The Autonomy Problem: Advisor360°'s Plan to Rewire the Advisor's Day

At Future Proof in Huntington Beach, Milind Mehere and I sat down to confront a phrase that has been repeated into near-meaninglessness across the wealthtech industry: "AI-native." Everyone claims it. Almost nobody can tell you what it changes.

Mehere can. He took over as CEO of Advisor360° in June, stepping off the company's board and into the job at a moment when wealth management is being remade twice over — once by AI, once by the fastest wave of RIA consolidation the industry has seen. He's done this before: he built Yodle from a startup into a $200 million ad-tech business that Web.com bought for $342 million. So, when he talks about the gap between what "AI-native" is supposed to mean and what most platforms are shipping, he isn't reciting a slide. He's making a bet about which platforms are still standing in five years.

That bet, and what it means for the advisor sitting down at their desk tomorrow morning, is what we spent our time on.

The Data Problem Nobody Wants to Admit They Have

Ask Mehere the question every advisor should be asking their vendors right now — what's the real difference between a platform-built AI-native and one that's had AI features bolted onto an old chassis — and he doesn't start with AI. He starts with plumbing.

"A unified data layer is a very important concept," he told me. "What that means is that the data that we control, both what advisors put in, as well as the feeds that we get from the custodian, all the product that we have, is very important." Third-party integrations get pulled into the same fabric, normalized and cleaned so it's usable by anything built on top of it. That, he said, is the whole ballgame: "AI needs data, and AI workflows need data."

Without that foundation, he's blunt about what happens when a legacy vendor tries to graft AI onto its existing screens. "It's not going to work," he said. "There are some cases in which it will work, but those workflows are going to be very limited." The reason is almost mechanical: "Old platforms were built on, 'Hey, if I need some information, I have to go to this module, click seven times to get a screen that might be useful for me. And then I have to go to three screens to put together that information.' Now you can build AI on top of it, but what are you going to change?"

He widened the lens further than his own product. Software itself, he argued, is about to look nothing like it does today: "It's no longer going to be someone sitting in an office thinking about how you as a user are going to use the software. It's going to be a lot more dynamic." And he didn't exempt his own company from the pressure. "The current platforms, if they don't change — and this applies to Advisor360° too — are going to have difficulty competing in the marketplace."

For advisors evaluating tech stacks, that's the practical takeaway: the question isn't whether a vendor has an AI feature. It's whether the underlying data model was built to feed one.

What the Parrot A.I. Deal Taught Him

Advisor360° didn't just build its data fabric and wait — it bought its way into applied AI when it acquired Parrot A.I. in January 2025, absorbing the whole engineering team. Within about a year, that work was rolled out to clients including MassMutual and GWN Securities. I asked what the integration required, and what he'd tell another wealthtech CEO trying to skip the bolt-on trap.

He came back to the data fabric immediately. "Because we had the unified data fabric, for us to add on an AI product was a lot easier, because we knew the client workflow. We controlled the client experience," he said. When clients asked for a note-taker or a meeting-prep tool, Advisor360° already knew where the underlying data lived. "We knew exactly where to fetch the data from and what change management we had to do, faster than anybody else."

His advice to a peer CEO wasn't about engineering at all. "The main thing is to understand the use case and the business problem you're trying to solve and then figure out what does an AI-native experience look like. That's the most important," he said. "Because the rest of the stuff is just technology, and now technology is almost becoming free."

That's a notable thing for a tech CEO to say out loud, that the code is the easy part. It suggests the real competitive moat in wealthtech right now isn't a model or a feature. It's whether a firm understands, in granular detail, how an advisor's day works.

The Ugly Truth About RIA Roll-Ups

With RIA M&A and advisor breakaways accelerating, I asked Mehere how the technology behind those transitions moving data, compliance records, and client relationships from one firm to another is evolving. His answer: mostly, it isn't.

"It is still stuck in the old manual way of doing things, unfortunately," he said. He laid out why plainly. A lot of RIA consolidation is financial engineering — buying smaller firms, stitching them together, and selling the bigger combined entity for a richer multiple. "So, the buyer is not necessarily focused on operations and technology and the end client. They're focused on, 'How do I reduce costs and drive organic growth so that I can sell it for a bigger multiple.'" Those two priorities, he said, are "almost in conflict with each other," and it's why the actual mechanics of a transition get so little attention.

Advisor360° has been trying to close that gap directly. Mehere said the firm onboarded a batch of breakaway advisors over the past six months using a process built to skip the old manual data-extraction steps in favor of automation. The result: an advisor who used to wait months to move firms can now be fully onboarded "in under a month."

But he didn't let the industry off easy. "When acquisitions and roll-ups happen and breakaways happen, people are not thinking about operations and tech, which is rather unfortunate, because ultimately that's what impacts your client relationship." For advisors weighing a move, or investors underwriting a roll-up's growth story, that's worth sitting with the technology behind the transition is often an afterthought to the deal that created it and the client is the one who feels the gap.

Rebuilding the Advisor's Tuesday Morning

I pushed Mehere to strip the marketing label off entirely: what does an "AI-native operating system" change about the first hour of an advisor's day, compared with the tools they were using two years ago? He called it a layup — this is where the abstraction turned into something concrete.

The old routine, as he described it, is familiar to every advisor reading this. Come in, scan the FT or Barron's or a morning newsletter, then do mental math: which clients need a call, whose account has a fire to put out, how does everything happening in the world connect to what's happening in each portfolio. "With AI, you don't have to do that," he said.

He walked me through a live example. Advisor360° is building a product that ingests macro signals — he used, as an example, the Fed moving rates by 25 basis points — and cross-references them against every household. "Imagine if you came in tomorrow morning and the system showed you: here are 15 clients who are in the market for refinancing, and this is going to adversely impact them. You need to call three of the 15 for these reasons. That's a game changer." Then he turned the example on me directly: "Doug just moved $2 million from his investment portfolio into cash. He probably needs something else to do that, right? So, I should call up Doug to find out why he wants to do that, and maybe I can put that money to work."

That's the shape of it — micro signals from household activity, macro signals from markets, fused into a short list of who to call and why. "Advisors will know exactly who to call, what to do, and what they have to say," he said, "which is very different than scrambling to run to a meeting half-prepared."

Client Intelligence and Practice Intelligence, in Plain English

Advisor360° now serves close to two million households and 10,000 advisors, which makes it easy to talk about adoption stats and hard to talk about whether the work itself has changed. I asked for one concrete, measurable difference not a usage number, but something that tells you the workflow is different.

He pointed to two features the firm has rolled out: Client Intelligence and Practice Intelligence. Log into a household now, and the system surfaces the handful of things that household specifically needs based on its own data. Zoom out to the whole book, and Practice Intelligence flags what's slipping — as Mehere put it, "here are your top 10 clients by AUM that you have not touched base with in the last two months. You need to touch base with this client."

The useful part isn't the alert itself, it's that it's ambient and constantly recalculated. "The data is always working," he said, "and every day it changes based on market movements, additions, subtractions, deposits, things like that." That's the difference between a dashboard an advisor must remember to check and intelligence that's simply built into how the day runs. Advisor360° backs this with productivity data — hours saved, before-and-after comparisons — that Mehere says matters less for its own sake than for getting advisors to adopt it: "This makes a difference in your life and you should truly adopt it."

From Automation to Autonomy

I closed with the question that matters for a new CEO: a year from now, what does an advisor say about their day that they can't say today, and what has to be true underneath the platform for that to happen?

Mehere didn't hesitate. "A year from now I want to move away from automation to autonomy." He drew a sharp line between the two. Meeting prep that summarizes CRM notes that are only half-complete, he said, is still just task automation — the advisor walks in only "50% prepared for the meeting with your client." Autonomy is a different order of thing entirely: "the system tells them how their day is designed based on what happened yesterday, one week ago, one month ago."

The point of getting there, in his telling, isn't efficiency for its own sake. It's freeing advisors to do the part of the job that software can't touch, "human judgment, critical decision-making, and relationships." His own bar for success is disarmingly simple and, for an industry drowning in AI jargon, refreshingly human: "If the advisor says, 'Listen, I feel that every client of mine feels like they are the only client,' we have done our job."

That's the version of "AI-native" worth paying attention to not the label on a pitch deck, but whether the technology underneath quietly disappears into the background of a relationship that was never supposed to feel automated in the first place. For advisors sizing up their next platform, and for investors sizing up the next generation of wealthtech, that's the question to keep asking not whether AI is present, but whether it's doing the remembering, the noticing, and the connecting so the advisor doesn't have to.

Connect with Milind and his team at Advisor360° here.