Artificial intelligence tools are only as good as the data they rely on.
Or, as Tim Welsh, president of Nexus Strategy, put it, "Garbage in, garbage out."
That was the focus of the "Is Your Data AI-Ready?" panel on Tuesday at Financial Planning's
The panelists emphasized it isn't just the data itself that firms should be focused on, but how that data is connected and how relevant it still is.
Maintaining data connectivity and hygiene
The most effective uses of AI feature a short distance between the data and the question being asked.
If an advisor asks for a summary of what's happened with a client over the last year, the AI needs the demographic data, recent communications, portfolio information and more to be able to provide insights, said Ludlow.
"But if nothing's connected, if the data in your CRM has no connectivity to the data in your portfolio reporting system, if the data in your eMoney or financial planning platform has no connectivity to that system, you're hobbling yourself," he said. "So it's unfortunately the old, boring story of you have to have good data hygiene and good client data set up between CRMs and other platforms."
Data cleanup is often the most difficult part of the development process. Moore said that when they were developing an AI tool that advisors could ask questions of, they had to go through and remove outdated documents from the portal so the information wouldn't be used in answer. Other pieces of information weren't removed, but were organized so that AI would ignore them when looking for an answer.
"We list all of the archives with all the old webinars so people can see them, but they might have the correct answers from three years ago, but not the correct answer today," he said.
Moore said they then ensured that users could provide feedback on the questions they were asking so they could improve on the underlying documentation.
READ MORE:
"We just had our conference last week," he said. "We rolled out our AI search. We had 200 people put questions in, and we just used AI to analyze those questions that they asked, and it came back and said, 'You need to write three new articles because this is what you didn't have.'"
Data ownership is key
As AI tools have proliferated, the question of firms
Firms should not outsource data that is the source of insights and governance to anyone, Karnell said. But all data is not the same.
"You keep that," he said. "If it's infrastructure-related, then that's a whole different story. … On the infrastructure side, we have no issue whatsoever taking that data, getting it replicated daily into Snowflake, being able to process our models and sharing that data out to our enterprise partners that need that on either a semireal-time or daily basis."
The idea of firms owning their own data highlights the importance of having a single source of truth, said Tishkevich.
"You could have a lot of different information in different places," he said. "I love the questions like, 'How many clients do I have?' Does anybody really know? Because it's different depending on which system you're going to look at and how well you track that information."
