FutureProof Citywide | SoBe Stage | March 8
The AI Demo Drop session at FutureProof Citywide was not another showcase of shiny technology looking for a problem to solve. It was a sharp, fast-moving glimpse into how artificial intelligence is already being embedded inside modern advisory firms.
The format was simple: a series of fintech firms, each given only a few minutes to show how AI is being applied in practice. No long theory. No vague futurism. Just live propositions, real use cases and a clear sense of where the industry is heading.
What emerged was striking. AI in wealth management is no longer just an experimental layer sitting on the edge of the business. It is becoming part of the infrastructure of advice itself.
These tools are not replacing advisors. They are beginning to sit underneath them, supporting the work that too often consumes time, capacity and attention: data analysis, meeting preparation, client communication, portfolio intelligence, workflow management and relationship prioritization.
Chaired by industry thinkers including Diana Cabrices and Brian Portnoy, the session captured a defining shift. The next phase of advice will not simply be about who has the best planning software or the largest client base. It will be about which firms can use intelligence, automation and human judgment together most effectively.
Slant: Turning Complexity into Clarity
Slant, led by co-founders Thomas Clawson and Max Metcalf, delivered one of the most compelling demonstrations of the session.
Its proposition is simple but powerful: take the overwhelming complexity of client financial data and turn it into clear, actionable insight.
Advisory firms sit on huge volumes of information. Portfolios, cash flows, liabilities, tax exposures, planning assumptions, estate considerations and behavioral clues all exist somewhere inside the client relationship. The challenge is that this information is often fragmented across systems, spreadsheets, notes and conversations.
Slant’s AI engine is designed to bring that data together and translate it into advisor-friendly insight. The value is not just aggregation. It is interpretation.
The platform surfaces what matters: risks, opportunities, planning gaps and suggested actions that advisors can use immediately in client conversations. In practice, this has the potential to remove hours of manual preparation and replace it with real-time intelligence.
That changes the role of the advisor. Instead of acting as a data processor, the advisor becomes a decision interpreter — the person who understands the client, applies judgment and brings clarity to complexity.
For firms trying to scale high-quality advice across hundreds or thousands of households, that shift is significant.
Nevis: The AI Co-Pilot for Advisory Firms
Nevis, under CEO Mark Swan, positioned itself as an AI co-pilot for advisory businesses.
Where Slant focused on insight, Nevis leaned into execution. Its platform integrates across the systems that already sit inside advisory firms — CRM, planning tools and communication channels — to automate the workflows that consume so much advisor time.
That includes meeting preparation, follow-up notes, compliance summaries, client recaps and action points. The AI does not simply record what happened. It helps anticipate what needs to happen next.
One of the strongest elements was automated meeting intelligence. An advisor can come out of a client conversation and receive a structured summary, clear next steps and even draft follow-up communication.
That is not a small efficiency gain. It goes directly to the capacity issue facing many advisory firms. Advisors are not short of work. They are short of time to do the work that matters most.
Nevis represents a broader theme in the market: AI as invisible infrastructure. When it works well, it should not feel like another piece of technology to manage. It should feel like the firm simply runs better.
Perscient: Intelligence at the Portfolio Level
Perscient, represented by Ben Hunt and Jeremy Radcliffe, brought the investment lens to the AI conversation.
Its focus was on portfolio construction and investment intelligence — using AI to analyze large volumes of data and translate that information into advisor-ready insight.
That distinction matters. Advisors are not suffering from a lack of information. If anything, they are overwhelmed by it. Market signals, fund performance, macroeconomic data, manager commentary and product developments arrive constantly. The issue is not access. It is interpretation.
Perscient’s value lies in filtering signal from noise. Rather than adding more data to the advisor’s desk, it seeks to contextualize that data and turn it into usable recommendations.
For advisors, the opportunity is twofold. They can make faster, more informed decisions, but they can also explain those decisions more clearly to clients.
That may become one of the most important roles for AI in investment advice. Not to create a black box, but to support a more transparent, better-evidenced conversation between advisor and client.
Advice.ai: Scaling Personalization
Advice.ai, led by Brian Pierson and Kevin Knull, addressed one of the most persistent bottlenecks in financial advice: personalized communication at scale.
Clients expect relevance. They expect timely communication. They expect their advisor to understand what matters to them, especially during moments of volatility, uncertainty or major life change.
The challenge is that highly personalized communication is difficult to deliver consistently across a large client base.
Advice.ai is designed to help advisors communicate in a way that feels specific, timely and relevant without requiring every message to be manually created from scratch. The platform uses client data, behavior and preferences to help shape communications that speak more directly to the individual.
That has obvious value during market volatility, tax deadlines, retirement transitions, inheritance events or significant planning moments. But it also speaks to a bigger point.
The future of advice will not only be about better financial plans. It will be about better ongoing engagement.
Clients do not judge their advisor only by the annual review meeting. They judge the relationship by whether they feel seen, understood and supported throughout the year. AI may become one of the key tools that allows firms to deliver that feeling at scale.
CurrentClient: Rethinking Client Intelligence
CurrentClient, founded by Dustin Belliston, focused on the core of every advisory business: the client relationship.
Rather than acting like a traditional CRM, CurrentClient uses AI to turn client data into active relationship intelligence.
The platform helps identify patterns inside the client base: who needs attention, who may be at risk, where opportunities exist and where an advisor should focus time today.
That last point is crucial. One of the biggest challenges inside advisory firms is prioritization. Advisors have full calendars, full inboxes and long lists of tasks. Not every client need is obvious, and not every opportunity announces itself clearly.
CurrentClient is trying to answer a very practical question: where should the advisor’s attention go next?
That moves the firm from reactive service to proactive relationship management. It also supports better client outcomes, because the advisor is prompted to act before issues become problems or opportunities are missed.
In a business built on trust, that kind of intelligence is not merely efficient. It can be deeply valuable.
VastAdvisor: Scaling the Advisor Model
VastAdvisor, led by Ian Karnell, addressed one of the structural challenges facing wealth management: scalability.
The traditional advisory model has often relied on adding more people to serve more clients. But that model becomes increasingly difficult as firms grow. Headcount is expensive, talent is competitive and client expectations continue to rise.
VastAdvisor’s proposition is to help firms scale through technology rather than simply through more staff.
Its platform combines AI-driven automation, client servicing tools and workflow optimization to help advisors increase capacity without weakening the client experience.
What stood out was the breadth of the approach. VastAdvisor is not trying to solve only one narrow point in the advisory process. It is looking across onboarding, servicing, communication and firm workflows, then using AI to create a more connected operating model.
For growing firms, that matters. Scale is only valuable if quality is preserved. The firms that can serve more clients while maintaining a strong client experience will have a real advantage.
The Bigger Picture: From Tools to Infrastructure
The power of the AI Demo Drop was not any single product. It was the collective signal.
Across Slant, Nevis, Perscient, Advice.ai, CurrentClient and VastAdvisor, a clear pattern emerged.
AI is collapsing time. Tasks that once took hours can now be completed in minutes, or even seconds.
AI is enhancing judgment. The strongest tools are not trying to remove the advisor from the process. They are trying to give the advisor better context, better preparation and better insight.
AI is enabling scale without stripping out personalization. That may be one of the most important developments for the next decade of advice.
Most importantly, these tools are no longer theoretical. They are live, deployable and increasingly relevant to the way advisory firms operate today.
As Brian Portnoy has often argued, the future of advice is not simply about better numbers. It is about better human outcomes.
There is an irony here. AI is often described as impersonal. Yet, used properly, it may be one of the things that allows advisors to build more human, more responsive and more meaningful client relationships.
The advisor who uses AI well does not become less human. They may become more available, more prepared and more relevant.
Final Thought
The AI Demo Drop was not about technology for its own sake. It was about what happens when intelligence becomes embedded across the advisory business.
The firms on stage were not just pitching features. They were showing how the operating model of advice is beginning to change.
For advisory firms, the question is no longer whether AI will matter. It already does.
The real question is how quickly firms can move from curiosity to integration — and whether they can use these tools not just to become more efficient, but to become better advisors.
If this session was any indication, the future of advice has already arrived.
