Summary
A 2026 survey of 938 American investors found that 62% already use artificial intelligence to inform investment decisions. Most are not handing their portfolios to machines. They are using AI to research stocks, understand market news, generate ideas, analyze data, and evaluate portfolios faster. The biggest opportunity is efficiency, while accuracy, overreliance, and verification remain important concerns.
AI Has Entered the Everyday Investor’s Research Process
Artificial intelligence is becoming part of how Americans research investments, but the reality is more practical than the image of a computer autonomously trading stocks.
A March 2026 survey of 938 American adult investors conducted by Investing.com found that 62% were already using AI tools to help inform their investment decisions. That figure included 23.6% who used AI regularly, 27.4% occasionally, and 11.5% who had experimented with it once or twice. Another 21% said they had not yet used AI but were considering it.
The more interesting question is what those investors are actually doing with AI.
The answer is surprisingly straightforward: research.
Among investors who had experimented with AI, 62.4% said they used it to research stocks or other assets. About 35% used it to better understand market news, 34.4% used it to generate trading ideas, and 21.7% used it to help with portfolio decisions. Chatbots such as ChatGPT were the most commonly reported AI tools, used by 53.5% of respondents for investment-related research.
That distinction matters. The typical AI investor isn’t necessarily asking a chatbot, “What stock should I buy?” Instead, they are increasingly using AI as a research assistant—one that can summarize information, explain unfamiliar concepts, organize data, compare companies, and help formulate better questions.
So, What Are Investors Actually Using AI For?
The most useful way to understand AI investing is to look at the individual tasks it can accelerate.
Imagine a U.S. investor researching a company before an earnings report. Traditionally, that person might open the latest 10-K, 10-Q, earnings presentation, analyst commentary, financial news and several years of historical data.
AI can help turn that pile of information into a more manageable research process.
An investor might ask an AI system to identify the company’s largest revenue segments, summarize changes in operating margins, explain management’s latest guidance, compare current results with previous quarters, or list the major risks mentioned in its filings.
The AI isn’t necessarily producing new financial information. Its value often comes from organizing existing information faster.
This is one reason speed is the leading perceived advantage. In the Investing.com survey, 39.5% of respondents said AI’s biggest advantage was faster analysis of market data. Other respondents cited earlier opportunity identification, reduced emotional decision-making, and greater accessibility.
The most common uses include:
- Researching individual stocks, ETFs and other assets
- Summarizing earnings reports and financial documents
- Explaining market news in plain English
- Generating potential investment ideas
- Comparing companies or sectors
- Organizing investment research
- Reviewing portfolio allocations
- Exploring historical market data
- Creating questions for further research
- Translating technical financial language into everyday language
The important point is that these are mostly information and analysis tasks, rather than completely automated investment decisions.
1. Researching Stocks and Other Assets
Stock research is probably the clearest use case.
Suppose an investor is considering a large technology company but doesn’t understand how its artificial-intelligence spending could affect future earnings.
Instead of asking, “Should I buy this stock?”, a more useful AI prompt would be:
“Summarize the company’s latest annual report. Identify its largest revenue drivers, major cost increases, capital expenditures, AI-related investments and the three biggest risks to earnings growth.”
That question produces something much more useful than a simplistic buy-or-sell prediction.
The investor can then take each claim and verify it against the company’s SEC filings and investor-relations materials.
This workflow also works for comparing companies.
For example, an investor researching two semiconductor businesses could ask AI to create a framework comparing revenue growth, gross margins, capital intensity, customer concentration, competitive advantages and valuation.
The AI is effectively helping build a research checklist.
That can be valuable even when the investor ultimately disagrees with the conclusions.
2. Making Complicated Financial News Easier to Understand
Markets produce an enormous amount of information every day.
A Federal Reserve announcement might involve inflation expectations, labor-market conditions, financial conditions and future interest-rate policy. A company’s earnings release might contain dozens of financial metrics and several pages of management commentary.
For an individual investor, understanding the significance of the information can be harder than finding it.
This is where AI can act as a translator.
An investor might ask:
“Explain this Federal Reserve statement in plain English. What changed from the previous statement, what stayed the same, and what could it mean for stocks, bonds and the dollar?”
That is fundamentally different from asking the model to predict the S&P 500.
The first task is interpretation and organization. The second is prediction.
The former can be useful. The latter requires considerably more caution.
The SEC, FINRA and NASAA have specifically warned investors that AI-generated information can be inaccurate, incomplete, outdated or misleading. They recommend verifying information through reliable sources rather than relying exclusively on AI-generated investment information.
3. Generating Investment Ideas
The third major use is idea generation.
About 34.4% of surveyed investors said they use AI to generate trading or investment ideas.
This is where investors need to distinguish between an idea generator and an investment adviser.
For example, instead of asking:
“What stock will go up next month?”
an investor could ask:
“What industries could benefit from increased U.S. data-center construction? Give me several publicly traded companies that could be exposed to the trend and explain the investment thesis and major risks for each.”
The resulting list is a starting point for research—not a portfolio.
This approach is particularly useful when an investor already has a thesis but wants to discover companies they might have overlooked.
The same process can be used in reverse:
“I believe AI data-center construction will accelerate. What assumptions would have to be true for this thesis to fail?”
That second question may actually be more valuable.
Good investing isn’t just about finding reasons to buy. It is about actively searching for reasons not to buy.
4. Helping Investors Analyze Their Portfolios
AI is also being used for portfolio-related decisions, although this is less common than basic research.
The Investing.com survey found that 21.7% of respondents used AI to help make portfolio decisions.
Consider an investor who owns 15 stocks and several ETFs.
Instead of asking AI whether the portfolio is “good,” they can provide a structured list of holdings and ask:
“Group these investments by sector and identify where there may be significant concentration.”
That could reveal something the investor hadn’t noticed.
Someone who believes they are diversified might discover that several seemingly different investments are all heavily exposed to the same technology trend.
AI can also help create scenario questions:
- What happens if interest rates remain high?
- What happens if technology earnings slow?
- Which holdings are most sensitive to oil prices?
- Where am I concentrated geographically?
- Which positions have similar economic drivers?
This doesn’t eliminate portfolio risk. It can make hidden exposure easier to see.
5. Using AI to Reduce Emotional Decision-Making
Investing is not purely mathematical.
Fear, greed, regret and the fear of missing out can influence decisions even when an investor understands the fundamentals.
Interestingly, 14.6% of respondents in the Investing.com survey identified reduced emotional decision-making as an advantage of AI.
AI can potentially help by forcing an investor to slow down and articulate a thesis.
Imagine a stock has just fallen 20%.
Instead of immediately selling, an investor could ask:
“Give me the strongest arguments for selling this stock today, the strongest arguments for holding it, and the specific evidence that would invalidate each argument.”
That creates a structured debate.
The same process works after a stock surges.
Rather than asking AI to justify buying more, the investor could ask:
“What assumptions about revenue growth, margins and valuation are already reflected in the current price?”
The objective isn’t to make AI emotionally neutral. It’s to use a structured process that may make the investor’s own emotions easier to recognize.
What AI Still Gets Wrong About Investing
The biggest mistake would be assuming that better information automatically produces better returns.
It doesn’t.
Investing requires dealing with uncertainty, incomplete information, changing expectations and human behavior. An AI model can summarize information while still misunderstanding its importance.
There is another problem: confident language can make incorrect information sound authoritative.
Investing.com found that 53.5% of AI users said they somewhat trusted AI but verified its information elsewhere. Only 3.8% said they completely trusted AI-generated investment analysis.
That skepticism is healthy.
The SEC and FINRA have similarly cautioned that investors should not rely solely on AI-generated information when making investment decisions. AI can use inaccurate or outdated information, misunderstand events or generate information that appears plausible but is simply wrong.
AI also does not automatically understand an individual’s complete financial situation.
A chatbot may know that someone is 55, has $750,000 invested and wants to retire soon. That doesn’t mean it understands their tax situation, insurance coverage, household obligations, Social Security expectations, estate plan, risk tolerance or other financial circumstances.
Those details can materially change an appropriate strategy.
The AI Investment Bubble Problem
There is another risk: investors may become overly enthusiastic about the technology itself.
Janus Henderson’s 2026 survey of 1,000 U.S. affluent and high-net-worth investors found that nine in ten had at least some concerns about investing in AI, with 67% worried about an AI bubble over the following 12 months. At the same time, 61% expected AI to have a positive effect on returns over five years.
That combination—optimism about the technology but concern about valuations—is important.
An investor can believe artificial intelligence will transform the economy while simultaneously believing that a particular AI stock is overpriced.
Those are not contradictory positions.
The same principle applies when using AI itself. A powerful research tool doesn’t guarantee that every conclusion it produces is valuable.
A Better Way to Use AI for Investment Research
For most individual investors, the strongest approach is to make AI one part of a broader research process.
A practical five-step workflow
1. Start with the question.
Don’t begin with “What should I buy?” Begin with a specific research question.
2. Use AI to organize information.
Ask it to summarize filings, compare businesses, explain terminology or identify areas requiring additional research.
3. Verify important facts.
Check financial figures, guidance, regulatory filings and major news against primary or reputable sources.
4. Challenge the thesis.
Ask AI for the strongest bear case, potential weaknesses and assumptions that could prove wrong.
5. Make the investment decision separately.
Your portfolio should ultimately reflect your goals, time horizon, risk capacity and investment strategy—not simply the confidence of a chatbot.
The SEC is already considering how AI could improve access to complex investment disclosures. In a 2026 speech, SEC Commissioner Caroline Crenshaw discussed the possibility of AI systems helping investors interact with lengthy fund documents and answer questions about fees, holdings, risks, conflicts and benchmarks in plain English.
That points toward an important future use of AI: making financial information easier to understand rather than pretending to know the future.
Is AI Actually Making Investors Better?
The answer is not yet settled.
In the Investing.com survey, approximately 65% of AI users said AI had improved their investment performance, with 17.14% reporting significant improvement and 47.61% reporting some improvement. But this is self-reported survey data; it does not establish that AI caused those investors to outperform the market.
That distinction is crucial.
Someone may feel that AI improved their investing because it saved them hours of research. Another investor might attribute a successful trade to an AI-generated idea when the market would have moved in the same direction anyway.
Performance attribution is much harder than user satisfaction.
The more defensible conclusion is that AI appears to be changing how investors work.
It can reduce the time required to process information, help people ask better questions and make complicated financial concepts more accessible.
Whether that consistently translates into superior returns remains a much harder question.
The Human-AI Investment Partnership
The strongest evidence so far suggests that investors are not necessarily trying to replace humans with machines.
HSBC’s 2026 research involving approximately 10,000 affluent and high-net-worth investors across 10 markets found that 73% used AI for finance and investment, but only 12% said AI was the most influential factor in their last investment decision. The research also found that human expertise remained important when investors moved from exploration to actual decisions.
That makes sense.
AI is exceptionally good at helping someone process information.
Human judgment is still needed to decide which information matters, whether the assumptions are reasonable and how much risk is acceptable.
The likely long-term model is therefore not human versus AI.
It is human using AI well.
10 Questions Investors Should Ask Before Trusting AI
A useful discipline is to treat every AI-generated investment answer as a draft.
Before acting on it, ask:
- Where did this information come from?
- Is the data current?
- Can I verify the numbers?
- Is the AI confusing correlation with causation?
- What assumptions support the conclusion?
- What is the strongest argument against it?
- Could the model be missing information?
- Is this an investment fact or an opinion?
- Does the conclusion actually fit my financial situation?
- What would have to happen for this thesis to be wrong?
These questions are more valuable than asking whether an AI model is “smart.”
Frequently Asked Questions
1. What does the 62% figure actually mean?
It comes from a March 2026 Investing.com survey of 938 American adult investors. The survey found that 62% were already using AI tools to help inform their investment decisions. It does not mean that 62% of every U.S. investor uses AI, nor does it mean they allow AI to make their investment decisions.
2. What is the most common use of AI among investors?
Researching stocks and other assets is the most frequently reported use. In the survey, 62.4% of respondents said they used AI for this purpose.
3. Are investors using ChatGPT to pick stocks?
Some are. The survey found that 53.5% of investors who had experimented with AI used chatbots such as ChatGPT for investment-related research. However, most investors reported some level of verification rather than complete trust.
4. Can AI predict which stocks will go up?
AI can analyze patterns and generate scenarios, but investors should not treat its predictions as reliable forecasts. Market outcomes depend on uncertain future events, and regulators warn against relying solely on AI-generated information for investment decisions.
5. Can AI analyze an earnings report?
Yes. One practical use is asking AI to summarize revenue trends, margins, guidance, capital expenditures, risks and management commentary. Important numbers should then be checked against the company’s original filing or earnings materials.
6. Can AI help diversify a portfolio?
It can help identify concentration and organize holdings by sector, asset type or other characteristics. But determining appropriate diversification requires considering the investor’s objectives, time horizon and ability to tolerate losses.
7. Is AI better than a financial adviser?
That depends on the task. AI can be useful for education, research and organization. It does not automatically provide the personalized judgment, accountability and fiduciary responsibilities that may come with a qualified human professional.
8. What is the biggest danger of using AI for investing?
Overconfidence is one of the biggest risks. AI can produce fluent, convincing answers even when the underlying information is incomplete or incorrect. Regulators specifically advise investors to independently verify AI-generated financial information.
9. Should beginners use AI for investing?
Beginners can use AI as an educational and research assistant, particularly for explaining concepts and organizing information. It is safer to use it to understand an investment thesis than to blindly follow buy-and-sell recommendations.
10. Will investors use more AI in the future?
The survey points in that direction. Among respondents, 37.8% expected to use AI much more and 17.3% expected to use it somewhat more.
Where AI Fits Best in an Investor’s Toolkit
The most useful way to think about AI isn’t as a crystal ball.
It is closer to a research assistant that never gets tired of organizing information.
For an investor, that can mean turning a 100-page filing into a list of questions, translating an unfamiliar economic concept, comparing several companies, identifying portfolio concentrations or constructing a bear case before making a decision.
But the final responsibility remains with the investor.
The 62% statistic is therefore less about machines taking over Wall Street and more about something happening much closer to home: ordinary Americans are changing the way they investigate financial decisions.
The investors most likely to benefit may not be those who trust AI the most. They may be the ones who know when to use it, when to question it and when to verify it.
The Investor-AI Playbook
- Use AI to research, not blindly predict.
- Ask specific questions instead of generic buy-or-sell questions.
- Use AI to summarize complicated financial information.
- Ask for both bullish and bearish arguments.
- Verify important facts against primary sources.
- Treat AI-generated numbers as claims that need checking.
- Watch for concentration and hidden portfolio exposures.
- Don’t confuse confident language with accurate analysis.
- Remember that better research does not guarantee better returns.
- Keep human judgment at the center of important financial decisions.
