Technology also influences the investment of large fortunes.

For years, talking about algorithms and big data sounded like something for quantitative funds or techie investors. But the technological revolution has also reached the offices where large assets are managed. Firms that move hundreds of millions are incorporating advanced analytical tools to refine strategies, anticipate cycles, and detect risks. Technology is no longer just a promise for small investors ; it has also become a silent—and increasingly influential—ally of the wealthy.

In private equity, where information is limited and investment cycles are highly complex, "applying forecasting models allows for greater precision and control ," says Carlos De Andrés Pérez, director of asset management at BlueBull. His team has developed a proprietary database with more than 10,000 funds classified by strategy, type, or geography, to which they apply machine learning algorithms to anticipate how macroeconomic variables affect capital flows. This allows them to tailor each program to the client's objectives with a level of detail difficult to achieve using traditional methods.

The use of big data and predictive techniques allows for better adjustment of portfolio composition, anticipation of complex scenarios, and the justification of each decision, explains José Manuel España, director of asset management and wealth management at NTT DATA. This combination of analysis and strategy reinforces operational efficiency and improves both profitability and the perception of robustness of service.

Although other sectors have advanced rapidly in technological integration, private equity often still operates under analogical frameworks, warns Carlos De Andrés Pérez, director of asset management. In his opinion, this gap makes innovation a necessary condition, not only for competing, but also for adapting to a market that increasingly demands greater efficiency, precision, and responsiveness.

"Integrating advanced algorithms requires reliable data, understandable models, and rigorous regulatory compliance," warns José Manuel España of NTT DATA. Beyond the technological challenge, asset managers must overcome internal cultural barriers and update their infrastructures for these solutions to work effectively. Success depends on aligning innovation with financial expertise, maintaining control in an increasingly automated environment.

Although many firms have already resolved the initial technical challenges, there is still a need to integrate artificial intelligence (AI) without compromising the human relationship that defines wealth management, says Justo Hidalgo, director of AI at Adigital.

To achieve this, algorithms must be transparent, auditable, and applied within ethical frameworks that reinforce—but not replace—professional judgment and client trust.

"The incorporation of technology in private banking has followed different paths depending on the client profile," notes Carlos Contreras, a member of the Spanish Institute of Analysts. While advanced artificial intelligence is applied almost invisibly among the ultra-wealthy, middle-income segments are noticing its presence sooner through virtual assistants, digital reporting, and automated processes. The rise of robo-advisory responds to a new generation seeking agility, clarity, and reasonable costs without sacrificing expert support.

"The advancement of artificial intelligence doesn't mean the disappearance of human advice , but rather its transformation toward hybrid models," adds Carlos Contreras. The key is combining the efficiency of the algorithm with the oversight of the manager. The more transparency these tools offer—about their logic or their limits—the greater the client's trust. Therefore, the "human in the loop" approach, where human and machine complement each other, is gaining ground.

"The high-net-worth client is no longer the same," notes Andrés Dancausa, vice president of SpainCap. "Today, they demand constant access to information, transparency, and a high degree of personalization. Many belong to a new generation of young, digitally-savvy global entrepreneurs and professionals who value both technological efficiency and human touch," he notes. Demand is also growing for flexible strategies, with more liquidity windows and a diversification that is no longer a recommendation, but a strategic priority.

Spain is making progress in asset digitalization, although it still lags behind countries like Switzerland and the Netherlands, explains Carmen Orive, a manager at Accenture. "Many asset managers are still focused on consolidating their data architecture, an essential step that delays the full use of AI and big data," she points out. In other European markets, these tools are already being used "to refine recommendations, detect deviations, and optimize tax issues in real time."

"More than a geographical issue, the pace of artificial intelligence adoption depends on the profile of those leading asset managers," says Dancausa of SpainCap. "In the United States, there are plenty of technology-minded teams committed to scaling quickly with data. In Europe, a more financial approach still predominates. In Spain, there is talent and determination, although the lack of scale slows progress . The change will eventually affect the entire sector, although not at the same time," Dancausa points out.

"Predictive systems work with historical data, customer profiles, and macroeconomic variables," explains Boris Delgado, Director of Industry and ICT at AENOR. To ensure their reliability and responsible use, international standards of quality, privacy, and traceability are applied. "This technology allows for more precise portfolio planning, the design of customized financial products , and immediate recommendations, facilitating more agile decision-making aligned with the customer's objectives," he asserts.

"Generative AI can be a useful tool for explaining investment decisions or simulating hypothetical scenarios," notes Aitor Pastor, CEO of Disia. Beyond technical analysis, "its contribution lies in improving communication and clarity. Notifying clients that AI is being used will not only be mandatory; it also reinforces transparency and trust, without diminishing the advisor's role," he emphasizes.

"Confidence in artificial intelligence is also growing on the investment front," notes Jean-Paul van Oudheusden, an analyst at eToro. After a period of excessive expectations and valuation corrections, the markets are entering a more realistic phase. "Million-dollar investments continue: the United States is reinforcing its commitment with new laws and projects like Stargate , while Asia and the global technology giants continue to push. The underlying message, he says, is that artificial intelligence has ceased to be just an expectation and has become a real cornerstone of long-term investment decisions," he concludes.

"Although AI offers clear advantages in wealth management, it also carries risks if applied without judgment," warns Amadeo Alentorn, head of systematic equities at Jupiter AM. He emphasizes the importance of knowing what data each model has been trained on and avoiding bias or overfitting. A system that learns noise rather than real patterns can fail with new data and lead to erroneous decisions. Therefore, he emphasizes, "it should complement human judgment, not replace it."

Technology, data, and automation are redrawing the map of wealth management, but they don't replace the essentials: understanding clients, anticipating, and supporting them with judgment. AI allows us to go further, yes, but the difference will continue to lie in how it combines with human experience. What's at stake is not just efficiency, but a new way of building trust in an environment that's changing faster than ever.