AI and Wealth Management: When to Trust, When to Verify

Key Takeaways:

• AI streamlines repetitive tasks and allows advisors to focus on high-value work such as client relationships, strategic advice, and nuanced interpretation.
• While AI can generate quick insights, it often struggles with factual precision, regulatory nuance, and up-to-date fund details. Advisors must cross-check outputs with primary sources, especially when compliance and client trust are at stake.
• AI can help advisors by speeding up research and idea generation, but the core of wealth management, risk assessment, contextual decision-making, and relationship-building depends on human expertise and discernment.

Using AI in Wealth Management: When to Trust, When to Verify

Artificial intelligence is reshaping nearly every industry, and wealth management is no exception. The key benefit of applying AI workflows is efficiency. Advisors today face an avalanche of data, complex investment vehicles, and rising client expectations for timely, personalized insights. By leveraging AI, advisors can focus on interpreting insights and advising clients instead of spending hours on mundane operational tasks like combing through information and organizing reports.

However, while AI offers tools to simplify research, accelerate reporting, and generate ideas, AI’s limitations are real, especially when it comes to accuracy on fund details, regulatory nuance, and real-time data. Knowing when to trust AI, and when to verify, is the difference between value-add and costly mistakes. This article highlights where AI adds genuine value, and where advisors should tread carefully.

The Rise of AI in Investment and Wealth Management

Artificial intelligence is no longer confined to Silicon Valley or experimental trading desks. It has quickly become a mainstream tool in the investment and wealth management industry. Across asset managers, private banks, and advisory firms, AI is being integrated into everyday research workflows in three main ways:

• Natural language models: Drafting commentary, condensing research reports, or even generating client-ready summaries from technical material.
• Data synthesis engines: Pulling together inputs from market news, macroeconomic indicators, earnings transcripts, and sell-side reports into digestible outputs.
• Screening automation: Rapidly filtering securities based on quantitative or qualitative criteria, whether it’s identifying undervalued bonds, flagging ESG controversies, or highlighting thematic exposures.

Where AI Adds Value and Where It Doesn’t

For advisors, AI tools are appealing because they can dramatically reduce time spent on repetitive, operational tasks. This frees up more time for relationship building and strategic advice.

One of the most immediate ways AI is proving useful to advisors is in drafting portfolio commentary. Instead of starting from scratch with every quarterly or semiannual client report, advisors can prompt AI to summarize the major performance drivers, economic context, and asset class trends. A simple input like “Summarize how a 60/40 portfolio would’ve performed YTD given Fed policy and equity returns” can generate a baseline narrative in seconds. This doesn’t replace personalization; it simply eliminates the boilerplate writing so advisors can focus on adding account-specific nuance and insights.

AI is also helping to simplify investment concepts that are often difficult to explain to clients. Vehicles such as options, buffered ETFs, interval funds, or separately managed accounts can sound intimidating. With the right prompt, AI can translate these strategies into plain English, giving advisors language they can confidently use to make complex investments more approachable. That kind of clarity goes a long way in building trust with clients who may be exploring alternatives for the first time.

But while the benefits are real, so are the risks. Large language models (LLMs) are designed to predict patterns in text, not to guarantee factual accuracy. That means they can confidently provide answers that look credible but are subtly wrong, or in some cases, entirely fabricated. A model might, for example, invent ETF holdings, misquote regulations, or fail to reflect the most recent Fed policy update.

That’s why the rise of AI in investment and wealth management should be seen as an augmentation, not a replacement. The technology can streamline research and enhance productivity, but it should always be paired with advisor expertise, a disciplined review process, and a healthy dose of skepticism.

High-Value Use Cases

Beyond saving time on commentary or simplifying jargon, AI can provide real, tangible value in areas that support both advisor research and client conversations.

One of the most practical applications is in product comparison and differentiation. For example, when evaluating two emerging markets ETFs or weighing the merits of an active versus passive approach in fixed income, AI can generate clear, side-by-side language that highlights differences in strategy, cost, performance history, and risk. This not only strengthens the advisor’s due diligence but also arms them with a client-ready narrative for explaining allocation shifts or product recommendations.

AI is also well-suited for asset class and sector overviews. Advisors often need to get up to speed quickly on areas outside their immediate expertise, whether that’s the outlook for energy, the complexities of biotech, or the fundamentals of private credit and REITs. By synthesizing disparate data sources into concise briefings, AI reduces research time while giving advisors the confidence to speak knowledgeably across a broader range of topics.

Finally, AI can be a powerful tool for idea generation around portfolio tilts. For example, with the right prompt, an advisor can explore the pros and cons of adding nuclear energy exposure, or weigh the tactical case for gold in a 2025 portfolio. These insights help advisors test their own investment thesis, uncover fresh angles, and keep client portfolios differentiated and aligned with current themes.

In each of these use cases, AI is less about replacing expertise and more about enhancing it. Helping advisors focus their judgment where it matters most.

Known Weak Spots and Hallucination Risks

For all its promise, AI also has blind spots that advisors need to recognize. Because large language models are trained on historical data, they often miss the most recent filings, product launches, or regulatory changes. That lag can be costly if an advisor relies on the output without verifying it against primary sources. For example, in our own experience at VanEck, our research shows that 75% of the time ChatGPT will show holdings for VanEck ETFs as either inaccurate or outdated.

Even more concerning are the instances where AI simply makes things up. Models have been known to “hallucinate” details, confidently listing ETF holdings that don’t exist or inventing product features that were never part of a strategy. In other cases, the language generated can sound overly certain, leaning on absolute statements or implied guarantees that no responsible advisor would ever use.

These are not hypothetical risks. Investors have already reported real-world examples of AI fabricating facts, misquoting regulations, or providing outdated commentary, as documented in online forums and discussions. The takeaway is clear: while AI is a powerful tool, it is not infallible. Advisors who use it effectively understand its strengths, but they also remain vigilant about its limitations.

Real-World Signals: What Investors Are Saying

The conversation around AI in investing is no longer theoretical, it’s unfolding in real time across trading desks, research teams, and advisory practices. Thought leaders in the industry tend to strike a balanced tone: enthusiasm for the efficiency gains and productivity enhancements AI offers, paired with a healthy skepticism about its reliability when the stakes are high.

While large asset managers are increasingly incorporating AI into their investment research process, so too are individual investors. Accordingly, to have more meaningful conversations with clients and prospects, it’s important for advisors to stay up to speed on developments as it relates to using AI in investment management.

Online investor communities frequently discuss both the promise and pitfalls of AI tools. Many investors praise the speed of AI-powered screening or the clarity it can bring to complex concepts, but they also share cautionary tales of hallucinated ETF facts or outdated regulatory references. These experiences reinforce a key point: skepticism is not anti-AI; it’s a form of due diligence.

Ultimately, what investors are saying reflects a pragmatic reality. AI is here to stay, and it’s already adding value. But its outputs are not gospel. The most successful advisors and asset managers are those who view AI as an assistant, not an oracle, leveraging its strengths while double-checking its work.

Trust but Verify: A Checklist for Accurate AI Information

Before using AI-generated investment insights, advisors should:

• Cross-check against original sources (filings, fact sheets, earnings releases).
• Confirm regulatory citations (SEC, FINRA, ESMA updates).
• Watch for overconfident language (absolute forecasts, “guaranteed” claims).
• Check attribution: Ask, “Where is this data from?”

In addition, advisors should also run AI-generated content through compliance review and designate internal (or third-party) experts to validate accuracy.

Also, for certain publicly available data like fund data and economic figures, it’s more appropriate to simply go straight to the source or prompt the AI tool to include a link to the source in its output and then cross-check against it.

The Role of Human Judgment in an AI-Driven Future

For all its speed and scale, AI cannot replace the core of investing and wealth management: human judgment and personal relationships. At its best, AI provides acceleration, rapidly surfacing information, highlighting patterns, and cutting through noise. But the heart of wealth management and investing have always rested on decisions that require discernment, context, and trust. Those are qualities no algorithm can replicate.

Risk management is a prime example. Models may flag statistical anomalies or market shifts, but only humans can assess how those risks intersect with client objectives, liquidity needs, or behavioral biases.

There’s also the matter of interpreting subtle signals. AI can summarize an earnings call or scan headlines across 50 countries in seconds, but tone, credibility, and context are where human advisors excel. A seasoned portfolio manager can detect when a CEO is dodging a question, or when a “beat” on earnings hides a weakening balance sheet. These qualitative insights don’t show up in the data, but they often drive the most important calls.

The future, then, is not AI replacing advisors, but AI empowering them. Imagine a model that drafts the baseline market outlook while the advisor tailors it for a client’s personal goals. Or a screening tool that identifies potential thematic opportunities, which the advisor can then test against their own conviction and risk framework. In this way, AI becomes the assistant that handles repetitive, time-consuming work, leaving advisors free to do what only humans can: apply wisdom, build trust, and deliver nuanced, high-value client engagement.

In short, AI may reshape how advisors work, but human judgment will continue to define why they do it.

Final Thoughts

AI is not a substitute for expertise, it is a tool that, when used responsibly, can help advisors save time, improve communication, and enhance research workflows. The key is balance: trust the efficiencies AI provides but always verify the details.