Advisors reclaimed 200 hours. Platforms will capture the value.

AssetMark's 2026 Advisor Insights: Artificial Intelligence Report, released September 15, puts a number on a payoff the advice industry has been promising for years: more than half the advisors who have adopted AI-integrated tools say they are reclaiming at least four hours a week, which annualizes to more than 200 hours, or 26 full working days, in the firm's survey of 400 U.S.-based advisors.

The headline writes itself, but the follow-up is the more informative question: what are advisors doing with the software?

Meeting notes and summaries top the list at 45 percent of adopters, followed by automated performance reports and dashboards at 43 percent, research summarization and risk analysis tied at 42 percent, and workflow and scheduling automation at 40 percent; eighty percent expect to lean harder on the tools over the next twelve months.

Every entry on that list sits adjacent to the client conversation—capture, summarize, format, schedule—and the work AI is doing is the work that keeps an advisor from the job. This publication has argued that AI's value for advisors would accrue to whoever owns the connector rather than whoever owns the model, and a use-case list headlined by meeting notes is hard to read any other way: a note is a transcript plus a CRM write-back plus an archive that satisfies compliance. The model is the commodity in that chain.

A note-taking habit is also the stickiest form of adoption available, because once summaries flow into the client file, the tool sits on the compliance record, and replacing it means rebuilding an archive—which suggests the note layer, not the interface, is where renewals get decided.

The benefit figures run the same direction: half of respondents name improved work quality as a primary benefit, 43 percent cite business growth, 41 percent point to a better client experience, and 40 percent credit higher firm revenue. Separately, 85 percent say AI has helped them expand the kinds of clients they serve—a distinct measure from the 85 percent who report adopting AI-integrated solutions to some degree, though the two sit close enough on the page to get quoted as one.

The 200-hour figure deserves a cooler read than it will get, because it is self-reported time savings collected by a firm that sells the technology, and self-reported savings measure perceived capacity rather than output. Capacity is the easiest thing in an advisory practice to absorb: four hours a week is one more client meeting, one more review cycle, one more prospecting call. An advisor who reports that AI has widened the client types she can serve is, most plausibly, serving more households with the same headcount; for a platform paid on assets, growth is the more valuable outcome, even if efficiency is the language that gets quoted.

A 400-advisor sample is a reasonable instrument for a directional read on a fast-moving practice pattern and a thin one for precision; AssetMark is not a disinterested narrator, and it does not have to be. The Concord, California-based TAMP runs about $91.8 billion in platform assets across 456,453 accounts, and the advisors making these adoption decisions are its customers. Michael Kim, the firm's chief executive, frames the finding the way a platform would: advisors face increasing pressure to deliver personalized service while managing greater complexity, and the technology has to create capacity without pushing their judgment out of the client relationship.

Alex Pape, the chief product and technology officer, is blunter about where the bar has moved: the AI conversation in wealth management has passed adoption, he says, and the question now is whether the tools actually make advisors better at the job, and what they do with the hours. Both statements are true; neither is the whole report.

The report also turns up a divergence between registered investment advisors and independent advisors affiliated with broker-dealers, though the coverage does not carry the size of that gap—the part worth having, because the gap is where the interesting argument lives. Whichever way it runs, the likeliest explanation is procurement architecture rather than advisor aptitude: in the broker-dealer channel, technology gets bought once, by a home office that also owns supervision, archiving and the books and records.

In the RIA channel it gets bought a thousand times over, by principals who adopt whatever they can implement without breaking the CRM, and that asymmetry decides how the connector question gets answered in each channel. It also suggests the integration gap widens rather than closes as the tools get more capable and the plumbing work gets heavier. Similar use-case lists across both channels would be the surprising result; a persistent sophistication gap that traces to who signs the contract is the one to expect.

The platform layer has already placed this bet. In August, the conclusion was inescapable: AI's wealth payoff now runs through the client meeting, where Practifi's Sentir, Morningstar's Gemini arrangement and AssetMark's own Talk Tracks all aim at the advisor-client conversation, and the caveat from that piece holds—which one lands depends on how clean the underlying data is. Vanguard's tax-AI deal with Altruist pushed the same contest into the meeting itself.

AssetMark's own marketing has moved in step: on September 1 the firm hired a former Russell marketing chief to lead brand and advisor engagement, a distribution hire at a company whose research arrives quotable and arrives every September. The hire is consistent with a platform that wants its research read by the people who buy from it.

Risk analysis is the one use case on the list that touches the advice itself, and it sits at 42 percent, behind note-taking and tied with research summarization; that number, not the 200 hours, is what the 2027 edition should be judged against. If the ordering holds while the hours keep accumulating, the industry will have bought itself capacity and spent it on the work it was already doing.