By Babak Gahvari, CFP®, AIF®, Managing Partner
Over the last year, it has become almost impossible to avoid the conversation around artificial intelligence*. In finance, narratives tend to follow a predictable path. Better data. Faster analysis. Smarter decisions. The implication being that as technology improves, so will financial outcomes. There is some truth to that. AI is already improving parts of financial planning and investment management in very real ways. Calculations that used to take hours now take seconds. Modeling is more dynamic. Access to information is broader and faster than ever. Even the client experience is evolving as tools become more responsive and personalized.
AI in Financial Planning Is Improving Efficiency, Not Judgment
There’s an assumption in all of this that’s worth pausing on. The idea that better tools automatically lead to better financial decisions. In practice, that has rarely been the case. If it were, this would all be a lot easier than it is. The technical side of financial planning has never really been the bottleneck. The math is not particularly complicated. The rules are well documented. The inputs are usually knowable. If financial outcomes were purely a function of calculation, most people would be in a much better position than they are today. Where things tend to break down is in the space between the plan and real life.
Why Behavioral Finance Still Matters More Than Technology
Most financial decisions are not made in a vacuum. They are made in the face of uncertainty, often with incomplete information, and usually at moments that do not feel calm or rational. Markets are down. Headlines are loud. A portfolio that looked fine six months ago suddenly feels like a problem, even if nothing fundamental has actually changed. This is where the numbers stop being the hard part, and where behavioral finance starts to show up.
That’s the part that is easy to underestimate. AI is very good at optimizing. That part isn’t really in question. It can process large amounts of data, identify patterns, and generate outputs that are internally consistent. If the objective is to determine the mathematically optimal savings rate or asset allocation based on a defined set of assumptions, it can do that extremely well. But real financial decisions are not always optimization problems.
A client may be able to save more but chooses not to because of lifestyle priorities. Another may have the capacity to take on more investment risk but cannot tolerate the experience of volatility when markets decline. Someone else may be technically on track and still feel behind, despite the numbers saying otherwise. And sometimes the situation is far more personal than that.
Financial Decisions Are Not Always Rational
A portfolio includes a single stock that was inherited after a parent passed away. On paper, it may make sense to diversify. Concentration risk is obvious. The recommendation is straightforward. In reality, that position is not just a line item on a statement. It represents something else entirely. Letting go of it does not feel like a financial decision. It feels like something closer to loss. There isn’t a model that solves for that. Sometimes the textbook recommendation is not the real-life recommendation. That gap is not a technology problem. It is a human one.
Can AI Predict the Stock Market? Not Exactly
A similar misunderstanding shows up when people start to think about AI in the context of investing and the stock market. There is a natural tendency to assume that better data and faster processing should lead to better predictions. That with enough computing power, markets become more knowable. But markets do not behave that way.
I’ve written before about thinking of markets in a more probabilistic framework. At any given moment, there isn’t a single predetermined outcome waiting to be revealed. There are multiple potential outcomes, each with a different likelihood, and the path that ultimately unfolds is just one of many that could have occurred. It’s not all that different from how quantum systems are sometimes described in physics. Multiple outcomes can exist as possibilities at the same time, even if we only ever experience one of them.
If you’ve ever gone down that rabbit hole, the parallel is hard to ignore. AI can help process information within that system, but it does not collapse uncertainty into certainty. It does not turn probabilities into guarantees. How a portfolio is structured still plays a major role in how those outcomes actually feel over time. Financial markets are competitive and forward-looking. Information is incorporated quickly, often before it becomes widely recognized. As more participants gain access to similar tools, any advantage those tools provide tends to get competed away. If anything, the widespread adoption of AI may compress perceived edges rather than expand them.
Where AI Actually Adds Value for Investors
Where AI is likely to have a more meaningful impact is in how financial advice is delivered, not in replacing the role of the advisor. It can make planning more efficient. It can improve scenario modeling. It can help identify tax planning opportunities or risks that might otherwise be overlooked. It can enhance how information is communicated and understood.
All of that is useful. It should make the process better. But it does not replace the part of the process that tends to matter most. The conversations that happen when a client calls after a market decline and wants to make a change. The moment someone is deciding whether to retire and is less concerned with the math and more concerned with whether they are making a mistake. The tradeoffs between spending today and saving for a future that is inherently uncertain.
What Actually Changes, and What Doesn’t?
As AI continues to develop, it will almost certainly make financial planning more precise. It will make the tools better. It will make the process more efficient. What it is less likely to do is change the nature of the decisions themselves. Because at its core, financial planning is not a model or a projection. It is a person. And people do not always behave in ways that are perfectly optimized. That’s always been true. There’s no real reason to think that changes.
Disclaimer
*Any references to artificial intelligence in this discussion reflect general industry tools and concepts and do not imply the use of predictive or proprietary technology to generate specific investment outcomes.
This article is provided for general informational and educational purposes only. It does not constitute investment, financial, or tax advice, nor is it a recommendation or solicitation to buy or sell any security. The views expressed are general in nature and may not apply to all individuals. Readers should consult with their own financial, tax, or legal advisors regarding their specific circumstances.
This document does not constitute advice or a recommendation or offer to sell or a solicitation to deal in any security or financial product. It is provided for information purposes only and on the understanding that the recipient has sufficient knowledge and experience to be able to understand and make their own evaluation of the proposals and services described herein, any risks associated therewith, and any related legal, tax, accounting, or other material considerations. To the extent that the reader has any questions regarding the applicability of any specific issue discussed above to their specific portfolio or situation, prospective investors are encouraged to contact Canter Wealth or consult with the professional advisor of their choosing.
Except where otherwise indicated, the information contained in this presentation is based on matters as they exist, as of the date of preparation of such material, and not as of the date of distribution or any future date. Recipients should not rely on this material in making any future investment decision.
