AI in wealth management is maturing fast, but most of the conversation is still stuck on surface-level applications like meeting notetakers and basic content tools. At Future Proof’s Citywide Conference in Miami, Jeremi Karnell, Head of Envestnet Data Solutions, made a compelling case that the real battleground is shifting from “do I have enough data?” to “can I turn this data into better judgment, at scale, for every client in my book?”

From Data Scarcity to Judgment Scarcity

For years, the hardest problem in wealth management was getting clean, complete data in one place. Aggregation, normalization, and reporting were the differentiators. Today, that's table stakes.

“There was a point in time in which data was the scarcity in wealth management,” Karnell explained. “Business intelligence… is all descriptive and diagnostic in nature, all rear-view mirror looking. And that’s now table stakes. Data is no longer the scarcity in wealth, it’s judgment.”

Advisors are buried under CRM data, performance and accounting feeds, product data, and now AI-generated content and analytics. The question is no longer what happened and why—it’s what’s likely to happen next, and what the advisor should do about it for each client, right now.

That is the problem Envestnet is aiming at: using AI to transform raw data into decision-ready insight that helps advisors prioritize time, conversations, and actions across their entire book.

What Decision Intelligence Really Means

Envestnet has chosen “decision intelligence” as the organizing principle for its AI strategy. The idea emerged from a simple but powerful reframing: instead of starting with “give me more data,” start with “what decision are we trying to make?”

Karnell pointed to early AI work at Google as inspiration: “Instead of you asking for more data to help train AI models, let’s talk about what decisions that data is gonna help you make. Can we frame everything around that?”

Traditional business intelligence answers “what happened?” and “why did it happen?” through dashboards, reports, and rear-view analytics. Decision intelligence layers machine learning, knowledge graphs, and predictive models on top of that foundation to answer two different questions: “what’s likely to happen next?” and “what should I do about it?”

For advisors and home offices, that means:

  • Moving from static dashboards to continuous next-best-action recommendations.

  • Turning complex cross-platform data into targeted opportunities and risks.

  • Embedding those insights directly into workflows, rather than asking advisors to log into “one more tool.”

“Business intelligence, table stakes,” Karnell said. “Decision intelligence is really what’s driving data strategy, AI-ready data, a lot of focus on next best action… how can we make advisors smarter about where they spend time in their entire book of business, not just a fraction of their book of business?”

Why “AI Note-Taking” Isn’t Enough

Karnell is quick to acknowledge that early AI tools—like notetakers—have value. They save time and are easy to adopt. But he’s equally clear that they can’t be the end of the story for serious advisory firms.

“Things like AI note taking are great. Jump.AI is a phenomenal company,” he said. “That’s an easy use case, an early dividend for AI adoption. The real use case in our positioning at Envestnet is around judgment.”

If all an advisor brings to a client meeting is an AI notetaker, Karnell warns, they’re “showing up at parity.” Clients are increasingly using their own AI copilots, armed with their own data and interpretations. In that environment, parity is not enough.

“Our thesis around that is, if that’s all you show up at a client meeting with, is an AI notetaker, understand you’re showing up at parity,” he said. “In this day and age, they lose.”

Envestnet’s advantage, in his view, is their ability to harness the scale of the platform—trillions in assets and millions of accounts—to generate insights an individual advisor or firm could never see on their own.

“Our signal universe compared to that is 5,000 times greater,” he noted. Envestnet can “empower our advisors to show up with AI enabled solutions that are taking all of the daily trade position, valuation data… and arm them with all of the context, all of the judgment, all of the orchestration, learning loops, and all of it wrapped with all of the necessary compliance.”

Insights AI: Turning Scale into Organic Growth

That scale is not theoretical. Envestnet manages over $6 trillion in platform assets, supports nearly 20 million investor accounts, and works with more than 109,000 advisors. The “digital exhaust” generated by that activity is the raw material for Envestnet’s decision-intelligence engine.

Within that engine, Insights AI has become one of the most tangible proofs of value. The system now generates more than 25 million insights daily across roughly 90 signal types, influencing nearly half of managed-account flows.

Karnell described how that translates into real outcomes:

“Think of the digital exhaust that comes off of managing $7.4 trillion of assets under management. Feeding that into knowledge graphs that represent a third of the wealth industry, 12 to 14 million accounts, seeing patterns that exist that would otherwise be not something that anyone would see.”

Those patterns drive propensity models and prescriptive analytics—identifying where there is:

  • Single-stock concentration that needs to be diversified.

  • Brokerage assets that are ripe to move into managed accounts.

  • Tax overlay opportunities across FSP and UMA portfolios.

“Out of all of those insights, 60% of them are non-managed related,” he said. “There are a number of different growth related insights that are tax strategy based.”

For firms that fully deploy Insights AI across their advisor base, the numbers are meaningful:

  • Around 20% year-over-year growth in brokerage-to-managed transitions for enterprises that actively use the tool.​

  • Roughly $6 billion more in brokerage assets moved into managed portfolios last year for those active users compared with the prior year.​

  • A 60% lift in tax-strategy asset flows for firms leveraging tax insights versus those that do not, representing about $1.2–$1.4 billion annually that laggards are leaving on the table.​

“Looking at both of those, you know, last year we broke $36 billion of brokerage to managed assets. $1.2–1.4 billion in tax strategy assets,” Karnell said. “That is an organic growth engine for wealth advisors that adopt that tool.”

For advisors under pressure to demonstrate value, grow wallet share, and deliver more personalized planning at scale, that kind of embedded, data-driven growth engine is hard to ignore.

Generative BI: Data Access at the Speed of Conversation

If Insights AI is about “what should I do next,” Generative Business Intelligence (Gen BI) is about “show me the data I need, the way I need it, right now.”

Historically, Envestnet supported over a hundred bespoke APIs for fees, flows, performance, and other key BI metrics—each effectively a pre-built chart, grid, or dashboard. When a home office wanted a new view that wasn’t in the library, it meant a professional services project, additional cost, and weeks or months of friction.

With Gen BI, Envestnet flipped that model. As long as the data exists in the platform, users can request it through a conversational interface—“show me fees by channel over the last four quarters,” or “compare managed vs brokerage flows by advisor team”—and the system dynamically generates the needed view on the fly.

“We now allow our clients to, via a conversational chat interface, ask to see it in any way they want,” Karnell said. “We send that query to a large language model, then through its structured output, dynamically gives us the API on the fly, and then we render that view… instantaneously. There’s no fees. There’s no time. And that’s all free. We’ve democratized their access to that data.”

For advisors and home-office leaders, the practical benefits are clear:

  • Faster time-to-insight, without waiting on analysts or IT.

  • Lower cost and less dependence on custom projects.

  • More experimentation with data, because the marginal cost of asking a new question is effectively zero.

In a world where AI can surface opportunities in real time, Gen BI ensures decision-makers can drill into the “why” and the “how big” in seconds, not weeks.

From “Search and Synthesize” to “Think and Act”

The next horizon for Envestnet—and for the advisors on its platform—is agentic AI. This is where systems don’t just summarize or recommend; they coordinate multi-step actions within defined guardrails.

Karnell described the evolution as moving away from “‘search and synthesize’ AI, which is what everyone sees through a chat interface, to ‘think and act’ AI.”

In practice, that means agents that:

  • Monitor advisor activity and client context across the platform.

  • Understand the intent behind tasks like raising cash, rebalancing, or updating a plan.

  • Automatically handle time-consuming workflows such as proposal creation or service ticket generation—without the advisor having to manually re-key data.

Crucially, Envestnet is not expecting advisors to design or manage these systems themselves.

“It’s not on the advisor,” Karnell emphasized. “It’s incumbent upon a platform like Envestnet to look at its trading platforms, look at its financial planning platforms, and see where there’s opportunities to take out traditional old legacy risk, or cognitive loads, or friction.”

Agents operate within strict legal and compliance guardrails, and the vision stops short of fully autonomous AI. The aim is to let advisors “lean on Envestnet” to safely automate the busywork while they stay focused on judgment, relationships, and nuanced planning conversations.

What This Means for Forward-Looking Advisors

For advisors who plan to be in the business for the next decade, the implication is clear: AI is no longer a side project or a gadget—it is quickly becoming part of the core operating system of advice.

Karnell is candid about where Envestnet is focusing its energy: not on convincing late-career technology laggards, but on equipping early adopters who want to move beyond episodic tools and into embedded intelligence.

“Where we focus our time and energy are some of the early adopters, but that are using it again at this level of maturation that is episodic,” he said. The real opportunity is to show those firms how to move from AI as a one-off “copilot” to AI as a continuous, decision-support layer across the entire wealth platform.

For those who make that shift, Envestnet’s combination of scale, decision intelligence, Insights AI, Gen BI, and emerging agentic workflows offers something rare: an integrated, AI-enhanced ecosystem designed to generate organic growth, better tax and portfolio outcomes, and a more defensible value proposition—without asking advisors to become data scientists.

To learn more about how Envestnet’s AI and decision-intelligence capabilities can help transform your practice or enterprise, visit the Envestnet website at envestnet.com.