We are at the beginning of a fundamental transformation in wealth management. Financial decisions are increasingly being made by algorithms. Within just a few years, AI-driven applications will become the primary source of advice for retail investors, with usage expected to grow to 80 percent by 2028. This is not some distant vision of the future—it’s already happening.
The first wave of AI adoption in wealth management has focused primarily on efficiency. Asset managers already use machine learning to process massive datasets, automate routine reporting, and streamline compliance functions. While important, these applications are still relatively predictable. After all, artificial intelligence excels at dull and repetitive work. But the real revolution is now beginning, with a second phase in which AI not only works faster than humans—but also smarter. AI is becoming increasingly capable of handling complex problems.
Within five years, AI systems will refine predictive analytics to the point where market movements can be forecast with unprecedented accuracy using alternative data sources such as satellite imagery and social media. This will affect portfolio construction and asset allocation, but also financial and estate planning, family office management, due diligence, and risk management. This evolution will also transform narrative economics—from a field that retroactively analyzes how stories influence economic behavior into a discipline that can predict in real time how narratives form and drive market sentiment.
AI systems that can predict narratives can also be used to manipulate them, creating a kind of “narrative arms race” in which the line between observing and influencing stories becomes blurred. This raises the risk that only organizations with advanced AI capabilities will be able to capitalize on these insights. For example, a wealth manager might detect that increased LinkedIn activity in certain industries correlates with upcoming mergers and acquisitions—or that changes in traffic patterns around industrial areas signal production shifts that could impact stock prices. These are subtle market signals that emerge before they appear in traditional financial reports.
A revolution in trust
Private banking is two-thirds relationship management. Research shows that trust in financial advice is based on four factors: credibility, reliability, intimacy, and self-orientation. AI scores well on the first two, but still struggles with the latter two.
Younger generations, however, are more open to AI. Only 21 percent of Gen Z investors worry that AI doesn’t have their best interests at heart, compared to 44 percent of baby boomers. This suggests that acceptance of AI-driven wealth management is generational—and likely to grow over time.
Even so, the human advisor remains essential. AI may detect sentiment, but it lacks the lived experience and cultural nuance that human advisors bring. It’s likely we’ll first move toward a hybrid model in which AI handles much of the analysis, while humans provide empathy and manage complex decision-making.
Autonomous portfolios
By 2035, fully autonomous investment platforms—managing entire portfolios without human involvement—could become the norm. These systems won’t just execute trades, but will also develop investment theses and continuously adjust strategies based on macroeconomic trends, geopolitical events, and market sentiment.
This, however, introduces new challenges. If many asset managers use similar algorithms, they could unintentionally amplify market volatility. Imagine if all AI systems simultaneously decide to exit a certain sector—the result could be an artificial flash crash, entirely disconnected from economic fundamentals.
It also raises questions about accountability and transparency. Who is liable if an AI system makes a bad investment decision that results in millions in losses? And how do you explain to a client why an algorithm allocated their retirement savings to obscure cryptocurrencies?
Democratizing wealth management
One of the most promising developments is how AI is making wealth management more accessible. Personalized investment strategies and access to alternative assets were once reserved for ultra-high-net-worth individuals. AI is changing that by enabling premium wealth management services at scale.
Where a human advisor might serve fewer than 100 clients effectively, an AI system can serve thousands—delivering hyper-personalized portfolios that dynamically adapt to real-time market conditions and individual preferences. This means that even individuals with modest wealth can access investment strategies that were previously available only to the ultra-rich.
The technology also makes it possible to open up private markets, such as private equity and private debt, to a broader audience. AI can automate due diligence and assess the risks of investment opportunities that were once too time-consuming or expensive for smaller investors. This increased interest will likely lead to larger deal sizes, greater liquidity, and in turn, higher valuations.
Opportunities and threats
For wealth managers who adapt quickly, AI offers tremendous opportunity. They can serve more clients with higher-quality service at lower cost. They can spot market opportunities that competitors miss, and manage risks that were previously invisible.
For those who lag behind, the outlook is less promising. In a world where AI-powered competitors operate 24/7—without coffee breaks or emotional biases—it will become increasingly difficult to remain relevant with purely human capabilities.
The greatest opportunity lies in finding the right balance: AI for analytical power and efficiency, humans for trust, creativity, and ethical judgment. Firms that master this combination will become the leaders of tomorrow.
Han Dieperink is chief investment officer at Auréus Wealth Management. He previously served as CIO at Rabobank and Schretlen & Co.