As AI tools have proliferated across the AdvisorTech landscape over the last few years, it's become more clear what are and aren't good use cases for AI within the context of an advisory firm. AI so far does well with sifting through large amounts of information and doing something with it: e.g., creating a summary of key takeaways (as with the many AI notetaker tools on the market), matching client financial information with potential planning strategies (as tools like Conquest Planning and FP Alpha have experimented with), and serving as a single interface for advisors to interact with client data from across all of their different technology tools (as Dispatch and Milemarker have been building out). What those uses all have in common is that they start with unstructured or inconsistently-structured data (meeting transcripts, raw financial data, differently-formatted client data across multiple systems, etc.) and put it into a more structured, readable format.
Except it's worth remembering that the AI itself doesn't create the structures that shape its outputs. The Large Language Models (LLMs) behind the AI tools can't decide for themselves what the best format is for a client meeting note summary or a list of proposed recommendations or a client data interface. The human developers who create those tools need to build that structure in order to create a user experience where the person using the tool gets the most out of it. To use a musical analogy, if AI can create the melody, a human still needs to give it a structure and arrangement to turn it into something that we can recognize as a song.
This is all to say that while AI technology can make it much easier for advisors to look up, summarize, or analyze information, it still takes substantial human involvement to overlay a structure onto the LLM's output so that it's consistent, readable, and ultimately useful for the advisor using it. And in an advisory firm context, that structure is really important: Advisors must often follow very specific processes in order to remain in compliance with their firms' policies and procedures and ensure that their clients get consistent service. If an advisor asked an AI agent to open a new IRA for a client and roll their 401(k) account over into it, they wouldn't want the AI alone to decide how to complete that task: They'd want to be very sure that the technology adheres to the specific processes put in place by their firm for opening accounts, getting client signatures, moving money, investing funds, etc.
Which is why even though AI tools have taken over all manner of individual tasks for advisors, they haven't yet consumed entire workflows and processes, at least on the operational sides of firms where those processes need to be specific and consistent to stay in compliance. They can generate individual tasks (e.g., a follow-up email after a client meeting) or even be used to kick off workflows, but the workflows themselves don't need (and likely shouldn't use) AI in order to be done the same each way.
And so it's notable this month that Hubly, the provider of workflow and task management tools for financial advisors, has announced that it has built integrations with several notable AI tools including the AI notetaker Jump, the meeting management tool GReminders, and the client engagement tool Pulse360.
Hubly's role since it was founded has been to provide an Asana- or Trello-like workflow and task management tool specifically for financial advisors. To that end, it has integrated primarily with advisor-specific CRMs like Wealthbox and Redtail, to enhance the built-in workflow tools that those platforms have (but which many advisors have found lacking). It makes sense to expand that integration list to AI notetakers and other tools that pull from disparate data sources: From Hubly's perspective, because it creates another source from which workflows can be triggered other than CRMs (especially if advisors are starting to spend more time in their AI notetakers than their CRMs); while from the AI tools' perspective, Hubly provides the workflow structure that their tools can feed into without having to build the workflows into their own tools.
The other interesting implication here is that when workflow tools like Hubly can integrate with AI tools like Jump while bypassing the CRM entirely, it diminishes the role of the traditional advisor CRM even further. Hubly came about because advisors rely on the data in their CRM, but were frustrated with their CRMs' workflow tools. AI notetakers came about initially because they can save the advisor time in taking and transcribing meeting notes and doing meeting follow-up tasks, but have increasingly pushed towards a role as an interface for client information across multiple systems (CRMs, email, financial planning software, etc.) so the advisor doesn't need to navigate through their CRM to find it. If Hubly can handle workflows better than CRMs, and AI notetakers can serve as a better client data hub than CRMs, then where does that leave the CRMs themselves? And what happens if AI tools do start to build in their own workflow tools (a version of which the AI-native Slant CRM is already working on)? If advisors can manage client data and workflows while bypassing their CRM entirely, then what is the CRM even for?
Ultimately, without placing too much importance on a single integration announcement, the Hubly news is at the very least an indication of the direction where the tides are moving in the industry. AI tools are eating up the use cases that advisors used to use CRMs for, and advisors never liked their CRMs' workflow capabilities all that much to begin with. Without either of these legs to lean on, traditional CRMs stand in real danger of becoming obsolete unless they can build out capabilities that can compete with the AI notetakers'.
But the news is also a reminder of the limitations of AI technology today: While much is made of AI's ability to automate ad hoc tasks, that feature can become a liability when the tasks need to be done the same way every time. And so in the end, if AI notetakers really are aiming to become the new CRM, they'll need to build workflows that can provide the structure that the AI itself lacks – or, as in the case of Jump, GReminders, and Pulse360, they'll need to tie into other tools like Hubly that can deliver those workflows for them.
This article first appeared on the Nerd’s Eye View at Kitces.com at https://kitc.es/advisortech-feb2026, 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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