Investing

AI investment: how to invest in AI, and how to invest with AI

09 October 2026
General Investing

AI investment can mean two things. Investing in AI means buying shares or funds of companies that build or supply artificial intelligence, such as chipmakers and cloud providers. Investing with AI means using AI tools to research, screen, or manage your own investments. This guide covers both, plus the risks.

Investing in AIInvesting with AI
What it isOwning companies or funds that benefit from AI demandUsing AI-based tools or services to make or automate investment decisions
ExamplesAI-related shares, AI-themed ETFs, broad index funds with large technology holdingsStock screeners, robo-advisors, trading bots, AI research assistants
Main risksValuation, concentration in a few companies, AI washingOver-reliance on model output, fees, scams that promise guaranteed AI returns

Artificial intelligence (AI) is rapidly transforming the way investors analyze markets, select stocks, and manage portfolios. With AI-driven investing tools becoming more accessible, both institutional and retail investors are leveraging machine learning algorithms, big data analytics, and automation to enhance their decision-making and optimise returns.

A recent study by Mercer  of 131 asset managers (February to March 2026) found that 55% have AI integrated in at least one of their strategy’s investment processes, and 27% are at pilot stage. 91% plan to increase their use of AI in the next 12 months. AI’s ability to process vast amounts of financial and alternative data in real-time allows it to detect patterns, identify opportunities, and react faster than human investors.

Unlike traditional investing models that rely on static analysis, AI-based investing platforms use adaptive learning, continuously refining their strategies based on evolving market conditions. While AI doesn’t eliminate investment risks, it serves as a powerful tool for improving efficiency, removing emotional bias, and uncovering insights that may be overlooked by human analysts.

This guide starts with how to invest in AI, then looks at how AI is used in investment management and how you can use AI tools yourself. We explore the real-world applications of AI in investing, from stock selection and trade execution to portfolio management and risk assessment. Whether you’re a passive investor seeking automated wealth management or an active trader looking for AI-enhanced strategies, understanding how AI is shaping the future of investing is crucial to staying ahead in today’s data-driven markets. 🚀

How to invest in AI: stocks, ETFs, and themes

Investing in AI means owning part of the companies that build, supply, or use artificial intelligence. In Singapore, you can do this through a brokerage account or an investing platform. There are three common routes.

  • Individual shares: you pick the companies yourself. This gives you full control, but your results depend on a handful of businesses.
  • AI-themed ETFs: these funds hold a basket of companies linked to AI, such as semiconductors, software, and data centres. Holdings, fees, and index rules differ widely, so read each fund’s factsheet before you buy.
  • Broad index funds: global equity ETFs already hold many large technology companies, so you may have AI exposure without a dedicated fund. See our guides on what an ETF is and the types of ETFs.

Before you choose, check the fees, currency conversion costs, and how much of your portfolio would end up in a single theme.

Where does the value sit in the AI stack?

Asset managers often describe AI as a stack of layers. Each layer carries different risks, and the layers tend to move differently over time.

LayerWhat companies in this layer doWhat to watch
Hardware and chipsDesign and make the semiconductors that supply the computing power AI needsCyclical demand and heavy reliance on a few customers
Data centres and cloudProvide computing and storage at scaleLarge capital spending that may take years to pay off
Models and researchBuild large language models and other AI systemsFast-changing competition, and not all of these companies are listed
Applications and softwareAdd AI to products used by businesses and consumersWhether customers pay more for AI features
Power and infrastructureSupply electricity and equipment to data centresRegulation and supply limits

 Layers adapted from State Street Global Advisors, How to invest in AI. Descriptions are general and not a recommendation.

What are the risks of investing in AI?

  • Valuation: AI-related shares are often priced on expectations of future growth. If that growth disappoints, prices can fall sharply.
  • Concentration: a small number of very large technology companies carry a big weight in many stock indexes. A fall in those companies can affect a broad portfolio, not just an AI fund. Narrow, single-theme funds tend to be more volatile.
  • AI washing: some firms overstate how much AI they use. In March 2024, the US Securities and Exchange Commission charged two investment advisers over misleading statements about their AI use, with penalties of US$225,000 and US$175,000. Check what a company or fund actually does with AI, not just what it says.
  • Obsolescence: technology leaders can lose ground quickly when a new approach emerges.

Spreading your money across regions, sectors, and asset classes can reduce these risks. Read more in our guide to managing risk in your investments.

Investing with AI: how do investors use it?

The rest of this guide covers investing with AI: how professional managers use it, which tools are available to you, and how to avoid scams.

How is AI used in investment management?

Artificial intelligence (AI) is transforming investment strategies by enhancing research, improving decision-making, and optimising portfolio management. While AI applications vary in complexity—from basic quantitative screening to advanced deep learning models—their primary function is to augment human expertise, not replace it.

Investment managers are leveraging AI to analyze alternative datasets, refine stock selection, and automate risk assessment. While some firms integrate AI as a supportive tool for research and analysis, others use it to suggest investment decisions, which human teams then evaluate. A smaller subset of firms deploys fully automated AI-driven models, but these still require human oversight to ensure accuracy and alignment with investment objectives.

Source: Mercer

AI is transforming investing, but adoption varies across different applications. Traditional techniques like quantitative screening and factor models are widely used, while generative AI and causality analysis are still gaining traction, with many firms planning future implementation. 

This trend shows that while AI-driven investing is evolving, investment managers are taking a measured approach—prioritising proven AI strategies before fully integrating advanced technologies. 

As AI capabilities improve, more firms are expected to incorporate machine learning, NLP, and generative AI to enhance investment research, risk management, and portfolio optimization.

Key AI applications in investment strategies

AI ApplicationRole in Investment StrategiesImpact on Investment Decisions
Generative AIProcesses vast amounts of financial and alternative data to create new investment insights.Enhances research efficiency by uncovering previously unseen correlations and investment opportunities.
Large language models (LLMs) & natural language processing (NLP)Extracts and interprets valuable insights from financial reports, earnings calls, news articles, and social media.Helps investors gauge market sentiment, understand qualitative data, and refine investment strategies.
Machine learning modelsAnalyzes historical and real-time market data to predict asset price movements and detect patterns.Improves forecasting accuracy and enhances systematic investment strategies.
Causality analysisDetermines cause-and-effect relationships between market variables rather than just correlation.Strengthens investment decision-making by focusing on fundamental drivers instead of misleading correlations.
Non-linear factor modelsUses complex relationships between multiple market factors to assess investment risk and return.Enables more adaptive and dynamic risk management strategies.
Linear factor modelsEvaluates relationships between financial variables using traditional econometric techniques.Provides a structured approach to asset pricing and portfolio construction.
Quantitative screeningApplies predefined investment criteria to filter stocks and bonds for portfolio inclusion.Enhances asset selection by identifying high-potential investments using real-time and historical data.

Invest with AI

StashAway’s General Investing offers AI-powered, globally diversified portfolios designed to optimise long-term returns while managing risk. Powered by our Economic Regime-based Asset Allocation (ERAA®) framework, portfolios adapt to changing economic based on macroeconomic conditions, balancing performance and protection. With personalised risk management, transparent fees, and no lock-ins, investing confidently for your future has never been easier.

How do companies integrate AI applications in investment processes?

Source: Mercer

AI is transforming how investment firms analyze data, predict market trends, and manage risk. Instead of just relying on traditional financial reports, firms are now using AI to process alternative datasets like sustainability metrics, social sentiment, and satellite imagery, giving them a competitive edge in spotting trends early. 

AI is also refining predictive indicators and market analysis, helping firms move beyond historical data and anticipate shifts before they happen.

Bias reduction is another key area. AI is being used to identify and correct imbalances in research and risk models, ensuring investment strategies are based on accurate, diverse data rather than skewed historical patterns. 

While AI plays a major role in monitoring portfolios and analysing scenarios, most firms still keep trade execution and asset allocation under human control, using AI as a decision-support tool rather than a replacement.

Looking ahead, investment firms are prioritising real-time monitoring, stress testing portfolios, and refining predictive models to make smarter, faster decisions. AI is not just automating processes—it’s reshaping how investment strategies are built, making them more adaptive, data-driven, and forward-looking.

How to leverage AI for smarter investing

AI isn’t just for hedge funds and institutional investors—everyday investors can also use AI to pick stocks, automate portfolios, manage trades, and optimise asset allocation. With AI-driven tools becoming more accessible, retail investors can now analyze vast amounts of data, remove emotional bias from decisions, and streamline portfolio management like never before.

Stock picking with AI

Investors face an overwhelming amount of data when evaluating stocks. AI-powered stock screeners simplify this process by scanning financial reports, technical indicators, and sentiment analysis to identify stocks that meet your investment criteria.

💡 How AI helps:

✔ Filters stocks based on fundamental and technical data (e.g., earnings reports, trading volume, market capitalization).

✔ Uses sentiment analysis to scan news and social media for bullish or bearish signals.

✔ Employs predictive analytics to assess potential future price movements.

AI-powered automated portfolios

Robo-advisors like StashAway use AI techniques to build and manage investment portfolios based on your financial goals and risk tolerance. These platforms automate asset allocation and portfolio rebalancing, making investing effortless and cost-effective.

💡 How AI helps:

✔ Creates a diversified portfolio based on your investment goals.

✔ Automatically rebalance your portfolio if asset allocations drift.

💡 Where to start:

StashAway’s General Investing offers AI-powered, globally diversified portfolios designed to optimise long-term returns while keeping risk in check.

StashAway’s proprietary Economic Regime-based Asset Allocation (ERAA®) leverages AI for data analysis to manage and adjust your portfolio based on macroeconomic conditions. Augmented by human oversight to continually create, test, and monitor the algorithms, your investments are optimised based on market realities—not short-term fluctuations.

Why StashAway stands out

✔ Personalised risk management – The StashAway Risk Index (SRI) ensures your investments won’t exceed your risk tolerance, giving you peace of mind in any market condition.

✔ Smart portfolio optimization – Instead of cherry-picking stocks, ERAA® monitors economic trends to allocate assets intelligently, balancing protection and performance.

✔ Low, transparent fees – StashAway charges 0.2% to 0.8% annually, with no lock-in periods, withdrawal fees, or hidden charges. New investors enjoy a 3-month fee waiver on up to $25,000 SGD.

Managing trades with AI

High-speed computing and algorithmic trading have transformed how investors execute trades, manage risk, and optimise buy/sell decisions. AI-powered trading tools help eliminate emotional bias, automate trade execution, and react instantly to market conditions.

💡 How AI helps:

✔ Executes trades automatically based on pre-set conditions.

✔ Manages exit strategies with stop-loss and take-profit levels.

✔ Reduces emotional decision-making in volatile markets.

Optimising your portfolio with AI

AI can help you construct a personalised, risk-adjusted portfolio using modern portfolio theory. These models analyze asset correlations, market trends, and investment goals to find the most efficient mix of assets for higher returns at a given level of risk.

💡 How AI helps:

✔ Identifies the ideal asset allocation for risk-adjusted returns.

✔ Uses machine learning to refine investment strategies based on historical performance.

✔ Monitors and adjusts your portfolio to keep it optimised over time.

What are the best AI investment tools?

There is no single best tool. The right one depends on whether you want automated portfolios, stock research, technical analysis, or crypto bots. Prices are in USD unless stated, change often, and are listed for information only. StashAway does not endorse third-party tools. 

AI ToolTypeBest ForKey FeaturesCost
StashAwayRobo-advisorAI-powered wealth management and portfolio optimization Automated, globally diversified portfoliosUses ERAA® (Economic Regime-based Asset Allocation) to dynamically adjust asset allocations based on macroeconomic conditions, keeping risk constant0.2% - 0.8% of your asset under management
Zen InvestorStock researchAI-powered stock research with human expertiseUses machine learning to evaluate 115 stock factors, integrates fundamental and technical data, and ranks stocks with AI-backed Zen Ratings$99/year Basic US$15 a month; Premium US$60 a month
TrendSpiderTechnical analysisAutomated technical analysis and trend detectionAI-driven trend lines, multi-timeframe analysis, backtesting, custom alerts, and AI-powered trading botsStarts at $52.38/month From US$52.38 a month (Standard plan, monthly billing, discounted from US$82)
Trade IdeasActive tradingAI-generated trade signals for active tradersUses Holly AI assistant to conduct real-time market scanning, pre-market stock analysis, and risk managementStarts at $127/month Premium (includes Holly AI) US$189 a month or US$2,268 a year; Standard (no Holly AI) US$89 a month
Seeking Alpha PremiumStock researchAI-driven analyst reports and market insightsProvides AI-powered Virtual Analyst Reports, stock ratings, and financial metric analysis to assist with investment decisionsFree plan available, paid plan starts at $299/year
MagnifiPortfolio analysisAI portfolio analysis and brokerage integrationConnects to brokerage accounts, offers AI-driven portfolio analysis, fee reduction suggestions, and trade insightsStarts at $11 per month
AinvestStock researchAI-powered stock screeners and researchUses AI chatbots for market insights, stock screening, backtesting, and real-time news scanningMonthly $16.67 or yearly $199.99
LevelFieldsMarket eventsAI-driven market event analysisScans financial filings, leadership changes, regulatory actions, and other market-moving events; offers real-time alerts and scenario-based stock analysisStarts from $25 per month with annual plan
CryptohopperCrypto botAI-driven crypto trading botAllows automated crypto trading, strategy ranking, backtesting, and trade execution based on optimised AI modelsStarts from $24.16 to $107.5 per month with annual plan
TuringTraderPortfolio automationAI-powered long-term portfolio automationUses machine learning for asset rotation, real-time monitoring, and alerts for rebalancingFreemium plan available, paid starts from $41.5 - $61.5
WunderTradingCrypto botAI-driven crypto trading with risk managementUses advanced statistical models, arbitrage strategies, and automated execution to optimise crypto tradesFrom as low as $18.19 to $83.99 a month for yearly subscription

How do you start investing with AI?

While institutional investors leverage cutting-edge AI models, real-time alternative data, and high-frequency trading systems, retail investors now have access to a growing suite of AI-powered investment tools. 

These tools can help with stock screening, portfolio optimization, trade execution, and market analysis—but they work best as decision-support systems rather than fully automated solutions.

1. Define your investment goals

Before diving into AI-powered platforms, clarify your financial objectives, risk tolerance, and time horizon. Different AI tools cater to different strategies—whether you’re looking for short-term trading signals, long-term wealth accumulation, or automated portfolio management.

  • For long-term investors: AI-driven robo-advisors like StashAway can dynamically adjust portfolios based on macroeconomic conditions.
  • For active traders: AI stock screeners like TrendSpider and Trade Ideas can identify patterns, price movements, and trading opportunities in real-time.

2. Choose AI tools that fit your strategy

AI investing tools can be categorised into stock-picking platforms, automated trading bots, sentiment analysis tools, and AI-powered portfolio managers.

  • If you want to pick high-potential stocks → Use Zen Investor, FINQ, or Seeking Alpha Premium to get AI-enhanced fundamental and technical analysis.
  • If you prefer passive investing → Use StashAway or TuringTrader for automated, AI-managed portfolios that adjust based on economic conditions.
  • If you’re an active trader → Use TrendSpider or Trade Ideas to scan the market, identify trade setups, and automate execution.
  • If you want to analyze market trends → Use LevelFields to monitor regulatory filings, leadership changes, and financial news that could impact stock prices.

3. Combine AI insights with human judgment

Even the most sophisticated AI models have limitations. While AI can filter through vast amounts of data, detect patterns, and automate processes, it still lacks contextual understanding, adaptability to unforeseen events, and the ability to incorporate qualitative factors like changing consumer behavior or geopolitical risks.

  • Example: AI stock screeners might flag a stock based on strong earnings and bullish technical indicators, but human investors can factor in industry trends, competitive positioning, or regulatory changes that the model may not fully capture.
  • Example: An AI robo-advisor like StashAway optimises portfolios based on economic regimes, but investors still need to ensure it aligns with their personal risk appetite and long-term goals.

4. Automate routine tasks but stay in control

AI is best used for enhancing efficiency, not replacing decision-making. Consider automating parts of your strategy—such as setting AI-driven trade alerts, risk management triggers, or portfolio rebalancing—but remain actively engaged to validate AI-generated recommendations.

  • Use AI for: Identifying stocks, spotting trends, scanning news sentiment, optimising asset allocation.
  • Manually review: Major investment decisions, long-term strategic shifts, macroeconomic risks AI might overlook.

5. Keep learning and refining your approach

AI technology is evolving rapidly. The tools available to retail investors today—like large language models analysing earnings call transcripts or AI stock screeners filtering opportunities—were once exclusive to hedge funds and institutional traders. As AI capabilities improve and become more accessible, staying informed will help you make the most of these advancements.

How Can Investors Avoid AI Investment Fraud?

The rise of AI in finance has led to new forms of fraudulent schemes. Scammers often promote proprietary AI trading systems that claim to guarantee returns, using complex terminology to create an illusion of sophistication. 

Some unregistered investment platforms falsely advertise risk-free AI strategies, preying on investors who may not fully understand how these models work. Fraudsters have even begun using AI-generated content, including deepfake videos and automated chatbots, to impersonate financial experts and create fake investment platforms.

To avoid these scams, investors should verify platform legitimacy through regulatory bodies such as the SEC or FINRA, be wary of claims that sound too good to be true, and seek transparency regarding how an AI model makes its investment decisions.

In Singapore, check whether a firm or its representative appears on the MAS Financial Institutions Directory and the Financial Institution Representatives Register at eservices.mas.gov.sg before you invest. Be wary of anyone who guarantees returns, pressures you to act quickly, or offers referral commissions. If you think you have been scammed, call the ScamShield helpline on 1799 and file a police report.

What Kind of Financial Data Does AI Analyze?

AI models process an extensive range of financial and alternative data to assist in investment decisions. Fundamental data, such as earnings reports, cash flow, and valuation metrics, is analyzed alongside technical indicators like stock price trends and trading volume. AI also incorporates alternative data sources, including news sentiment, earnings call transcripts, satellite imagery, and consumer spending patterns.

By synthesising multiple data points, AI can identify correlations and trends faster than human analysts, but the accuracy of these insights depends on data quality and market conditions. AI’s predictive capabilities remain limited, especially during unpredictable market events such as financial crises or geopolitical disruptions.

Is AI Investing Suitable for Beginners?

AI-powered investing is accessible to beginners, particularly through robo-advisors and AI-enhanced stock screeners. Robo-advisors simplify investment management by automatically building and maintaining diversified portfolios based on an investor’s risk tolerance and financial goals. 

AI stock screeners help individual investors filter through thousands of stocks based on customised criteria, making it easier to identify potential opportunities.

While AI tools can streamline the investment process, beginners should not blindly follow AI-generated recommendations. Understanding the logic behind AI-driven suggestions and maintaining a balanced portfolio remains essential, as even the most advanced AI cannot eliminate all market risks.

Is Investing With AI Safe?

AI investing is generally safe when used responsibly, but it is not without risks. AI can improve risk management by analysing vast amounts of data and identifying potential investment opportunities. It can also help reduce emotional decision-making, which often leads to poor trading choices.

However, AI models are only as effective as the data they are trained on and may not always perform well under extreme market conditions. Overreliance on AI-generated investment strategies can lead to herd behavior, where many investors make similar moves based on AI recommendations, potentially amplifying market volatility.

Investors should view AI as a complement to traditional investing methods rather than a replacement for thorough research and strategic decision-making.

Can AI Predict the Stock Market?

AI has shown some success in identifying short-term price trends and optimising trading strategies, but its ability to predict the stock market with certainty remains limited. Machine learning models can analyze historical data to identify patterns that may indicate future price movements, but they struggle with unexpected events such as economic downturns, geopolitical crises, or sudden regulatory changes.

Some AI-driven trading models have outperformed traditional methods in controlled environments, but their real-world performance is inconsistent. While AI can enhance investment decision-making by providing data-driven insights, investors should not expect AI to guarantee returns or replace human judgment in long-term financial planning.

AI in Investing: A Game-Changer, But Not a Silver Bullet

AI is no longer a futuristic concept in investing—it’s already shaping the way portfolios are built, trades are executed, and market trends are analyzed. Even if you’re not actively using AI, many portfolio managers, hedge funds, and robo-advisors are leveraging its capabilities to optimise investment strategies.

For hands-on investors, AI-powered platforms can help analyze market trends, automate trading decisions, and manage risk more efficiently. Retail investors now have access to AI tools that were once exclusive to institutional players, making sophisticated investment strategies more accessible.

However, AI is not a magic formula for success. It should be used as a decision-enhancing tool rather than a replacement for sound investment judgment. While AI can process vast amounts of data and uncover insights that humans might miss, it cannot fully account for market sentiment, black swan events, or the nuances of economic shifts.

The key to successfully integrating AI into investing lies in balancing automation with human oversight. Investors who combine AI-driven insights with strategic thinking, risk management, and continuous learning will be best positioned to navigate the evolving landscape of AI-driven investing.

Want to compare automated options? Read our guide on how to choose a robo-advisor in Singapore and our guide to choosing a StashAway Risk Index.

FAQs about AI investment

How do I invest in AI in Singapore?

You can buy shares of AI-related companies, AI-themed ETFs, or broad index funds through a brokerage account or an investing platform. Compare fees, currency conversion costs, and how much of your portfolio ends up in one theme. Broad funds spread risk better than a single-theme bet.

Is there an AI ETF?

Yes. Thematic ETFs hold companies linked to AI, such as semiconductor, software, and data centre businesses. Holdings, fees, and index rules differ widely, so read the factsheet before buying. Broad global equity ETFs also hold many large technology companies, which gives indirect AI exposure.

What is the difference between investing in AI and investing with AI?

Investing in AI means owning companies or funds that build or supply artificial intelligence. Investing with AI means using AI tools, such as stock screeners, robo-advisors, or trading bots, to research or manage investments. You can do either, both, or neither, and each has different risks.

Can AI predict the stock market?

Not reliably. AI can spot patterns in past data and speed up research, but models struggle with surprises such as financial crises, geopolitical shocks, or sudden regulatory changes. Real-world results are inconsistent, so treat AI output as one input to your decision, not a guarantee.

Is investing with AI safe?

It can be reasonable when you use AI as a decision-support tool and understand its limits. Models depend on the data they learn from and may perform poorly in extreme markets. Avoid any platform that guarantees returns, and check that a firm is licensed in Singapore.

Is AI a bubble?

Nobody can know in advance. AI-related shares are often priced on expectations of future growth, which can disappoint, and a small number of large companies carry a big weight in major stock indexes. Spreading your investments across regions, sectors, and asset classes can reduce the impact if AI shares fall.

What is AI washing?

AI washing is when a company or fund overstates its use of artificial intelligence to attract investors. In March 2024, the US SEC charged two investment advisers over misleading AI claims. Check what a product actually does with AI, how it is regulated, and whether claims can be backed up.


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