The Role of Technology in Investment Decision-Making: A Comparative Analysis of Human vs. Machine-Led Investment Strategies


This study investigates how technological advancements have impacted the investment decision-making process by comparing human-led approaches and those driven by machines. Technological advancements are transforming how people make choices during the decision-making process. There is a need to explore the ethical implications of using automated technologies and adopting various investment schemes, along with possible bias and displacement associated with human expertise. The technology considerably impacts investment decision-making and demands a structured plan to gain its rewards entirely. The proclamation that technology can improve investment decision-making is overwhelmed by algorithmic partiality. Corporations should thoroughly assess their present state of AI implementation and identify any aspects requiring modification.


Within the realm of investment decision-making, technology has grown to become an increasingly significant factor. Many financial advisors and investors have resorted to utilizing innovative automated strategies like Artificial Intelligence (AI) that possess a unique ability to upgrade their portfolio’s performance through machine learning techniques (Milana and Arvind 189-209). This study aims to investigate how these technological advancements have impacted the process by conducting an in-depth comparison between human-led approaches versus those driven by machines to determine the effectiveness and efficiency of each chosen method.


Problem Statement

The focal point is technology’s involvement in making investment decisions. Lately, there has been an abrupt transformation within the investing sphere due to emergent technologies like artificial intelligence (AI), machine learning, and big data analytics. These advancements are revolutionizing how choices are made during the process, for example, risk and portfolio management alike (Dwivedi et al. 101994). Still, a misconception about these changes prevails regarding their impact on decision-making regarding investments that need addressing more thoroughly -such as uncertainty whether AI-driven strategies beset traditional methods or which factors prove essential enough for successful implementation purposes-. In addition, ethical implications inherent in exploiting automated technologies while implementing varied investment schemes must be further explored, inclusive but not limited solely to potential biases and displacement linked to human expertise.

Arguments For

According to Hansen et al.’s source, which presents a model for the development of AI adoption in organizations, one can infer that technology substantially influences decision-making and necessitates an organized plan to reap its advantages fully. Five phases comprise this maturity model; from preliminary exploration to continuous innovation, companies must systematically advance through each phase when integrating AI effectively. As reported by Deloitte, a study shows that almost 70% of corporate managers think that artificial intelligence and machine learning will substantially influence investment decision-making in an upcoming couple of years (Fedyk et al. 938-985). This demonstrates an augmented acknowledgment among business executives concerning technology’s significance while making vital investment decisions.

In addition, research led by McKinsey and Company discovered that advanced analytics and AI implementation might lead to escalating investment returns of about 20-30% for asset managers (Kameswari et al. 245-282). This displays the promising ability of technology to produce significant advantages for businesses that integrate it into their decision-making systems. Technology can be beneficial in investment decision-making because it can alleviate partiality and enhance impartiality. People who make decisions are inclined to cognitive prejudices that may conceal their discernment, whereas emotions or predispositions do not sway technology-led making of judgments. Employing technology as a supplement for human decision-making enables establishments to limit the risk of generating bad investment choices due to biases.

Collectively, the proof points to technology contributing greatly toward successful investment decision-making. Entities that handle AI integration organizationally can gain substantial advantages (Samson et al.). A streamlined approach is key when adopting technological advancements such as AI into any organization’s framework if they aim for significant benefits.

Counter Arguments

The assertion that technology can enhance investment decision-making is plagued by algorithmic partiality. Studies have concluded algorithms may prolong and even intensify prevailing biases, resulting in unjust and prejudiced verdicts. This presents a significant problem as it could be exceedingly problematic when used for critical life determinations such as employment or loan applications where automated decisions wield substantial impact on individuals’ lives. Moreover, it has been found that the rise in the application of technology for investment decision-making can cause a reduction in transparency and accountability (Herold et al.).

Notably, one has to consider the danger of data breaches and possible violations in cybersecurity. The more technology-reliant investment firms become while storing confidential information digitally, the greater chance these occurrences have of manifesting themselves. An examination by IBM showed that a single instance of data breach fees for financial companies totaled $5.85 million last year alone. As much as technology could revolutionize investment decisions, it is necessary to acknowledge the critiques and issues that come with its use. Responsibility and ethics should guide us in utilizing this tool (Milana and Arvind 89-209). A fair balance between human judgment and technical assistance must be achieved to maximize returns while minimizing risks associated with decision-making processes.

Analysis and Evaluation

When making investment decisions, firms must thoroughly assess their present state of AI implementation and pinpoint any aspects needing refinement. Factors critical to successful integration, as identified by Hansen et al., including strategic alignment, data quality, and organizational readiness, are especially pertinent within the context of investments. It behooves these firms then that they validate both accuracy and reliability from all sources, collecting their data while maintaining requisite technological infrastructure alongside personnel prowess in support of enabling more optimal use cases via artificial intelligence means possible.

When comparing investment methods, it is essential to acknowledge that the success and speed of each approach hinge on various elements. These factors may comprise investment type, risk level, and an enterprise’s goals and aims. Although human-led techniques have some perks, like factoring in personal information or forming opinions based on subjective judgment, machine-based methodologies can analyze large amounts of data sets faster than humans can while detecting patterns not visible otherwise.

Technology has a huge influence on how investment firms make decisions. These companies must take stock of their current AI efforts and pinpoint areas that could use improvement. To help them do this effectively, they might consider using the Hansen et al. maturity model as part of a structured approach toward adopting AI tools in investment decision-making. Nonetheless, it’s important to note that whether human-led or machine-led strategies are more effective depends upon numerous factors- with hybrid approaches possibly being optimal overall. As per the investigation done by Herold and colleagues in 2022, it is apparent that organizations must establish and carry out adaptable proficiencies for the successful execution of digital procurement transformation.

The research implies that incorporating AI and digital advancements into investment firms and procurement operations can yield substantial advantages. However, a cautious approach is necessary concerning planning thoroughly while ensuring strategic alignment of objectives and organizational preparedness for such an undertaking. It’s also imperative for these entities to establish adaptable capabilities to facilitate smooth implementation and guarantee seamless integration within their existing frameworks.


To sum up, the technology significantly impacts investment decision-making and can benefit firms that adopt it. Nevertheless, challenges surrounding bias, transparency, and cybersecurity threats must be confronted responsibly to ensure the ethical utilization of the tool. Incorporating an organized approach, such as a maturity model, is necessary for businesses to evaluate AI implementation and identify areas for development. Also important is combining human judgment with technology which could maximize returns while minimizing the risk involved when making crucial investment decisions. Therefore, companies should assess their level of adoption concerning Artificial Intelligence critically using evaluative techniques available while maintaining the balance between relying solely upon digital or human inputs. Therefore this balanced mode ensures both responsible technological use alongside profitability attainment by organizations seeking success from investing wisely.

Works Cited

Dwivedi, Yogesh K., et al. “Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy.” International Journal of Information Management 57 (2021): 101994.

Fedyk, Anastassia, et al. “Is artificial intelligence improving the audit process?.” Review of Accounting Studies 27.3 (2022): 938-985.

Hansen, Hans Fredrik, Elise Lillesund, and Patrick Mikalef. “Examining AI adoption through a maturity model.”

Herold, Silke, et al. “Dynamic capabilities for digital procurement transformation: a systematic literature review.” International Journal of Physical Distribution & Logistics Management ahead-of-print (2022).

Kameswari, Jada, et al. “Identification, Assessment and Optimisation of Key Impact Variables in People Analytics Using AI.” The Adoption and Effect of Artificial Intelligence on Human Resources Management, Part A. Emerald Publishing Limited, 2023. 245-282.

Lyu, Daoming. Towards Trustworthy Decision-Making in Human-Machine Symbiosis. Diss. Auburn University, 2022.

Milana, Carlo, and Arvind Ashta. “Artificial intelligence techniques in finance and financial markets: a survey of the literature.” Strategic Change 30.3 (2021): 189-209.

Samson, Danny, Alon Ellis, and Stuart Black. Business Model Transformation: The AI & Cloud Technology Revolution. Taylor & Francis, 2022.

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