The 2025 RIA Benchmarking Study from Charles Schwab, which surveyed 1,288 firms representing over $2.4 trillion in assets,1 tells a story of an industry performing well by almost every measure. AUM grew 16.6% in 2024. Revenue was up 17.6%. Client retention has held steady at 97% for a decade.
And yet, within that picture of collective strength, there is a performance gap that deserves more attention than it typically gets.
Top Performing Firms (those in the top 20% of Schwab's Firm Performance Index) generated organic growth of 12.5% in 2024. Across the broader study, organic growth for most firms ranged from 5% to 9%, depending on AUM size. More striking: Top Performing Firms gained 3.8 times more assets from existing clients than all other firms.1 Same market. Same client base. Same tailwinds. Meaningfully different results.
What separates them is not the quality of the advisors or the marketing spend. What separates them consistently is infrastructure. Top Performing Firms have standardized workflows, written strategic plans, and defined client personas. They have built systems that allow their advisors to spend more time on the work that drives growth and less on the work that doesn't.
Over the last several years, we have been working toward the same question: what does that infrastructure actually look like, and how do you build it in a way that scales? The release of Claude opened an opportunity we did not have before now: to take the SOPs, documentation, templates, and workflows we have built and formalize them into a platform where AI and human judgment work in deliberate partnership.
The Time Constraint Behind the Growth Gap
Fidelity's research on what they call the Time-Value Equation puts a number on what most advisory leaders already sense. Advisors, on average, spend 59% of their week on administrative tasks, compliance, and other non-client activities,2 leaving just 41% for the clients and prospects who actually drive growth.
That is not primarily an advisor discipline problem. It is a structural one. The organic growth infrastructure at most advisory firms was not designed to keep pace with the scale of the opportunity in front of them. Demand for financial advice is projected to rise 30% over the next decade.3 UHNW wealth grew 173% between 2019 and 2023.4 The firms that capture a disproportionate share of that growth will be the ones running a consistent, high-quality organic growth program, not the ones with the best individual advisors working the hardest.
| $270Kin potential new revenue per advisor annually, from reallocating just five additional hours per week to client and prospect activity2 |
The implication is straightforward. The constraint is not effort. It is how the operating infrastructure directs time and attention. Firms that have solved this problem have not done so by hiring their way out of it. They have done it by building systems that handle the structured, repeatable work, so that advisor time flows toward the relationships and decisions that only advisors can drive.
Figure 1. Growth Infrastructure Maturity Map. Source: Marketing Wiz, 2026. Framework based on observed operating patterns across RIA firms; informed by 2025 Schwab RIA Benchmarking Study performance data.1
What AI Actually Changes and What It Doesn't
The 2025 Schwab study reports that 68% of RIA firms are now using AI in some form. The most common applications: note-taking and meeting support (43% of firms), generating marketing content (38%), and developing client correspondence (31%).1 These are real productivity gains. They reduce friction at the margins of the work.
What they don't do is change the underlying structure of the organic growth function. The variable is not whether AI is in the workflow. It is whether the operating model behind the workflow has been redesigned. A firm can use AI to draft emails and still have a business development process that runs on who remembers to follow up, and when. A firm can use AI to generate content without a supporting operating model, and still have a content program built around availability, not architecture.
The shift we are building toward starts with a different question: not which tasks AI can assist with, but how do we redesign the operating model so that AI and human judgment each do what they are genuinely suited for?
The answer to that question produces something we call the AI + Human Agent operating model. AI agents handle structured, repeatable, throughput-dependent work: intake, signal capture, content structuring, first-pass production, scheduling, distribution, and tracking. Human agents handle the work that requires judgment, trust, or authority: editorial voice, client-facing communication, relationship decisions, compliance review, and advisor approval. The handoffs between them are documented and deliberate, not improvised.
| Most firms are asking which tasks AI can help with. The more consequential question is how AI changes what the operating model can do. |
Figure 2. AI + Human Agent Operating Model. Source: Marketing Wiz, 2026. Dashed rings indicate strategic handoff points where AI transfers to human judgment. Model applied across business development, content, client communication, events, technology management, and reporting.
Content as the Proof Point
Content is a low-risk, high-return entry point for the AI + Human Agent model. The feedback loop is short, the cost of iteration is manageable, and the results are visible quickly enough to demonstrate what the operating model can do before extending it further.
Content has always been the channel through which advisors' intellectual capital reaches the market. It is how trust gets built before a conversation happens, how a firm's perspective becomes part of how prospects think about their decisions. There is nothing new about that. What has changed is what it takes to run a content program that actually compounds, as competition for client attention increases and the expectations of affluent investors continue to rise.
Most advisory firms have the raw material. Their advisors have genuine intellectual capital: views on markets, planning philosophy, hard-won perspective on what clients get wrong and why. The constraint has never been what to say. It has been the production model.
The standard model is built around availability. An advisor has an idea when they have time. It gets handed off when someone has capacity. It publishes when the calendar allows. The insight is real; the infrastructure that carries it is not consistent enough to produce a program.
The AI-augmented model shifts the advisor's responsibilities. AI handles intake, structuring, briefing, sequencing, and distribution prep. The advisor's time goes to the work only they can do: the insight, the voice, the approval. Advisor time per piece drops from three to five hours to roughly 45 to 90 minutes. Monthly output moves from one or two pieces to four to six. When that model holds, content stops being a one-off output. It becomes a compounding asset, generating the signals that inform COI outreach, event strategy, and prospecting across the firm.
| Traditional Model | AI-Augmented Model | |
| Advisor time per piece | 3–5 hours | 45–90 minutes |
| Monthly output | 1–2 pieces | 4–6 pieces |
| Content driver | Availability | Architecture |
| Intake, structuring & briefing | Advisor | AI agent |
| Sequencing & distribution prep | Advisor | AI agent |
| Insight, voice & approval | Advisor | Advisor |
| Program consistency | Variable | Systematic |
Figure 3. Content Production: Traditional vs. AI-Augmented. Source: Marketing Wiz, 2026. Estimates based on observed production patterns across advisory firm engagements. Results vary by firm size, team structure, and content type.
What the Build Actually Requires
We have spent the better part of a year mapping this infrastructure for our own practice. Three things have been consistently true.
It requires treating this as an operating decision, not a software decision. Adopting tools does not change how work flows. Defining who owns each handoff, what the review gates look like, and how information moves between functions is what changes the model. The Schwab data supports this: Top Performing Firms distinguish themselves not by the tools they use but by the standardized workflows and written plans that govern how their practices operate.1
It requires a system owner. Someone whose job is not just to use the tools but to tend the infrastructure: enforcing the process, managing the handoffs, identifying where things are breaking down. Without that accountability, even a well-designed model reverts to familiar patterns within six months. The calendar drifts. The tools become subscriptions used for one-off tasks. The system owner does not have to be a full-time role. The accountability has to live somewhere specific.
It takes time to get right. The architecture has to be mapped to how a specific firm's growth function actually works: its advisors, channels, client relationships, and content program. That mapping cannot be purchased. It is built through iteration, and it is the part that competitors cannot easily replicate.
The Schwab data is consistent on this point: the firms that outperform have built infrastructure. They have documented their processes, defined their clients, and systematized the workflows that drive growth. The tools have changed. That principle hasn't.
Closing Thoughts
The RIA industry is in the middle of an extraordinary growth cycle. The demand tailwind is real, the wealth transfer is accelerating, and the firms that have invested in their operating infrastructure are showing what is possible. The 2025 Schwab benchmarking data makes clear that the performance gap between those firms and the rest of the market is not narrowing. It is compounding.
We are documenting this build as it happens, not because we have arrived at a finished answer, but because we believe this is a conversation the industry needs to be having now. The questions around AI in wealth management are moving fast. The firms engaging with them seriously, at the infrastructure level, are building something that will hold its value as the competitive landscape continues to shift.
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Works Cited
1 Schwab Advisor Services. Insights from the 2025 RIA Benchmarking Study. Charles Schwab Corporation, 2025.
2 Fidelity Investments. "The Time-Value Equation: Optimizing Time to Unlock Growth." Fidelity Institutional Insights, 2025. Based on the 2025 Fidelity Investor Insights Study, the 2025 Fidelity Advisor Insights Survey, and the 2025 Fidelity Wealth Management Leader Study.
3 Cerulli Associates. U.S. Retail Investor Advice Relationships. 2023. Cited in Fidelity Investments, 2025.
4 Fidelity Investments. Fidelity RIA Benchmarking Study. 2024.