Building an AI tech stack that works for clients, advisors

Advancements in artificial intelligence have the power to revolutionize wealth management — everything from lead generation to meeting notes. Yet how to compliantly integrate and use these tools is still being debated by both regulators and advisors.

And because the technology is advancing so rapidly, firms will have to decide for themselves how to best proceed.

Exactly how to do that came into focus during Financial Planning’s recent AI Virtual Summit, a precursor to the upcoming second annual Advise AI conference in Las Vegas.

The session on “Building an AI Tech Stack That Works for You and Your Clients” (moderated by this reporter) featured panelists Parker Ence, CEO of Jump; Oleg Tishkevich, CEO of Invent; Sindhu Joseph, founder and CEO of CogniCor; Doug Fritz, co-founder and executive chairman of F2 Strategy; and Timothy Welsh, president of Nexus Strategy.

Firms mostly left on their own as regulators slow to catch up

Regulatory bodies like the SEC and FINRA have yet to provide prescriptive directions on AI, though various states are attempting to pass laws related to the technology.

“The [Office of the Comptroller of the Currency is] being slow to produce any kind of guidance that it’s OK to start using AI for internal operations,” said Fritz.

Because this technology is evolving so rapidly, each firm is mostly left to its own devices to interpret existing regulations, said Ence.

“They’re having to use their best judgment, which I think is good, because you don’t want regulators to get too far ahead of technology,” he said.

READ MORE: Advisors clamor for estate planning tools as attorneys wave red flags

One example of a decision firms have to make is whether to archive specific types of client communications. While there are rules around archiving emails, there is currently no consensus as to what to do when live conversation data is captured, such as what Jump does, said Ence.

“Suddenly we’ve got things like federal and state wiretapping laws,” he said. “Do we need to archive video, audio and transcriptions the same way that we would archive an email? Or can it be treated like a page of handwritten notes that can be disposed of? We’ve seen firms take different positions on this.”

Some have determined that if there’s a video or audio, they have to archive it in the same way they do an email, said Ence. Others don’t think they have to archive it, but also don’t want it discoverable for a long time if there’s a claim. They may want to auto-purge it after a certain span of time.

“We are building a product for all of those scenarios to make all those different chief compliance officers happy,” he said.

The potential of agentic AI in prospecting and lead generation

More familiar uses of AI like answering questions or rewriting emails mean the technology serves in an assistant-like role. With agentic AI, human oversight is still required to create clear instructions and fine-tune the processes, but it can act more as an autonomous agent, said Tishkevich.

“They become your virtual worker bees,” he said.

READ MORE: How much time AI saves advisors — and how they spend it

In a marketing and prospecting context, Welsh said advisors can use agentic AI in sales funnel management.

“You can say, ‘Give me all the physicians in northern California who are thinking about selling their practice,'” he said. “And then text them a message saying, ‘Hey, we specialize in this. Would you like to set up an appointment?'”

From there, those leads can be optimized and scored, said Welsh. Firms like FINNY, WEALTHAWK and Catchlight are examples of AI-powered prospecting and lead generation fintechs that are already operating.

“We haven’t even started the first inning,” he said. “We’re still in the warm-ups, catching fly balls out in the outfield, before we even start the game.”

Intelligence layer versus a ‘single source of truth’

But how should firms approach the implementation of AI into tech stacks? Differing business models, such as those of Joseph and Tishkevich, have emerged to attempt to answer that question in various ways.

Even as these AI-powered tools seek to save time and increase efficiency, Joseph said she is concerned that the issue of tech fragmentation will become even more pronounced.

“Advisors today use eight to 10 tools to switch between the planning, custodian platforms, portfolio management, market insights and compliance tools; each one holding one piece of the puzzle, but none of them the entire picture,” she said. “The paradox is, we are excited to adopt more and more AI tools, but we are multiplying the fragmentation. … If you look ahead in five to 10 years, advisors, instead of switching between eight to 10 platforms, would be switching between 15 to 20 platforms. And that’s not something anybody wants.”

Joseph’s firm, CogniCor, creates an intelligence layer that uses the existing technology, tools and data from different, disparate sources, but combines it to provide a holistic level of intelligent insights and actions.

“We are a copilot platform that uses data from all of these sources,” she said.

Taking an opposing position, Tishkevich said while an intelligence layer is a “great thing to have,” it all starts with the data.

“If you don’t have a single source of truth for all your data, it’s difficult for any AI agent or intelligence layer to figure out what you know,” he said.

Some firms have begun to tackle this by setting up the infrastructure to own their own data in data warehouses, data lakes and data lakehouses.

“You know your data is correct when you’re pulling it out of any different technology, because all of your surrounding tools are running off the same set of data to capture something like a name, name change, address change, that’s an objective change in one system,” said Fritz. “It can feed back into that database and it’s updated everywhere. This saves operational time. It saves the embarrassment of coming to your clients with wrong data.”