Time To Separate The Work Savers From The Work Creators In AI
By Justin Whitehead
New wealthtech companies built around AI seem to pop up every day, with promises to make advisors and investment managers work more efficiently.
But all that efficiency threatens to make them busier, not easier, and that’s why it’s time for the marketplace to view AI innovations through a different lens, one that separates the solutions that tell us what to do from the ones that do the work for us. More simply, we need to distinguish the AI that does the work from the AI that makes the work.
The latter category is what seems to get the most attention nowadays, and it is causing the most consternation among advisors. How may AI note-takers support financial services? Whatever your answer, be sure to add a couple more since there were a few that no doubt nailed their seed round in the time it took you to read that sentence. At FutureProof Festival’s tech demo last September, it was a veritable parade of one agentic AI solution for advisor practice management after another, with all the pitches sounding indistinguishable.
Indeed, many of these are helpful. Several solutions on the market do collect enough data and information to fill advisors’ calendars and give them plenty of opportunities to support their clients by filling in gaps they otherwise may not have seen.
This has led to advisors dropping the ball less in managing their practices and clients. But it has also caused them to juggle more balls in the air, and that juggling can be exhausting. As Michael Kitces pointed out during XYPN Live in Austin recently, these so-called “efficient” solutions aren’t creating capacity to bring in new clients or free up time for advisors. They’re sucking up what little time and capacity advisors have with the clients they already support. In short, all this efficiency threatens to kill their practices.
It’s no wonder that many advisors and investment managers are starting to tune out a lot of these AI pitches.
That, however, is counterproductive. On the other side of the AI equation are the solutions that do the work for you. AI sits behind many applications, driving better data, synthesizing information more quickly, and automating processes. These work savers operate within existing systems to perform tasks that not only create better to-do lists but perform the necessary follow up.
As a metaphor, think of the dynamic now between Google, the king of search, and ChatGPT. You can ask each the same question, but the results will differ greatly. Under Google’s traditional model, it offers a string of links, having you wade through them to make selections after it makes its own judgments as to what should be ranked. ChatGPT will give you a straight (-ish) answer, with its reasoning. But then it will solicit more information to improve the results. It will continue to work for you to improve what you need. And do you want it to write a thesis for you? Sure! Give it a few seconds. Want some pretty graphics? Done.
Competitively, such innovation would put competitors out of business. But Google is bowing to the cliche that if you can’t beat ‘em, join ‘em — or at least incorporate some of what makes the competitor better. Search for something on Google or Bing and you get an AI synopsis. Microsoft’s Bing employs its Copilot AI tool, making sure its own products are front and center, no matter where your tastes for information sit.
When we launched a company, which allows investment managers and advisors to quickly synthesize news and data in a way that could more readily make better decisions, we did so because we wanted to take work away from our users, not add to the load. The companies that are using AI in this way often aren’t visible to the end user, but that’s OK. After all, in the near future, the same workflows people use when using ChatGPT are likely to be the model for the way advisor-tech platforms are operated. Instead of the Frankenstein-level complexity of multiple advisor tools in a tech stack, a ChatGPT-like interface managing all these solutions on the back end will likely be the norm. Not in a decade but within fewer years than you think.
That’s precisely why, as we map and rank all the AI solutions, we should not view the category as a monolith. Too many solutions exist that create work and starve capacity. But, as technology evolves, the true winners will be the one who do the work for advisors, and many won’t be seen. It only matters that their impact is felt.
Justin Whitehead is founder and CEO of Pebble Finance, a portfolio analytics and technology company that synthesizes news and proprietary research, connecting the dots between real-world events and investment performance.
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This article originally appeared in Financial Advisor Magazine.
Image courtesy of Storyset.
