Every piece of technology was new at one point, and even the most widespread technology today was once only used by a small group of early adopters who saw its potential and were willing to try it out, live with the initial bugs as the software got improved, and take the risk that it might not last for the long term. Some technology never quite makes it out of this early adopter phase, but if it does, it has to transition from a small and relatively homogenous user group – risk-taking, relatively nerdy, and often fond of using new technology just for the fun of trying new things – to a wide and diverse group of mainstream users who tend to be far less tolerant of problems and just want confidence that the software will work the way it's supposed to without making them have to figure it all out themselves (a leap that Geoffrey Moore famously dubbed as "crossing the chasm").
General-purpose generative AI took little time to burst into the mainstream, with ChatGPT becoming one of the fastest-adopted consumer products in history a few months after its launch in late 2022. But advisory firms on average tend to take more time to adopt new technology than the population as a whole: According to research by Schwab, fewer than one-third of advisors were using any AI tools by mid-2023, and while by the end of 2025 that number had crept up to nearly two-thirds, much of that adoption appears to have been via experimentation by individual advisors and employees, not RIAs broadly adopting their own firmwide systems.
One reason that many advisory firms tend to take a conservative approach towards new technology is that in a client-facing, fiduciary business, it's often too great a risk to put client data or firm operations in the hands of new and unproven products: Many providers don't survive long enough to see significant adoption, and given how disruptive it can be for firms to change technology, most of them would prefer not to be forced to do so on short notice. Not to mention the very real client data and privacy fears that arise in the context of AI in particular, which can be solved but have taken time for the industry to build proper protocols to address.
But the other reason for slow adoption amongst advisors in particular is that when new technology comes along, it doesn't tend to come from the existing 'incumbent' providers that advisors already use; instead, it usually arises from newer startups that offer it on a standalone basis. For example, when AI notetakers first came on the scene, they all came from new companies that existed solely to do AI notetaking (plus other related functions that they built in over time, like email follow-ups and task management). It wasn't until a year-plus into the AI notetaking boom that highly-used incumbent providers like Wealthbox and Nitrogen began to incorporate their own AI notetakers. And it's simply harder to get advisors to adopt a new technology when doing so requires buying yet another piece of standalone software, rather than having it available within the tools they already have. In an environment where most advisors use two or three core pieces of software (and two or three providers make up most of the market share within each of those core areas), and the rest of the landscape is a huge swath of scattered, mostly niche tools, a new type of tech going mainstream often requires getting into one of the tools that advisors already use and trust.
Which is why it's notable to see that Advyzon, the all-in-one portfolio management and CRM platform, has announced this month the rollout of "Advyzon AI", a suite of new AI tools incorporated throughout its platform. Built on the client and portfolio data that already lives within the Advyzon system, Advyzon AI includes features like AI meeting notes, client meeting prep summaries, analysis and summarization of uploaded client documents, and next action recommendations.
As an incumbent software provider (and particularly a multipurpose platform like Advyzon), there's a balancing act to strike when it comes to incorporating new technology into your platform. On the one hand, if the new technology proves popular and effective and you wait too long to incorporate it, you risk being disrupted by early movers (as may be the case in the CRM category, as incumbents like Wealthbox and Redtail which were slow to incorporate AI are now being challenged by AI-native startups like Slant). But on the other hand, waiting gives you time to see how things shake out in the marketplace (e.g., which features or use cases prove popular, and which ones fail to live up to the hype), so that by the time you do roll out the technology you can incorporate that information by building exactly what advisors are known to want, and make your product better by offering exactly what they will actually use.
Advyzon's new AI feature set suggests that it has spent time surveying the landscape of what has and hasn't worked with regards to advisor AI, and built versions of what has already become popular elsewhere to incorporate in its platform: Advyzon users who were using standalone tools like Jump, Zocks, VRGL, or Altruist's Hazel can now find most of those tools' features bundled into Advyzon's platform (and advisors who weren't using those tools already can now get their first exposure to AI through Advyzon). Meanwhile, AI use cases that have so far fallen relatively flat – like AI prospecting and the idea of a unified AI 'operating system' – are absent (in the latter case, likely because Advyzon has always aimed to be the 'operating system' – even pre-AI – by building portfolio management, CRM, and eventually financial planning software onto a single platform and codebase).
Which ultimately shows how for technology, going from 'early adopter' to 'mainstream' can be a self-reinforcing cycle: The technology is picked up first by early adopters, incumbents stay on the sidelines and wait to see what features become popular among the early adopters, the incumbent finally releases their version that focuses on what the early adopters like, and then the incumbent's mainstream users 'discover' the technology and become users themselves. From the competitive landscape perspective, this is the great hazard in building new tech in a heavily saturated industry like financial services… even if the new capabilities are meaningful and valuable, the challenge is still getting enough distribution to gain traction with advisor users, before incumbents copy the feature set and do it themselves. When it comes to individual advisors, though, this means that the way AI will likely be adopted by the firms who haven't already picked it up yet will not be through purchasing one of the new innovators in the AI category, but instead by being incorporated into an established platform that they already use (like Advyzon) that has cherry-picked the most user-friendly and popular AI features to add in.
Most financial advisors' service models include managing their clients' investments (and charging a percentage of those assets under management). But most advisors don't really manage investments at a granular, single stock- or bond-level; instead, they allocate their clients' portfolios to third-party asset managers who do the actual day-to-day research and trading. Historically, throughout much of the 1980s and 1990s, advisors mainly allocated to active mutual fund managers, because in those days advisors were primarily paid on commission, and mutual funds were structured to pay sales loads or 12-1b distribution fees on top of the management fees paid to the fund company, which made mutual funds lucrative both to the fund companies that created them and to the commission-compensated advisors who sold them.
But over the last 30 years, as advisors have been increasingly paid for advice rather than product sales, and have largely incorporated the principles of Modern Portfolio Theory into their investment philosophy, the dominant model of investment management has shifted from picking active mutual funds and plugging them into a client's portfolio to constructing diversified asset-allocated portfolios designed around the client's risk tolerance. Which in turn led to a shift first from mostly active to mostly passive mutual funds (which could serve as low-cost 'building blocks' from which advisors could build their portfolio models), and then from passive mutual funds to ETFs (which could do the same job as a passive mutual fund but with generally lower cost and more tax efficiency).
In the last decade or so, however, there have been signs of yet another large-scale shift in how advisors allocate their clients' portfolios. As robo-advisors like Betterment have made it possible for consumers to get a diversified, asset-allocated ETF portfolio at a fraction of the cost of a human advisor, advisors have increasingly sought to differentiate themselves by using separately managed accounts (SMAs), which allow for more personalization and tax management of clients' portfolios than the ETF building block model. For example, a direct-indexing portfolio held within an SMA can be designed to broadly replicate the performance of an index like the S&P 500 while adjusting the holdings to account for the client's preferences around specific ESG factors. And a Tax-Aware Long-Short SMA can add 'extensions' of short and leveraged long positions to an existing portfolio that will generate tax losses regardless of whether markets broadly go up or down. And improvements in technology over the years has brought down the costs of SMA portfolios to the point where many of them can be managed for less than 1% per year (which is less than what most of the actively-managed mutual funds charged in their heyday, despite being a 'customized' portfolio being managed directly for the client rather than being pooled together with the funds of thousands of different investors to manage at scale).
But one of the challenges of employing SMAs for portfolio management is that, like all third-party investment managers, advisors must do due diligence on SMA managers to ensure their products and strategies work as advertised and would serve in their clients' best interests. And unlike mutual funds and ETFs, which as registered investment companies must publicly report their prices and performance and therefore can be analyzed based off of public data gathered by platforms like Morningstar, FactSet, and YCharts, SMA managers are not required to publish their results, meaning there's no public dataset for advisors to analyze the performance of a particular strategy – or perhaps more importantly, to benchmark the performance of one SMA manager against others that implement a similar strategy.
Which makes it notable that this month the news came out that YCharts is acquiring the investment data and analytics software provider Zephyr, which owns one of the industry's largest proprietary data sets on SMA managers.
The centerpiece of the deal appears to be Zephyr's Plan Sponsor Network (PSN) database, which compiles portfolio and performance data for over 21,000 different SMA products going back 40+ years (which SMA managers voluntarily report to PSN on a quarterly basis). The addition of PSN data will presumably allow YCharts to include it in their investment analytics and proposal generation tools, giving them a better value proposition for advisors incorporating SMAs into their clients' portfolios. That allows YCharts to not only tap into the surging popularity of SMAs overall, but also to expand more 'upmarket' in their reach towards enterprise-level firms, given that SMAs, with typical account minimums of $1 million or more, tend to be used primarily by higher net-worth clients, and those clients are more likely to be served by bigger RIAs.
At a broader industry level, Zephyr's (and PSN's) acquisition shows how providers in the business of data analytics perceive the value of the data that underlays their tools. The fact that YCharts opted to acquire Zephyr outright rather than simply licensing PSN data to incorporate it into its software indicates that they see additional value in owning the data itself – either through exclusivity (e.g., by walling the data off from competing investment analytics tools to differentiate its own offering) or through monetization (e.g., by licensing the data to competitors, more of whom will want access to SMA data as overall interest in SMAs increase). Whatever the strategy, YCharts is now in a position to control the 'pipes' of the industry's biggest SMA dataset, at a time when SMAs are exploding in growth – which is good for YCharts and its users, but less so for users of competing analytics platforms who may need to pay more for PSN data or possibly be blocked from it altogether.
The big question going forward, then, will be whether or not the current popularity of SMAs really represents a large-scale shift in how advisors manage portfolios (akin to the earlier shifts from active mutual funds to passive mutual funds to ETFs). With higher account minimums, less liquidity, and generally higher fees than ETF-based portfolios, SMAs aren't likely to achieve the level of broad-market popularity enjoyed by ETFs, barring significant structural changes. But if technology continues to drive down the cost of SMA investing and it carves out a prominent niche among HNW investors, then the need for SMA data will at the very least not decline going forward, and could very well increase further in the years ahead – which positions YCharts well to capitalize with its new ownership of the primary source of that data.
This article first appeared on the Nerd’s Eye View at Kitces.com at https://kitc.es/advisortech-august2026, and has been reprinted here with permission.
Ben Henry-Moreland
Ben Henry-Moreland is a Senior Financial Planning Nerd at Kitces.com, where he specializes in writing and speaking on financial planning topics including tax, practice management, and technology. He also co-authors the monthly Kitces #AdvisorTech column. Drawing from his experience as a financial planner and a solo advisory firm owner, Ben is passionate about fulfilling the site’s mission of making financial advicers better and more successful.
Michael Kitces
Michael Kitces is Head of Planning Strategy at Focus Partners Wealth, which provides an evidence-based approach to private wealth management for near- and current retirees, and Focus Partners Advisor Solutions, a turnkey wealth management services provider supporting thousands of independent financial advisors through the scaling phase of growth.
In addition, he is a co-founder of the XY Planning Network, AdvicePay, fpPathfinder, and New Planner Recruiting, the former Practitioner Editor of the Journal of Financial Planning, the host of the Financial Advisor Success podcast, and the publisher of the popular financial planning industry blog Nerd’s Eye View through his website Kitces.com, dedicated to advancing knowledge in financial planning. In 2010, Michael was recognized with one of the FPA’s “Heart of Financial Planning” awards for his dedication and work in advancing the profession.
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