Anuj Gupta has heard every version of the AI conversation. The hype, the skepticism, the “we’re already doing that” from firms that bolted together a stack of point solutions and called it a day. As Principal Director of Sales Engineering at Envestnet, Gupta spends his days having a meaningful version of that conversation—the one where you get into the data infrastructure, the governance, the day-to-day workflow impact. And what he keeps coming back to is a deceptively simple idea: time.
“Time is the most precious thing for advisors,” he says. And everything Envestnet is building right now is designed around getting more of it back.
In conversation with The Wealth Advisor’s Scott Martin, Gupta walks through how Envestnet is translating that ambition into practice—from AI-powered client insights and predictive modeling to generative business intelligence tools that let advisors interrogate their own books of business without writing a single line of code.
Starting With the Workflow Problem
Before the conversation can address personalization, client experience, or practice growth, it has to start somewhere fundamental. Most advisors are drowning in process. Pulling data from multiple systems, reconciling it, formatting it into something usable before a client meeting—hours spent on work that has nothing to do with the reason someone became an advisor in the first place.
Envestnet’s answer to the workflow problem begins with what the firm calls Insights AI. The concept is relatively intuitive: Take CRM data, financial planning data, and investment information, synthesize it all in one place, and surface what matters to that advisor at that moment. A client approaching retirement who doesn’t yet have a lifestyle goal. An investor who hasn’t been contacted in months. Rather than requiring advisors to manually comb through every account and every plan to find these moments, Insights AI brings them forward automatically.
“Think of that if you can expand that out upon tax planning opportunities, just even touchpoints to your investors,” Gupta explains. “When’s the last time I talked to this investor who has a birthday coming up? But consolidate that on a foundation that brings everything together, surface it up to that advisor, and say, ‘Here’s some things that I’m meeting with this client or I should be meeting with this client or I want to talk to this client.’“
The point isn’t to replace judgment. The advisor still decides what to do with the information. But instead of spending an hour excavating that insight from five different systems, the work is performed in advance—and the advisor walks into the client interaction already knowing which conversation needs to happen.
Predictive Intelligence, Not Just Automation
There’s a meaningful difference between rules-based automation and actual intelligence. A lot of platforms provide the former and market it as the latter. Gupta is specific about where Envestnet’s capabilities move beyond triggers and thresholds into active, informed anticipation.
One example: propensity modeling around held-away assets. By analyzing what’s known about a given investor—their complexity, their goals, the shape of their financial picture—the platform can identify a strong probability that assets exist outside the advisor’s current view. Not a certainty but a statistically grounded flag that warrants a conversation.
“Based on the data that we have overall, based on a specific investor’s makeup, there’s a potential that there’s a high likelihood that that investor has assets held away. It’s an opportunity to have a discussion about that held-away asset,” says Gupta. “So, you’re able to go in and say from a predictive perspective that there’s a high likelihood that there’s actually assets over here that you can plan on, that you can advise on, and ultimately potentially invest on.”
For advisors thinking about practice growth, that kind of signal is difficult to manufacture manually. At scale, across a full book of business, doing so becomes nearly impossible. The platform reveals what a sharp advisor might eventually find on their own—just months faster and without the manual excavation.
Generative Business Intelligence: Your Book of Business, Translated
Beyond the client-level insights, Envestnet has built business intelligence (BI) tools—a layer that enables advisors and enterprise leaders to interrogate their own books of business through conversation rather than code.
The concept addresses a real-world friction point. Business intelligence has historically required technical fluency: SQL queries, pivot tables, custom dashboards built by someone in IT. The insights that practice management teams and enterprise leaders need are locked behind a skills gap most firms aren’t equipped to close.
The generative BI tools change the equation. Instead of writing a query, an advisor asks a question. The platform translates natural language into analysis—visualizations, breakdowns, opportunity maps—drawn from the same institutional-grade data foundation that powers everything Envestnet does.
“If you could take that and say, ‘This is what I’m looking for, help me get to that through a conversational layer that can then sit on top of all of that really robust data,’” Gupta says, “now you’ve combined the breadth of the data plus the ease of really getting at what you want, which again creates time and creates good data that people can make good recommendations and have a good analysis around.”
Personalization runs through both layers. The insights served to an advisor are tailored to their specific book. The insights surfaced about an investor reflect what’s true of that client’s goals and gaps—not a generic template. The result is an advisor who can walk into a meeting armed with highly specific, relevant, timely information about that particular client, generated without hours of manual prep.
The personalization cuts both directions. Advisors arrive better prepared, and investors receive attention calibrated specifically to their situation—which, Gupta argues, is what makes the conversation worth having in the first place.
“That advisor is coming to the table with very specific items that are specific to that investor relationship—so that it’s personalized for the investor but also personalized for the advisor in conjunction, which allows for a deeper conversation because it’s relevant and on point to that investor,” he notes. “And it’s easier based on the tool sets that are deploying these to get to that discussion, to get to that personalized conversation, that personalized strategy, that personalized recommendation that goes through the advisor, but they’ve gotten to that point through a more efficient workflow and a more efficient process.”
The Human Advisor Isn’t Going Anywhere
A fair question hangs over all of this technological development: If the platform knows your clients this well and can surface the right action at the right moment, what exactly is left for the advisor to do?
Gupta doesn’t hesitate to respond. Envestnet’s tools are built to strengthen the advisor relationship, not substitute for it, so the trusted human advisor becomes more valuable, not less. What changes is how much cognitive overhead advisors have to carry before they can get to that relationship work.
“Everything that we do is to enable and empower the firms and advisors to meet the investors where they are,” says Gupta. “The advisor’s not going anywhere. Especially in a world where there’s so much information, so much automation, so much noise out there for investors, that role of the advisor is critical to create that trust, that human connection.”
Getting to a great client meeting used to require hours of prep. Envestnet is trying to compress that prep so advisors can spend the recovered time doing what builds the business most effectively—listening, planning, and earning trust.
For Firms Already Running a Stack
One group Gupta is particularly eager to talk to: firms that have already built something. In this use case, the focus is not on the advisors still figuring out where to start, but on the practices that have assembled a collection of point solutions and assume the job is done.
The risk in a multi-vendor stack isn’t any single tool—it’s the handoffs among them. Data that doesn’t flow cleanly between systems. Insights that contradict each other. Governance gaps that no one planned for because no one was thinking about the architecture holistically. More vendors also means more oversight, more due diligence, and more potential for friction to accumulate quietly until it becomes a real problem.
“That’s where it becomes even more critical,” Gupta says of the firms with existing infrastructure. “How are different point solutions talking to each other? How do I unify that data if my foundation is separated out?”
The answer Envestnet offers is a unified data layer—institutional quality, robust governance, built to power everything from individual client insights to enterprise-level business intelligence without requiring firms to stitch it together themselves. The integration between financial planning and investment management is a specific example Gupta points to: If a plan has been built, executing on it shouldn’t require reentering data, navigating separate systems, or introducing new room for error. The connection should be seamless, because the gap between planning and execution is where value is lost.
“Anytime you’re connecting pieces of data and individual technologies, there’s more potential for friction, handoffs, things like that—which again, it’s just as critical if you already have something in place to just constantly keep reevaluating and looking for rooms for opportunities and growth,” Gupta points out.
Where to Start
For anyone feeling the pull of the AI conversation but uncertain where to plug in, Gupta’s suggestion is practical. Start with a conversation. Not a demo, not a product tour—a thoughtful discussion about how data flows through your current setup, where the friction lives, and what governance looks like. The firms getting the most out of what Envestnet is building are the ones who came in willing to examine their foundation honestly.
“Focus on your data, focus on your infrastructure, make sure you have acceptable use policies,” he says. “Those are all things that are going to be really, really critical as just all of these tool sets and the way that workflows start to change versus point solutions.”
AI capabilities in the industry are real and becoming more sophisticated quickly. But the firms positioned to benefit from them are those that did the foundational work first—and the ones who didn’t will spend the next few years catching up.
For more information, go to www.envestnet.com.
Additional Resources
- Contact Envestnet
- Three Ways AI Could Improve Your Practice
- Wealth Management Technology for RIAs
- Meet the Team
______________________
Disclosures
This is a paid advertisement. Envestnet has multiple paid engagements with The Wealth Advisor including payment for participation in America’s Best TAMPs, an annual directory of investment outsourcing organizations, with a minimum asset level of $200 million, catering to financial intermediaries.
The information, analysis, and opinions expressed herein are for general information only. Nothing contained in this video is intended to constitute legal, tax, securities, or investment advice, nor an opinion regarding the appropriateness of any investment, nor a solicitation of any type. Investing carries certain risks and there is no assurance that investing in accordance with the portfolios or strategies mentioned will provide positive performance over any period of time. Past performance is not indicative of future results.
Potential transactions identified by the Insights Engine or Non-Managed Insights tool are based on concentrated positions, concentrated asset classes, and/or high cash allocations but do not include a fee analysis or other factors that should be taken into account when considering brokerage versus advisory accounts. Potential transactions identified by the Insights Engine are for informational purposes only and are not to be construed as an instruction to take any specific action. Envestnet, Inc. and its subsidiaries and affiliates are not responsible for any decisions or recommendations you may provide to your clients.
There are risks inherent in AI technology and its application in the financial sector, including embedded bias, privacy concerns, outcome opaqueness, performance robustness, unique cyberthreats, and the potential for creating new sources and transmission channels of systemic risks. Trends or potential transactions identified by AI are for informational purposes only and are not to be construed as an instruction to take any specific action. Envestnet, Inc. and its subsidiaries and affiliates are not responsible for any decisions or recommendations you may provide to your clients.
