The first time I used an AI tool seriously, I had the same reaction many people have: This is impressive. The answer to my questions arrived quickly. They were organized, confident, and often more useful than I expected. It felt less like searching the internet and more like having a conversation with something that could put a complicated subject into plain English.
Then I started asking it questions in areas where I had expertise. That is when I began to see its limitations. Sometimes it missed an assumption that mattered. Sometimes it reasoned from the wrong framework. Other times it gave an answer that looked complete but did not ask the clarifying question that would have changed the analysis. The response still sounded polished. That is what makes the limitation easy to miss.
That experience did not make me less interested in AI. It made me more interested in using it well. AI can be a useful starting point for financial questions, research, and preparation. But an answer is not the same thing as advice.
Financial planning makes this distinction especially important. A choice about retirement, investing, or taxes is rarely about one account or one number. It can affect cash flow, risk, insurance, estate goals, and family priorities. A chatbot may help explain one piece of that puzzle. It might not have enough context to see the whole puzzle unless someone is there to bring the relevant pieces together.
What is happening behind the black box?
Generative AI is not a real-world expert or an all-knowing financial oracle. At a basic level, it produces a response one piece of language at a time. It uses patterns “learned” during training along with the instructions, facts, source material, and conversation in front of it to predict what should come next.
That is why a comparison to an autocomplete function is useful, even though it is incomplete. Your phone’s keyboard or email inbox may finish a word or suggest a phrase based on what you have typed. Generative AI is far more sophisticated, but it is still shaped by the context it receives. The question, the facts supplied, the instructions, and the sources all influence the answer.
AI works with the context it is given. More importantly, it cannot reliably detect what is missing. A tax detail the client did not think mattered, a family priority that was never stated, or a realistic view of how much risk their situation can actually support can change the decision. It does not stop to determine whether the question itself is complete before answering.
The questions behind the question
Consider the question, “Can I retire now, or should I work another two years?” It sounds straightforward. AI may explain common rules of thumb or help someone prepare for a meeting. Those can be useful starting points. But the real planning work starts with the questions behind the question.
How much income does the household need once work stops? How much flexibility matters? What happens if financial markets are weak early in retirement? Are there health concerns, pension options, tax consequences, or a spouse’s needs to consider? These are not distractions from the decision. They are fundamental to it.
Legacy can be especially revealing. Two people can be of similar ages and have similar portfolios and family situations, yet reach different conclusions because one feels strongly about leaving assets behind and the other is more focused on maximizing dependable lifetime income. Neither priority is automatically right or wrong. But it changes the tradeoffs that need to be considered.
For example, a person focused on dependable lifetime income may explore an income annuity as a way to create contractual payments that can last for life, regardless of market conditions. The decision, however, is rarely about the payout rate alone. It turns on whether the tradeoffs in liquidity, flexibility, and what is left for heirs fit that person’s specific priorities and overall financial picture.
Where judgment stays in the process
AI can support research, organize information, generate thoughtful questions, and improve communication. It can make a capable professional more efficient. My colleague, Shania A. Uhteg, has written about how automation has already made investing more accessible for many people, and I agree.
But an advisor’s role is not to compete with AI for a faster answer. It is to determine whether the question itself is the right question. That requires gathering the relevant facts, noticing what has been left out, testing assumptions, explaining tradeoffs, and applying judgment before the client acts. Those skills are developed through years of formal education and, even more importantly, years of solving real financial problems with real clients, which is experience that AI does not possess.
The danger is not that AI gives no answer. The danger is that it gives a polished answer to an incomplete question. Good advice makes sure the question is complete before someone acts on the answer.
This material is provided for general educational purposes only and is not individualized investment, tax, insurance, or legal advice. Financial decisions should be evaluated in light of a person’s specific circumstances.
Basic Disclosure:
Investment Advisory Services offered through HBK Sorce Advisory LLC, d.b.a. HBKS Wealth Advisors. Not FDIC Insured – Not Bank Guaranteed – May Lose Value, Including Loss of Principal – Not Insured By Any State or Federal Agency.
