How is artificial intelligence affecting business angels?

In recent years, a gradual transformation has been observed in the processes of analysis and decision-making within early-stage investment. Notwithstanding the fact that the so-called business angel has traditionally operated on the basis of experience, sector knowledge and intuition, the incorporation of artificial intelligence tools as a support element in the evaluation of opportunities is increasingly evident.

This phenomenon, still in a developmental phase and with varying degrees of adoption, cannot be understood solely as a technological innovation, but rather as a potential shift in how investment judgment is structured.

The environment in which private investors operate has, in recent years, been shaped by a correction following the exceptionally high levels of investment recorded in previous cycles. Recent reports from the OECD point to a certain stabilization of venture capital globally, albeit accompanied by greater selectivity in capital allocation and a noticeable concentration in certain technology sectors.

Within this dynamic, artificial intelligence has gained significant weight in terms of investor interest. Various estimates place a substantial share of recent investment flows in companies linked to this field, reflecting a trend towards specialization, although its sustainability in the medium or long term cannot be generally asserted.

This context directly impacts the role of the business angel, who continues to play a relevant role in early-stage financing, albeit in an environment characterized by increased analytical complexity and competitive pressure.

The adoption of artificial intelligence in business processes already shows a significant level of penetration. According to studies by McKinsey & Company, a high percentage of organizations report using these technologies in at least one function, albeit with heterogeneous levels of maturity.

In the field of startup investment, this trend translates into the emerging use of tools designed to structure the preliminary analysis of opportunities. In practical terms, investors acknowledge that these solutions allow for faster project assessment, enable the filtering of startups based on objective data and traction metrics, and facilitate the identification of trends in highly competitive markets. This, in turn, supports prioritization in environments characterized by a high volume of opportunities and increasingly accelerated launch cycles.

However, even within this context of growing technological support, one key element remains constant: the final investment decision continues to rely, to a large extent, on qualitative factors, particularly the quality of the founding team and its execution capabilities.

It should nevertheless be emphasized that the available evidence does not support the conclusion that such tools replace human judgment or consistently guarantee superior outcomes. Some academic studies suggest the existence of meaningful correlations between algorithmic models and investment decisions, but these findings must be interpreted with caution, taking into account methodological limitations and the dynamic nature of markets.

The progressive incorporation of artificial intelligence raises a fundamental question regarding the configuration of investment judgment. While data-driven analysis is not new to venture capital practice, its formalization through automated models introduces an additional layer of systematization.

From a legal perspective, this development could have implications for defining the standard of care expected of investors. However, at the current stage of market development, it is not possible to assert the existence of a general obligation to use artificial intelligence tools. Their adoption should instead be understood as an emerging practice, the relevance of which will depend on the investor’s profile and the specific circumstances of each transaction.

In this respect, the absence of such tools does not, in itself, constitute negligent conduct, just as their use does not eliminate the need for critical and contextualized analysis.

The use of artificial intelligence in investment decision-making also entails certain risks that must be taken into consideration. Among these is the possibility that models based on historical data may replicate existing market patterns, potentially fostering homogeneous decision-making or reinforcing prevailing trends.

Additionally, reports published by organizations such as Reuters have highlighted concerns regarding valuation developments in certain technology sectors, particularly those linked to artificial intelligence. While such concerns must be approached with caution, they underline the importance of avoiding excessive reliance on market trends or insufficiently validated predictive models.

Finally, it should be noted that algorithmic systems inherently incorporate, to a greater or lesser extent, the biases present in the data on which they are trained, thereby limiting their purported objectivity and requiring ongoing oversight.

In light of the foregoing, it may be concluded that artificial intelligence is being gradually incorporated into early-stage investment as a support tool for analysis, without, at present, fundamentally altering the nature of the business angel’s role.

Its use may contribute to improving efficiency in the identification and evaluation of opportunities, but it does not eliminate the inherent uncertainty of such investments nor does it replace the investor’s judgment.

From both a legal and practical standpoint, the issue is not one of substituting human judgment, but rather of integrating new tools into a decision-making process that ultimately remains personal and contextual.

Within this framework, ILIA ETL GLOBAL observes that this technological evolution is generating new needs for supporting private investors, particularly in relation to transaction structuring, risk assessment and the orderly incorporation of analytical tools into their decision-making processes.