AI for Advisors

About AI for Advisors

Exploring the intersection of AI & money.

Publisher

Mark Heynen, James Cantwell

Category

technology

Language

en

Explicit

No

First episode

18 Aug 2025

Latest episode

1 Sept 2026

Latest episodes

39 episodes in the feed.

1. 

   1 Sept 2026

   Running a Company Through AI Agents: Ian Karnell @ VastAdvisor

   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 it Why 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 early The internal structure of VastMind, VastAdvisor's ~40-agent system, including its "board of directors" and function-specific guilds How the team catches and fixes production code errors in real time, with human review calibrated to the severity of the fix Why Ian considers AI telemetry and observability — not marketing-content compliance — the harder unsolved problem for regulated AI deployments How VastAdvisor manages token costs across enterprise and individual AI subscription plans Ian's take on whether loyalty to any one AI lab or model will still matter in two to three years Key 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."

2. 

   20 Aug 2026

   What Comes After the AI Note-Taker: Aaron Klein @ Contio, Tom Fields @ Fynancial

   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 OS How Tom Fields built Fynancial as the first partner on Contio's MeetingOS, and what that partnership actually does for advisors Why locking up client data is a losing long-term strategy, even if it's tempting short-term Why older clients default to their phone as their primary computer — and what that means for advisor tech decisions Why meeting prep should happen a week or two out, not 30 minutes before The rapid-fire debate on the most overrated and underrated AI technology in wealth management right now Key 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.

3. 

   13 Aug 2026

   Why AI Adoption Fails: Vineet Mohan @ FastTrackr AI, Frantz Widmaier @ Altitude CRM

   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.

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5.0 / 5

6 ratings

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