Potential Growth For Prospecting Tools As FINNY Raises $17M (And More Of The Latest In Financial #AdvisorTech – January 2026)

FINNY Raises $17M For Its Prospecting Technology, But Is The Market For Outbound Prospecting As Big As Its Investors' Growth Goals?

Organic growth, i.e., getting new clients via the firm's internal sales and marketing processes (as opposed to growth via external acquisitions), is an imperative for most financial advisory firms. And while there are numerous marketing and sales techniques for advisors to choose from to achieve organic growth, they really all boil down to one of two main approaches: Inbound, which seeks to get prospective clients to reach out to the advisor (e.g., via various forms of content marketing); and outbound, i.e., where the advisor reaches out to and/or tries to meet or otherwise get in front of potential clients themselves in hopes that some of them will be interested in talking further.

Overwhelmingly, advisors tend to prefer an inbound approach to growth over outbound: According to our most recent Kitces Research on Advisor Marketing, "cold" (i.e., outbound) prospecting was among the least popular business development tactics, with fewer than 5% of advisors engaging in cold outreach and those who do it being broadly unsatisfied with its effectiveness.

This is for a variety of reasons: From a practical standpoint, inbound marketing can be done on a "one-to-many" basis, so that one social media post or blog article can reach hundreds or thousands of potential clients, while requiring only the amount of time needed to create and post the content in the first place (which makes the marketing tactic very scalable). And although a non-trivial amount of time and money might be required for that content to reach a sizeable audience, eventually the amount of effort needed goes down as search engines and AI tools begin to pick it up and older content can be repurposed to reach new audiences efficiently. Outbound prospecting, by contrast, is done making connections one individual at a time, by one individual (the advisor), and so the only way to increase the volume of prospects reached is to have advisors spend more of their (very expensive) time and effort on prospecting.

And from a psychological perspective, many advisors simply find the mental effort and stress load of outbound prospecting more taxing than other marketing techniques. When making one unsolicited outreach after another, advisors often must endure dozens or even hundreds of rejections before encountering a prospect who is willing to move ahead with the conversation, whether in the form of emails or LinkedIn messages sent into the void, or phone calls that end in a quick "no thank you" or hangup (or cold knocking that ends up with a few slammed doors). While a few advisors have no trouble shaking off the rejections, for many advisors the constant grind eventually takes a toll, with each "no" making it a little bit harder to reach out to the next lead.

That said, as Nick Murray has famously quipped, prospecting is just a "Game Of Numbers", and the odds are that with enough persistence, outbound prospecting will generate at least some new clients; in fact, our Kitces Research on Advisor Marketing finds that cold-calling has a whopping 89% Success Rate for advisors, second only to getting a new client by referral from an existing client. As a result, advisors who are just starting their practice often turn to (cold) outbound prospecting in order to get enough clients to get their businesses off the ground – since at that point, they have both the time and the hunger to put in the long hours of work and endless rejections. But in most cases, once those advisors reach a critical mass of enough clients that they don't need to prospect anymore, they drop outbound prospecting in favor of inbound client referrals, perhaps supplemented by one-to-many marketing or other inbound techniques for their subsequent organic growth.

But despite advisors' relative distaste for cold calling or emailing prospective clients, there's been a flood of new investment and innovation into the Outbound Prospecting category of the Kitces AdvisorTech Map over the last two years. Startups like FINNY, Catchlight, Wealthfeed, WEALTHAWK, and Cashmere have all popped up recently to form a new generation of (AI-driven) prospecting tools, which in essence use AI to analyze a wide range of consumer marketing data to identify prospects whom it would be most worth for the advisor to reach out to. And given the industry's hunger for organic growth, those startups have had no trouble finding investment from venture capital sponsors with their pitches to solve advisors' organic growth problems: 2024 alone saw Wealthfeed raising $2 million, Cashmere raising $3.6 million, and FINNY raising $4.3 million, along with Wealthfeed adding an additional investment from Broadridge in 2025.

And this month, FINNY is in the news for yet another fundraising deal, this one a $17 million Series A round at a whopping $150 million valuation. While this round was led by the venerable VC firm Venrock, it also includes funds from notable sources like Y Combinator and Altruist founder Jason Wenk.

On one level, it's understandable that the sales pitch of tools like FINNY that they can effectively "automate" advisors' organic growth has proven effective at garnering initial interest from investors and advisory firms. With RIAs' organic growth rates generally stuck in the mid-single digits year after year, a tool that purports to solve organic growth by matching advisors with the prospects who are a good fit for them is bound to generate buzz.

But it's worth remembering that tools like FINNY are, at their core, outbound prospecting tools, since they ultimately require the advisor to reach out to the prospect (albeit one that the technology helped to identify as pre-qualified). Such that even though they automate (or at least expedite) much of the work of researching prospects, winnowing down to a list of potential good-fit candidates, and writing and scheduling the outreach itself, their primary appeal is for advisors who do outbound prospecting. Who then still have to meet with total strangers in a cold meeting, and attempt to build trust and persuade the prospects to become clients. Which, as noted above, is only a small minority of advisors overall, as even if the technology determines the prospect is a qualified fit, it's still a tough sale that will require a lot of the advisor's time wading through "no's" to get to a few "yes's".

And that in turn raises questions about the size of the actual market for advisors who would buy outbound prospecting technology, and whether that supports FINNY's most recent valuation. With FINNY reporting 400 firms using its software and charging a per-firm subscription fee of $6,000 per year, we can estimate FINNY's annual revenue at around $2.4 million – making its $150 million valuation an eye-popping 62.5 times its annual revenue, implying enormous growth expectations from its investors. But are there enough advisory firms who do outbound prospecting to justify those expectations?

Assuming FINNY's total addressable market is on the order of 100,000 advisory firms between state- and SEC-registered RIAs, and the independent broker-dealer reps with enough autonomy to buy the software for their own practice (a rough estimate, as technology buying power varies greatly among individual RIAs their advisors, broker-dealers and their reps, and intermediaries like OSJs, but close enough to start with), then their current 400 firm user base represents a 0.4% market share. But as noted above, our Kitces Research on Advisor Marketing suggests that no more than around 5% of advisors use cold outreach as a marketing technique. Likewise, our Kitces Research on Advisor Technology shows that about 5.3% of advisors use some kind of third-party outbound prospecting technology. Either way, the total share of all advisors who use outbound prospecting to bring in new clients is about 13x FINNY's current market share. In other words, if FINNY achieves a 100% adoption rate among advisors who do outbound prospecting (despite the numerous competitors who have popped up alongside it), it would grow its client and revenue base by about 13x to a little more than $30M of revenue – which would be very respectable on its own, but at the same time might not be up to the expectations of the VC funders who just invested in the company for 63x its revenue.

If FINNY wants 20x growth, it would need to sell to 8,000 advisory firms (8% of the total market), if it wants 50x growth, it would need to sell to 20,000 firms (20% of the market), and if it wants 100x growth it would need to sell to 40,000 firms (40% of the market). In any event, in order to achieve growth commensurate with what its investors just paid for their stake in the company, FINNY would need to successfully sell its outbound prospecting solution to significantly more firms than are currently doing outbound prospecting. And not lose market share to other outbound prospecting tools also raising capital and competing for the same (limited) addressable market.

Which isn't to say that it's impossible for FINNY to sell its software to 10,000 – 20,000+ advisory firms. For one thing, if the technology can solve for some of the biggest pain points of cold prospecting and get a higher hit rate of success with less rejection, maybe more advisors will be convinced to adopt it (or at least to not stop doing it once they have a sufficient client base). And although there's a great deal of competition with all the other AI prospecting tools that have arisen, there is no one dominant players in that category as of yet – meaning that FINNY's leg up on fundraising could help it establish itself as the early category leader (similar to how Jump managed to stand out amid the crowded AI notetaker category with to its own "hyperfunding" round). And if FINNY is able to generate more revenue from its power users (e.g., by expanding on its reported "success fee" for larger-firm users), it could potentially reach its growth goals without having to rely as much on expanding the entire market for outbound prospecting and instead by lifting revenue per firm for the few that excel at the approach.

But there are still major headwinds for outbound prospecting tools in gaining significant traction among advisors. Even if FINNY's AI tools make outbound prospecting easier than it has been, that still might not be enough to convince the 95% of advisors who are averse to cold outreach in general, and prefer marketing methods where the prospect (and not the advisor) picks up the phone first that tend to have a higher success rate (as our Kitces Research on Advisor Marketing has persistently found that advisors overweight lead quality over lead quantity even if the latter results in lower Client Acquisition Costs). And while it might make life easier for the firms that need to rely on cold outreach (e.g., those just getting started on building their client base), many of those firms might still only use it until they get enough clients where they don't have to rely on outbound prospecting anymore, which leaves FINNY needing to constantly replace the experienced users it loses through attrition with new advisors just getting started (who are, unfortunately, often very budget-constrained as well).

And so the big question with FINNY in particular (and AI prospecting in general) is not just whether FINNY will manage to rise above the other AI prospecting tools, but whether it can lift the adoption rate of the entire category – i.e., to convince advisors who weren't already doing outbound prospecting to pay for a tool that can help them start doing it. It's notable that there are investors in FINNY with deep knowledge of the advisory industry, including not just Altruist's Wenk but also Ritholtz Wealth Management's Josh Brown, who believe that FINNY's potential for solving a major organic growth pain point for advisors makes it worth funding its future. But it isn't easy to picture advisors who have been overwhelmingly dissatisfied with outbound prospecting rushing to pay for a solution that requires them to do more prospecting, outside of the approximately 5% of advisors who have already built their growth strategy around prospect outreach.

Schwab's Decision To Shut Down Intelligent Portfolios Premium Illustrates How Hard It Is To Offer Low-Cost Advice To Mass-Affluent Clients

In the early 2010s, the first generation of robo advisors to arrive on the scene like Betterment and Wealthfront made headlines with their promises to replace human financial advisors by automating portfolio management at a quarter or less of the standard 1% AUM fee. But in reality, the original robo advisors were never a threat to human advisors, because they didn't provide advice – all they really did was implement a portfolio based on a short questionnaire about the investor's goals and time horizons. So in reality, financial advisors (at least those who did more than just implement portfolios for a 1% fee) had no trouble holding on to the mass affluent and high net worth delegator clients that they had always served, while the robo advisors created a new lower-cost implementation option for DIYers and those without the assets to meet the minimums of human advisors.

But this arrangement left a sizeable gap in the financial services marketplace: People who wanted personalized financial advice (and not simply the implementation solution the robo advisors offered), but lacked the income or assets to pay the fees charged by human advisors, had few options to meet their needs. By this time in the mid-2010s, the second wave of "B2B" robo advisors had arisen to try to fill this gap: Instead of competing directly against advisors, they instead aimed to serve advisors directly, promising to unlock new efficiencies that would allow advisors to scale up their services and serve more clients for less cost (e.g., by offering a robo-managed portfolio with "light" financial planning to lower-asset clients, in hopes of graduating them to full-fledged wealth management clients when they built up enough assets to meet the advisor's minimums). But once again, the robo advisors misjudged the dynamics of the advisory business: Advisors hadn't been ignoring lower-asset clients because they lacked the efficiencies to serve them; rather, it was because it was too difficult to efficiently market at the scale needed to bring in a critical mass of low-asset (and low-fee) clients to run a profitable mass-market financial planning firm. And so the B2B robo advisor phenomenon also passed without leaving much of a mark on the average financial advisor's fee structure and clientele (although many advisors did ultimately adopt the technology to free up time to do deeper planning for their existing clients).

If there was one development from that era that did seem like it could have a lasting effect on the advisory industry, however, it was the attempts by giant institutions like Vanguard and Schwab to meld robo-managed portfolios with human-delivered financial advice at minimal cost. First, Vanguard launched its Personal Advisor Services in 2014, with a $50,000 asset minimum and a 0.3% AUM fee. And a few years later in 2017, Charles Schwab followed up with its Schwab Intelligent Advisory, which had an asset minimum of just $25,000 and charged 0.28% of AUM up to a maximum of $900 per quarter (and after later rebranding as Intelligent Portfolios Premium, Schwab dropped the AUM fee altogether in favor of a $30 per month subscription fee plus a $300 upfront planning fee). In both cases, the firms bundled together asset management along with "access" to a CFP certificant financial advisor – which in practice usually looked something like a handful of 30-minute phone or video sessions to create an initial plan, plus the ability to schedule additional recurring or on-demand sessions thereafter.

The thinking at the time was that as large and well-known asset managers, Schwab and Vanguard wouldn't have the marketing and distribution issues faced by the standalone robo advisors, and could run their planning services at minimal or even negative margins while still coming out ahead from the management fees generated by keeping clients invested in their firms' funds (and by earning sizeable spreads on those clients' cash sweep allocations). And so at the time there was some real concern that the offerings from the likes of Schwab and Fidelity could drive down the going rate for financial planning and create fee pressure for independent advisors who still charged 1% of AUM for what was (at least on the surface) the same thing that the name-brand firms were charging 0.3% or less for.

However, once again the threat of disruption from low-cost advice never really panned out, as is exemplified by the recent news of Schwab's decision to shutter Intelligent Portfolios Premium early in 2026.

Again the culprit seems to have been a misunderstanding of the value that clients place in financial advice. Vanguard's and Schwab's low-cost offerings made financial planning and advice into a loss leader and an overhead expense, and so there was an imperative to keep costs at a minimum – which meant more clients per advisor, less time working for and meeting with each client, and less likelihood that a client would even talk to the same advisor from one meeting to the next. But the problem is that at its core, financial advice is a relationship business: clients who pay advisors 1% of AUM per year or multiple thousands of dollars in retainer fees aren't paying just for a set of recommendations. They're paying for a person who understands them, who is deeply aware of their values and intentions, and whom they can trust to give recommendations that are in their best interest. Which is a completely different service than a series of 30-minute sessions with a rotating cast of advisors, who may be able to help with relatively simple planning questions but who don't have the deep understanding of the client to get into more complex tax, estate, or retirement planning conversations (let alone matters of financial psychology, money trauma, and financial life planning that advisors are increasingly exploring with their clients).

All of which is why, even as Schwab can't find a way to sustain a $30 per month planning service, independent full-service advisors have had little trouble maintaining or even increasing their fee levels, and PE firms can't seem to find enough cash to pour into serial RIA acquirers. Clients who want financial advice want an advisor they can have a real relationship with, and there's so much demand for that advice that advisors can charge premium fees amid the presence of lower-cost (but lighter-touch) options. Which makes it worth wondering if anyone will ever find a way to profitably serve the "missing middle" of clients who can't pay the fees charged by most financial advisors, but who want real advice from an advisor with whom they can develop a relationship. There's still a viable business model for robo advisors who can serve high volumes of clients at low fees (as the continued existence of Betterment, Wealthfront, and Vanguard and Schwab's 'pure' robo offerings show). And there's a thriving business model for human advisors who can deliver high-touch relationship-based advice at high fees. But for now at least, it appears that nobody wants to be stuck in the no-mans-land between the two.

Nevis Raises $35 Million To Eliminate Advisors' Admin Work – But Can It Really Make Advisors That Much More Productive?

Although meeting with and advising clients is the core part of a financial advisor's job, that usually isn't what they spend the majority of their time doing. As the most recent Kitces Research on Advisor Productivity showed, advisors on average spend only around 20% of their time in client meetings, with the remainder being divided among other tasks like meeting and financial plan preparation, investment research and management, marketing and business development, client service tasks, and general administrative tasks like scheduling meetings and bookkeeping. And while the most productive advisors tend to find ways to maximize their meeting time, even they only spend about a quarter of their time – i.e., 10 hours out of a 40-hour week – on client meetings.

The takeaway from these findings seems clear: Advisors who find ways to spend more time meeting with clients will in turn be able to earn more money. And even just a small boost in meeting time can result in meaningful productivity gains, since the difference between the most productive advisors (with $1 million-plus revenue) and everyone else is only about 3 hours of meeting time per week. Or to put it more directly, fitting one or two more client meetings on the calendar each week can turn an "average"-productivity advisor into a $1 million-plus-productivity advisor.

The key question, however, is how to reduce the time spent on non-meeting tasks in order to clear up time for more meetings. Which isn't a matter of simply getting 5% more efficient on administrative tasks in order to fit another 2-hour meeting on the calendar – additional meetings come with their own additional tasks, such as meeting prep and follow-up plus handling any follow-on tasks generated from the meeting itself – so it's really more like finding 5-6 hours worth of time each week for both meetings and their related tasks.

But regardless, there's clearly a business opportunity for solutions that can help advisors better leverage their time to fit more meetings in their schedule, since there's a clear correlation between the time saved on administrative tasks and the advisor's revenue-earning potential.

Hence we have technology startups like Nevis, which was in the news recently after closing a $35 million Series A funding round, which have centered their value proposition around reducing the time advisors spend on admin work so they can spend more time with their clients.

At a surface level, Nevis's sales pitch makes sense given the clear business benefits for advisors in getting administrative tasks off of their plate. Although Nevis appears to function mostly as a client meeting support tool like Jump or Zocks at the moment, it clearly has aspirations of one day handling a bigger share of advisors' operational tasks (which was almost certainly a factor in the size of the recent fundraising round, as well as the presence of VC heavy hitters Sequoia, Ribbit Capital, and Iconiq, who reportedly valued the company at $200 million post-funding).

At the same time, however, there's reason to be skeptical that technology like Nevis can really deliver the productivity gains to advisors that it promises. Our Kitces Productivity Research has shown how, while technology can help alleviate some of the frustration that advisors experience in doing day-to-day administrative tasks manually, it doesn't directly lead to actual increases in efficiency. That's because when an advisor employs technology to do a certain task, the reduction in time spent doing the task is at least partially offset by the increase in time spent navigating the technology. And even if technology can fully automate some administrative tasks, there's still an accountability burden on the advisor, who needs to check to make sure that the technology actually completed the task correctly (and troubleshoot what went wrong if it didn't). For example, an advisor who uses an AI notetaker doesn't need to transcribe or brain-dump meeting notes into their CRM following each meeting, but they do need to review the notetaker's meeting summary, client emails, and follow-up tasks to make sure they all correspond to what was actually said in the meeting. All of which is probably less annoying for the advisor than the old manual method of taking notes, but doesn't necessarily save them that much time in the end (or at least not enough to fit a whole extra meeting on their calendar each week).

Where advisors do see more meaningful productivity gains, as our research has again shown, is when they have support staff to whom they can delegate away administrative tasks entirely rather than relying on technology to streamline them (but still being accountable for actually getting the tasks done). Which on the one hand is generally significantly more expensive than using technology, but on the other hand does actually tend to clear up enough tasks from the advisor's calendar to give them more time to meet with clients and in turn boost their revenue productivity. And so for an advisor who's really looking to less time on administrative tasks and more time with clients, the best solution might not be to use a tool like Nevis to streamline those tasks (even though its sales pitch might say otherwise) – instead, it's to hire a team member to delegate those tasks to entirely!

To be fair, there's likely still value in technology like Nevis in helping advisors' support staff better leverage their time. Much as the time efficiency gains of robo advisor technology didn't accrue to advisors themselves as much as to their support staff (who had often been the ones doing the day-to-day trading, rebalancing, and account opening in the first place), AI tools that can streamline back-office tasks like client onboarding could increase the number of clients that each back-office team member can support – and thus reduce the number of support team members that advisory teams need to hire to serve the same number of clients. Or to put it differently, while advisors may still need to hire some support staff in order to delegate tasks off of their own plate and increase their productivity, investing in technology can increase the team's capacity such that the advisor can wait longer before hiring more staff.

Ultimately, then, the value proposition for tools like Nevis may come more from helping advisors reduce their overhead costs (in the form of fewer support staff team members) than from directly increasing advisors' revenue productivity potential. Which could still be a substantial business opportunity if they can help advisory firms save on the order of 5% of their gross revenue on staffing overhead costs – but that isn't the same as opening up new revenue productivity potential, which is what the size of Nevis's Series A round implies. Or in other words, while it's much sexier to pitch a product that will boost revenue productivity (with a theoretically unlimited ceiling) than one that will reduce overhead (which is finite by definition), the reality is that even the most efficiency-boosting technology generally stays in the overhead-reduction category – which might be a multimillion dollar business opportunity, but it isn't likely a billion dollar opportunity.

ChatGPT Launches Embedded Morningstar And Pitchbook Apps To Compete Directly With Standalone AI Investment Research Tools?

Since the public launch of ChatGPT just over three years ago in late 2022, the general consensus that's formed around the app is that while it can reasonably replicate or summarize text in a human-sounding way, it doesn't necessarily analyze or interpret data very well. This is true for highly technical domains such as tax and law, where there's still a very real possibility of ChatGPT misinterpreting areas of the tax code or simply hallucinating (i.e., making up) sections of the law or case histories. But it's also true when it comes to numbers: While ChatGPT can do some basic math with two- or three-digit numbers, it struggles with bigger numbers and more complicated equations to the point that it's hard to trust it for any kind of real data analysis.

Which has meant that ChatGPT has typically not been a particularly good tool for doing financial-related analysis such as analyzing sequences of returns or crunching company financial data. While asking it for, say, the trailing 10-year performance of the S&P 500 will generally yield an accurate result, that's not because it's analyzing the raw price and dividend data from the index and calculating the compound total return over the last 10 years; instead, it's just scraping the web for publicly listed information that matches the request and pulling in the result from websites like YCharts. And so to the extent that the answer to a numbers-related question is on a public website somewhere (e.g., for returns of major market benchmarks), ChatGPT can be reasonably expected to bring in the correct result (though it's still always worth checking against the original source just to be sure). But when it comes to information that's not publicly listed – such as more proprietary analytics that might be behind a paywall, or private company information that isn't filed publicly with the SEC – it's harder to rely on ChatGPT to come up with a result worth trusting.

It's notable, then, that this month Morningstar announced that it has launched two new apps within the ChatGPT platform: One providing access to Morningstar's own public markets data (including its popular fund ratings, research, and analyst reports), and one with similar access to its subsidiary Pitchbook's research and intelligence in the private markets space.

The key aspect to this deal is that it gives access to users within ChatGPT of the proprietary data and research held by Morningstar and Pitchbook that isn't available on the open web. Notably, in order to use the apps, users must still be subscribed to Morningstar and/or Pitchbook, so this isn't necessarily about making that information "free" to anyone using ChatGPT. But it does make it so that Morningstar and Pitchbook subscribers who are users of ChatGPT, and who would value its chatbot interface and ability to save custom models for repeatable workflows, can plug their Morningstar data into the app without having to switch over to the Morningstar platform itself.

The news also shows that for companies that generate unique and proprietary information (e.g., investment research and analysis), and who don't list that information out on the public Internet where bots like ChatGPT can scrape it into their models, there's a real business opportunity to monetize that information by licensing it to ChatGPT and the like. Even though both Morningstar and Pitchbook have their own in-house AI tools within their respective platforms, there was enough of a demand for their data to also be available in ChatGPT that it made sense to set up this integration. Now Morningstar gets to charge for both the data, and for the privilege of having it available on the user's platform of choice.

At the same time, this news may not be so good for other third party investment research tools. The last few years have quietly seen an explosion in the number of AI investment research tools, from Boosted.ai to Fiscal.ai to Financial AI to Qdeck. Which has in part been a response to the fact that general-purpose tools like ChatGPT have been so unreliable for investment research and analysis, creating an opportunity for these companies to build their own front-end solutions pairing their own proprietary investment analytics with an LLM interface (perhaps even using OpenAI's own ChatGPT models). But now there's less reason to buy a third-party app if users can get reliable investment data from Morningstar within the ChatGPT interface itself.

But the key point is that while apps like ChatGPT have largely "figured out" the open (i.e., non-paywalled) internet, and can pull information from almost anywhere to provide an answer to a user's question (though the reliability of that answer still varies depending on how much interpretation is left up to the LLM), there's a whole world of private information that's been mostly walled off from ChatGPT and its ilk. Nearly every website requiring a login, from proprietary research sites to banking and financial apps, has information that has yet to be scooped up into LLM models – leaving the question of what other information the AI companies would like to pay for to have access to within their apps, and who stands to get paid for it?

Zeplyn Pivots Towards "Agentic AI" As The AI Notetaker Market Reaches Its Saturation Point

For most of 2024 and 2025, the Client Meeting Support category was one of the fastest growing sections of the Kitces AdvisorTech Map. This was thanks to a plethora of new AI notetaker tools that sprang up once entrepreneurs realized the potential for Large Language Model (LLM)-based tools to solve a major pain point for advisors around client meetings: The need to prepare before the meeting by reviewing old client information to bring up relevant talking points; the need to take detailed notes during the meeting (while somehow managing to stay present in the conversation itself) and log those notes in the advisor's CRM or archiving system, and the need to perform all manner of tasks after the meeting like sending a summary email to the client and assigning follow-up work to other advisory team members. All of which, according to Kitces Research on Advisor Productivity, can take on average around two hours to complete for each client meeting.

But as more and more AI notetakers piled onto the Map, it became ever more clear that the actual market for AI notetakers wouldn't be enough to sustain the 15-plus providers jostling to compete with each other. This has become even more true since other technology platforms have begun to roll out their own AI notetaking tools (e.g., Advisor360's Parrot AI, Altruist's Hazel, and Wealthbox's in-house AI notetaker) that could eliminate the need to use a third-party solution altogether. And with Jump emerging as the clear category leader (garnering a nearly 10% adoption rate in the most recent Kitces Research on Advisor Technology, which was conducted even before Jump's $20 million capital raise that allowed it to lean even further into marketing and acquire Mobile Assistant along with its user base), and Zocks coming in as a relatively distant second, the writing seems to be on the wall for the remaining AI notetakers: Either pivot to a different use case, try to get acquired by another AdvisorTech company to integrate into their platform, or else it will be a hard road ahead to remain a viable business. The only question is when we'll reach that tipping point, and how quickly other solutions will combine, shift categories, or drop out of the race entirely once the exodus begins.

Now we're starting to see signs of that tipping point beginning to arrive, as with the news this month that Zeplyn is redesigning its platform and launching new "agentic" AI tools, representing a shift away from its prior branding as a straightforward AI notetaker.

There's been a lot of buzz about agentic AI as the next frontier in AI technology, but it's still somewhat unclear what shape agentic AI would take in an advisory firm context. Zeplyn's version appears to act as an interface for firmwide client information, pulling in data from client meeting notes, CRM records, emails, and documents to answer advisors' questions and perform tasks like composing email responses to client questions. For instance, if an advisor wants to see how much a certain client has contributed to their IRA this year, they can simply ask Zeplyn's chatbot which can pull in the information itself, rather than the advisor needing to log into their custodial platform and find the correct client account.

But while that sounds useful enough, the challenge with agentic AI tools in the advisory world so far is that it's been hard to identify exactly which problems that they help solve for advisors. If the value proposition is "it can do anything or answer any question", it ironically becomes difficult to point to any one specific problem that it does solve. And so the challenge for Zeplyn, as it has been for most tools in the AI Assistant category of the AdvisorTech Map, will be to articulate what it can really help "assist" with. Or to put it differently, a freeform chatbox that can answer any client-related question often sounds great – until it's time to actually ask it a question, at which point simply finding the wording to ask the "right" question to ask can become its own blocking point.

But from a broader perspective, Zeplyn's shift towards agentic tools is an indication that, after more than a year of explosive growth in the Client Meeting Support category followed by a gradual plateauing, we may be seeing the signs of an exodus of companies that either pivot away from meeting notes or drop off the Map altogether. Because as is unfortunately often the case in AdvisorTech, there is really only room for one or two "winners" in any given category, and the AI notetaker field appears to be no exception. And while the rapidly shifting landscape of AI may create some hope among other providers that Jump and Zocks aren't as entrenched as they seem, the early signs of pivoting from providers like Zeplyn shows that not everyone plans to wait to find out.

Advisor Platforms Compete With Varying Approaches To Be The "AI Layer" To Client Data  - But Which Model Will Advisors Use?

For almost as long as there has been advisor technology, the persistent problem has been client data living in multiple systems. In the early days when software was housed on floppy disk drives, CDs, or hard drives, almost all client information needed to be keyed separately into each and every piece of software that the advisor used, since there was no way for those different programs to "talk" to each other (and the "source of truth" for client data was often their physical client file folder!).

But in the 2000s, as software began migrating into the cloud – essentially living online instead of being in local physical file folders or on individual local computers – there was all of a sudden a new ability for those tools to connect to each other using APIs. So over the last two decades, technology providers (some more diligently than others) have built integrations with one another, allowing data to migrate between tools so that, for instance, changing a client's address in one program would be automatically reflected in all the other tools that were integrated with it.

But despite the promise of software integration, in reality it became – and in many cases remains – a major point of frustration for advisors who use technology. Because the integrations between software tools are built by the software providers themselves, the existence and quality of integration between any two tools is 100% dependent on whether the tools' providers decide to integrate with one another, what type of integrations they decide (or are able) to build, and how effectively they maintain the integration over time. Which is challenging for most independent technology providers, as the lack of any clear standards for advisor and client data means every data integration with another partner is a unique (and costly and time-consuming) one-of-a-kind build.

The end result of this is a fragmented landscape of inconsistently integrated software tools, and some headache-inducing technology decisions for advisory firm owners. For example, if an RIA uses Software A for their CRM and Software B for their financial planning platform, and is trying to decide between Software C and Software D for their portfolio management, what do they do if Software C integrates with Software A (but not Software B), and Software D integrates with Software B (but not Software A)? For the most part, advisors have needed to muddle through such decisions and pick what they think will be most beneficial in the short term, and hope that in the long term the quality of integrations will improve enough to connect everything together eventually.

For a number of years, data warehousing was touted as the solution to advisors' challenges of managing and integrating data from different systems. As the sales pitch from providers like AppCrown, Skience, and MileMarker went, advisors who "owned their data" (i.e., piped it into their own data warehousing solution) had full control over how to use that data and how it was sent out from one application to another, and because the different tools only needed to connect to the warehouse "hub" (and not individually to each and every other software tool), there were fewer connections to maintain and therefore better and more consistent connections across the whole tech stack. And while all of this was true in theory, the reality all too often ended up being that once advisory firms invested in a costly data warehousing solution and had all their data sitting in one centralized place, they then felt the pressure to then do something with that data. Which often led to advisory firms building their own expensive custom software solutions to live on top of their data, effectively building and maintaining their own version of the same custom data integrations that technology providers already were, but without the scale of a technology provider… which mean their data systems now "talked to each other" with a solution that would have been much cheaper if they just kept using existing third-party tools with their own integrations to do most of the same things.

But in more recent years we've seen an increasing number of AI tools aiming to bridge the gaps left by inconsistent or nonexistent software integrations. Rather than relying on software providers themselves to clean up and standardize data across so many API connections, these AI tools can theoretically go back and forth between tools on their own to sync the data across them all. In other words, it doesn't matter if Software A has an established integration with Software C or Software D, since the AI tool can simply tie them all together and port information back and forth as if they had been integrated using existing open APIs. Furthermore, the AI tools can serve as an interface for all of the data they're harnessing from different systems, giving advisors a single place to find information pertaining to a client no matter which system that information actually lives in.

The big question, however, is that if these AI tools do become the solution for advisors' data integration woes (and it's still too early to tell exactly how reliable they are), where should these tools themselves live? Because as of now, there are various tools in advisors' tech stacks that are competing to be the "AI layer" unifying their client data across all of their systems.

For instance, the multifunction technology provider Advisor360 recently unveiled what it calls an "AI-native wealth operating system", with AI serving as a bridge between its performance reporting, onboarding, CRM, and notetaking tools and plugging into advisors' external tools as well. And Orion has released more details about its much-anticipated Denali AI tool that weaves together information from its own multiple CRM and portfolio management and financial planning systems. Meanwhile, other third party tools are seeking to be a standalone AI data layer, such as Dispatch (which syncs together and standardizes data from multiple sources without actually storing the data itself). And even some of the original data warehousing tools like Milemarker are rolling out AI tools to connect and query different systems – suggesting that maybe the need for advisors to "own their data" was overstated all along and that the real need was simply for a (AI-driven?) tool that could sync data together regardless of where it was stored.

With all these different solutions competing to be advisors' AI layer to normalize and integrate data across multiple systems, it remains to be seen which model (AI layers built on top of existing incumbents, or standalone AI-layer-that-integrates-them-all solutions) will eventually take hold. What is clear is that advisors don't need multiple AI layers at once, since the whole purpose is to have one unified solution that connects the data across all of their different systems. But with so many providers pouring money into their AI investments, in the short term at least it appears that advisors will have multiple tools in their tech stacks competing for the status of the One Data Source To Rule (or at least Manage) Them All – and so it will come down to which one actually does the best job of connecting to and moving data around the advisor's whole tech stack. In other words, in a world where AI can handle all the work of integrating advisor technology, it doesn't matter whether that AI is a part of an existing software tool or stands on its own – the only thing that matters is whether it can do a good job delivering on its promise of solving the advisor's integration problems.

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In the meantime, we've rolled out a beta version of our new AdvisorTech Directory, along with making updates to the latest version of our Financial AdvisorTech Solutions Map (produced in collaboration with Craig Iskowitz of Ezra Group)!

So what do you think? Will advisors who dislike outbound prospecting be convinced to give it a try using tools like FINNY? Will Nevis automate enough manual work away for advisors to actually add more meetings to their calendar? Are we at the "tipping point" for AI notetakers where most will need to pivot to a new use case in order to survive? Let us know your thoughts by sharing in the comments below!