What is AI's Impact on Investing?

When your advisor recommends a portfolio shift—moving out of one sector, leaning into another, rebalancing to capture emerging opportunities—you naturally want to understand the reasoning. Is the move based on economic forecasts? Company fundamentals? Market signals?

Today, some of those insights may come from sources that weren’t widely used even as recently as a few years ago—such as algorithms analyzing alternative data or AI models trained to detect patterns in consumer behavior or macro trends.

Though using AI for investing and wealth management isn’t a new phenomenon, the technology's capabilities have advanced significantly in recent years. Both professional financial advisors and sophisticated retail investors are beginning to explore newer AI functionalities across areas like market research, portfolio optimization, and risk management. But while there are promising opportunities, there are also important risks to be aware of.

Here's an overview of AI's impact on investment strategy in 2025.

AI in investing: Terms to know

To understand how AI is influencing modern investing, it helps to know some relevant vocabulary. Below is a quick glossary of the most common terms you may encounter:

• Algorithmic trading: The use of computer programs to automatically carry out trades based on pre-set criteria. Often used by institutional players because it can help them get better pricing and reduce their market impact.
• High-frequency trading (HFT): A subset of algorithmic trading that leverages powerful computers to make thousands of trades in very short time frames (milliseconds).
• Robo-advisors: Digital platforms that provide automated portfolio management, typically based on a client’s risk tolerance and investment goals.
• AI advisors: A newer category in which AI-driven insights are used to inform portfolio management—either through advisor-facing tools that enhance human decision-making or (less commonly) via fully automated AI-driven strategies.

The first two terms are more applicable to Wall Street and professional settings. For everyday investors, the last two are the most important to distinguish. Let's examine the difference between them:

Many early robo-advisors, which first gained traction in the mid-2010s, were built on relatively simple, rules-based algorithms. These platforms excel at low-cost, passive portfolio management—tasks like basic tax-loss harvesting or aligning client portfolios to stated risk tolerances. They offer efficiency and accessibility but not true intelligence.

The AI we're seeing dominate headlines today is fundamentally different. These advanced models can learn from huge data sets and adjust their recommendations based on information and patterns.

Among the technologies now influencing investment processes are:

1. Natural Language Processing (NLP): AI systems that can analyze text and speech to extract intel that informs investment decisions.
2. Predictive analytics: AI models designed to combine data and anticipate shifts in consumer behavior, industries, or regional markets.
3. Machine learning: Models that continuously refine their outputs based on new market feedback to improve over time.
4. Deep learning: A subset of machine learning that uses complex neural networks (algorithms modeled loosely on how the human brain processes information) to identify highly intricate relationships within large data sets.
5. Generative AI: The buzzy technology that exploded on the public radar in 2022 with the release of OpenAI's ChatGPT is starting to show up in things like advisor-facing tools—but it's still not core to the investment decision-making itself (and some experts question if it ever will be).

Pros: AI and human collaboration to streamline investment management

AI technologies can offer notable advantages for investing and wealth management—not as replacements for human advisors, but as powerful partners alongside sound strategy and judgment.

Here are some of the key ways AI is shaping the investment process today:

1. Surfacing market insights
AI models can process vast amounts of structured and unstructured data that older tools or human analysts would struggle to analyze at scale. NLP, for example, enables AI to scan corporate earnings calls, regulatory filings, news articles, and social media for sentiment and potential signals.

2. Analyzing alternative data
One of the most powerful uses of AI lies in extracting insights from nontraditional data sources. Predictive analytics models can scrutinize everything from satellite imagery of retail parking lots to credit card spending trends and online search patterns. When combined with an advisor's understanding of the market, these signals can inform views on supply chain dynamics or sector momentum.

3. Modeling complex relationships
Deep learning and machine learning techniques allow AI to detect subtle, nonlinear relationships across thousands of variables. This is especially valuable in today’s volatile markets, where traditional linear models often struggle. Still, AI-identified patterns require expert human interpretation to translate signals into actionable strategies.

4. Enhancing time-sensitive forecasting
Time series forecasting models are specifically designed to analyze sequential data over time—such as interest rates or macroeconomic indicators—and generate predictions about future trends. These tools can support both short-term trading strategies and longer-term asset allocation decisions, particularly in tumultuous market environments. Advisors play a key role in evaluating these forecasts.

5. Supporting portfolio management
AI-driven platforms are beginning to enable "mass customization"—the ability to tailor portfolio strategies to an investor’s unique tax profile, income needs, values (e.g., ESG preferences), and sector interests. Over time, this could allow for more precise alignment between portfolios and client goals. At the same time, human advisors remain essential in helping clients weigh trade-offs.

6. Streamlining client service and reporting
Generative AI can help advisors draft client reports, summarize portfolio performance, and even create explanatory materials tailored to a client’s interests and investment style. This allows advisors to devote more time to high-value conversations and personalized guidance.

7. AI-enhanced investment products
While adoption of such products remains limited today, AI might influence packaged investment solutions in the future. AI-powered ETFs, for instance, though still an emerging and somewhat niche category, use algorithms to select and rebalance holdings based on current market dynamics. As these products evolve, human oversight will remain a part of assessing suitability for individual clients.

Caution: Where advisor oversight is still non-negotiable

Even the most sophisticated AI tools have limits. The role of human advisors remains critical in navigating nuances like:

1. Opacity/“black box” risk 
Many advanced AI models, particularly deep learning systems, lack explainability. That means they can produce recommendations without a clear rationale that advisors or clients can easily interpret. Advisor judgment helps ensure recommendations are grounded, explainable, and appropriate to specific scenarios.

2. Overfitting
AI models trained heavily on historical data may struggle to adapt to new market regimes or structural shifts—such as those triggered by major policy changes or geopolitical shocks. Over-reliance on such models can lead to misplaced confidence in patterns that no longer hold true; human advisors help contextualize AI findings with real-time developments.

3. Herding behavior
If many market participants begin relying on similar AI-driven signals, this can lead to crowded trades or even contribute to flash crashes. Ironically, the very efficiency of AI can end up making the market more volatile if everyone is following the same playbook. That’s why human oversight is so important—it brings a fresh perspective and helps prevent the AI equivalent of groupthink.

4. False sense of security
Perhaps the most subtle risk is the potential for investors to place too much faith in AI’s predictive power. Disciplined strategy and human judgment remain irreplaceable in constructing resilient portfolios.

Questions investors should ask their advisors

As AI-driven tools become more prevalent in the investment world, it’s important to understand how they’re being used on your behalf.

Here are some key questions to discuss with your advisor as you evaluate the role of AI in your portfolio:

1. How are AI insights being integrated into my portfolio—as a tool or as the primary decision driver?
2. What level of transparency do you (the advisor) have into the models being used?
3. How is model performance monitored and evaluated—especially during volatile markets?
4. Are there human checks and balances on AI-driven recommendations?

Resources to stay informed

The AI landscape is changing on an almost everyday basis, and it’s worth putting in some time and effort to research how these technologies are being applied to your investments—and how the regulatory environment is keeping pace.

Here are a few reputable sources to follow for the latest insights and developments:

Regulatory and industry bodies:

• SEC’s public statements on AI in financial services
• FINRA’s guidance on the use of AI in brokerage settings

Leading academic and industry research:

• CFA Institute’s papers and articles on AI in investing
• MIT Sloan coverage of AI & finance

Industry news sources:

• Institutional Investor (premium)
• The Financial Times' AI coverage
• The AI and AI & Machine Learning in Finance article pages from Risk.net

As with any emerging technology, AI's value depends on how it's used. The best outcomes are likely derived from partnerships between thoughtful advisors and well-informed clients who know which questions to ask—and making decisions with a clear eyed perspective. At Fulton Private Bank, we’re here to help you do just that, combining deep expertise with personalized guidance to support your financial journey every step of the way.