They also discuss whether AI can help advisors scale relationships, why generic AI content is not enough, and how firms can stand out when everyone has access to the same technology.Topics include:AI-native operating systems for advisorsThe future of the wealthtech stackWorkflow automation and advisor productivityTrust and differentiation in an AI-driven marketHuman connection as a competitive edgeFavorite AI tools like Claude, Claude Code, and WhisperFlowAs AI raises the baseline, the real winners may be the firms that combine smarter technology with a stronger human signal.

AI is moving from hype into infrastructure. In this episode of AI for Advisors, Mark and James sit down with Alex Schlesinger of Advent to discuss what it actually looks like to build AI inside a large enterprise WealthTech organization — and why most firms are still approaching AI the wrong way. The conversation explores the tension between innovation and legacy systems, how large firms think about AI adoption, and why the future may belong to flexible AI platforms instead of one-size-fits-all solutions. What we cover: • Building AI inside large WealthTech enterprises • Why “AI-first” thinking often fails • AI infrastructure vs AI products • MCP, orchestration, and digital employees • Operational efficiency in wealth management • The future of advisor technology stacks Chapters: 00:00 – Dogs, tracking tech, and digital employees 07:00 – OpenClaw, AI agents, and orchestration 24:00 – Alex’s background and Advent journey 31:00 – Building AI inside large organizations 45:00 – Why most AI implementations fail 57:00 – The future of AI platforms in wealth management About AI for Advisors: Conversations exploring how artificial intelligence is transforming wealth management, advisor workflows, and the future of financial services.

Most advisors want to serve business owners — but many still lack the tools, data, and confidence to advise them well. In this episode of AI for Advisors, Mark Heynen and James Cantwell talk with Jason Early, Founder & CEO of RISR, about why business owners remain one of the biggest missed opportunities in wealth management. They discuss how advisors can move beyond rough business valuations, use better data to create deeper planning conversations, and help owners think through succession, exit planning, risk, and long-term goals. They also explore how AI is changing the advisor’s role, why relationship-building still matters, and why technology should support trust — not replace it. Featuring Jason Early, Founder & CEO of RISR.

In this episode of AI for Advisors, Mark and James sit down with Allen Darby, Founder and CEO of Alaris Acquisitions, to discuss how AI is beginning to reshape the future of RIAs, M&A, and wealth management operations. Allen shares insights from years of advising RIA transactions, including how buyers evaluate firms, why cultural fit matters more than price alone, and how AI could eventually impact valuations, staffing, growth, and advisor-client relationships. The conversation also explores the rise of AI note takers, automation in advisory workflows, and whether AI-powered firms could eventually command a premium in the market. What we cover:• The state of RIA M&A today• Why younger advisors are selling earlier• How Alaris uses AI for buyer-seller matching• The role of culture and compatibility in acquisitions• AI’s impact on advisor operations and staffing• Whether AI-ready firms could command higher valuations• The future of advisor-client relationships in an AI world Learn more about Alaris Acquisitions:https://www.alarisacquisitions.com

Most AI conversations in wealth management focus on efficiency. This one is about something harder: growth without breaking trust. In this episode of AI for Advisors, Eden Ovadia, founder of FINNY, shares how he’s building a modern, AI-native experience for financial advice — while staying grounded in the reality that trust is still the product. The conversation explores why most AI growth strategies feel inauthentic, how younger users actually want to engage with financial advice, and why the next generation of advisor experiences may look completely different from today’s model. If you’re thinking about AI purely as a productivity tool, you may be missing the bigger shift. Key Takeaways: • AI doesn’t replace trust — it amplifies it (or destroys it) • Growth in financial services is shifting from distribution → experience • Younger users expect real-time, contextual engagement • Most advisor tech is built for firms — not end clients • The winners will balance scale + authenticity Chapters: 00:00 – Intro & Eden’s background 04:12 – What FINNY is building 09:30 – Why most AI in finance feels inauthentic 15:05 – The real role of trust in AI-powered advice 21:40 – How younger users want to engage 28:10 – Scale vs personalization 35:25 – Why advisor tech misses the end user 42:50 – What good AI experiences feel like 50:15 – The future of financial advice 57:30 – Closing thoughts Learn more about FINNY: https://finny.ai

Most advisor technology isn’t broken because it lacks features.It’s broken because it’s solving the wrong problem. In this episode of AI for Advisors, Mark Heynen and James Cantwell sit down with Churni Bhattacharya, Chief Product Officer at Amplify, to unpack why so much wealthtech fails to deliver real impact—and how AI is forcing a rethink of how advisor platforms should actually be built. Instead of chasing more tools, Churni makes the case for experience-first design, where the goal isn’t adding functionality, but removing friction across the advisor workflow. The conversation goes deep on: why advisors default to tools instead of outcomeshow growth creates hidden operational complexitywhy “all-in-one” platforms often fail in practiceand why AI will only work if the underlying system is designed correctlyIf you’re evaluating your tech stack—or thinking about how AI fits into your firm—this episode will challenge your assumptions. Takeaways: Most wealthtech is feature-driven, not outcome-drivenFirms adopt tools to solve narrow problems, but end up increasing complexity across the system.Growth breaks systems faster than technology can fix themAs firms scale, hidden inefficiencies compound—AI can’t fix broken workflows.“All-in-one” platforms often fail because they ignore real user behaviorAdvisors don’t operate in clean, linear workflows—platforms need to reflect reality.AI is not a solution—it’s an amplifierIf your process is good, AI makes it better. If it’s broken, AI makes it worse faster.The future is experience-first, not tool-firstWinning platforms will design around how advisors actually work—not how software is structured. Chapters: 00:00 — Intro & setupMark and James introduce Churni and frame the core problem in advisor tech. 04:30 — Why advisor tech feels fragmentedHow the industry ended up with tool sprawl instead of cohesive systems. 10:15 — The “wrong problem” in wealthtechWhy most platforms optimize for features instead of outcomes. 17:40 — Growth creates operational chaosWhat actually breaks inside firms as they scale. 24:10 — The myth of the all-in-one platformWhy consolidation doesn’t always solve complexity. 31:30 — Where AI fits (and where it doesn’t)Why AI won’t fix bad systems—and what it can do. 38:20 — Designing for real advisor workflowsWhat an experience-first platform actually looks like. 45:00 — Final thoughts & future outlookWhere wealthtech and AI are headed next.