AINL#007 Augmented Intelligence in Investment Management Newsletter
Welcome to the 007 Edition of the Newsletter on Augmented Intelligence in Investment Management (AINL). Every two weeks, we deliver five unique insights tailored to empower investment decision-makers. Our insights are carefully curated by a seasoned team of market specialists. Unbiased, actionable and practical. They will help you navigate through the noise.
AINL#007 SYNTHESIS
What do these recent developments mean for investment decision-makers?
1. Ethical AI and the Future of Investment Decision-Making
Despite the growing technical competence of AI agents (Article 2 | Xu et al., 2025), their current limitations in executing complex, long-horizon tasks autonomously suggest that augmented intelligence—not full automation—remains the superior model for most financial service contexts. Both Le et al. (Article 3 |2025) and Yang et al. (Article 4 | 2025) show that visible human-AI collaboration increases trust, adoption, and client satisfaction, particularly under uncertainty. For investment firms, this implies that embedding AI into client-facing workflows should highlight human oversight rather than aim for substitution, as doing so enhances perceived reliability and accelerates ROI on AI investments.
2. Adapting to AI-Driven Economic Shifts
From a broader economic perspective, institutional adaptability plays a critical role in shaping the effectiveness of AI in investment decision-making. Acemoglu’s research highlights how seemingly minor differences in institutional and technological evolution can lead to massive economic disparities over time. This underscores the need for investors to stay adaptive, employing advanced assessment techniques to evaluate shifting market dynamics. AI’s ability to process large volumes of data, extract sentiment, and anticipate structural changes gives professional investors an edge in identifying macroeconomic trends.
3. The Rise of AI-Powered Sentiment and Impact Analysis
AI-driven sentiment analysis and impact measurement are becoming essential tools for investors looking beyond traditional financial metrics. With AI already outperforming human professionals in structured decision-making scenarios—such as psychotherapy and sentiment analysis—its potential in financial markets is immense. AI-enhanced impact modeling in ESG and philanthropic investments offers a more precise evaluation of societal change, allowing investors to better gauge risk-adjusted returns in social impact ventures. As AI-driven tools become more adept at handling rapidly changing datasets, professional investors can leverage them for more informed, adaptive investment strategies while maintaining the human oversight necessary for nuanced judgment and ethical responsibility.
TOP 5 ARTICLES
ARTICLE ONE
The Moral Psychology of Artificial Intelligence
HUMAN INTELLIGENCE <> ARTIFICIAL INTELLIGENCE <> ETHICAL USE OF AI | Annual Review of Psychology | 2024 | Paper
Important Development
The paper examines AI’s role in moral decision-making, categorizing machines as moral agents, moral patients, and moral proxies. AI acts as moral agents when making decisions that impact human lives.
While AI lacks consciousness, people may still empathize with machines, as seen in their discomfort when robots are mistreated. However, humans display a “machine penalty,” cooperating less with AI than with humans, raising challenges in integrating AI into society. Lastly, AI serves as moral proxies, automating decisions to absolve humans of responsibility or mediating ethical communication, raising concerns about responsibility and deception.
Key challenges include aligning AI with human values, addressing algorithmic biases, and ensuring transparency in AI-mediated interactions. As AI becomes more integrated into daily life, ethical considerations must evolve to guide its development and deployment responsibly. The study urges further research into AI’s moral roles and societal impact.
Why Relevant to You?
Companies must establish AI governance to ensure ethical decision-making, transparency, and bias mitigation. Fairness audits and diverse datasets are essential to prevent discrimination. To improve human-AI collaboration, businesses should promote AI literacy and train employees to work effectively with AI. Clear accountability frameworks are needed to manage risks and liability in AI-driven decisions. AI-mediated communication must be transparent, preventing deceptive interactions with customers.
ARTICLE TWO
How Is AI Reshaping Strategic Decision-Making for Entrepreneurs and Investors?
ARTIFICIAL INTELLIGENCE | Csaszar, Katkar and Kim | 11_2024 | Article
Important Findings
AI could transform strategic decision-making (SDM) much like it revolutionized financial trading, potentially shifting decision-making from humans to algorithms over the next 30 years. The pace of this shift depends on AI advancements. Even at current levels, AI is already enhancing SDM through sophisticated analyses. A key opportunity lies in translating strategy frameworks into executable algorithms. This integration could fundamentally reshape SDM by altering its core processes—search, representation, and aggregation—while improving decision quality, efficiency, and accessibility. As AI augments human strategists, it may drive an explosion of innovation and research in the field. This paper outlines the early contours of this transformation, inviting further exploration into AI’s role in strategy.
Why Relevant to You?
This paper explores the diverse applications of AI in strategic decision-making, ranging from generating a Porter’s Five Forces analysis to enhancing scenario planning for entrepreneurs and investors. One of its most novel contributions is the idea of leveraging large language models (LLMs) as a devil’s advocate—a role that strengthens decision-making by identifying overlooked risks and counterarguments. This approach not only improves the resilience of strategic choices or investment theses but also serves as a powerful tool to mitigate groupthink.
ARTICLE THREE
When ELIZA meets therapists: A Turing test for the heart and mind
HUMAN & ARTIFICIAL INTELLIGENCE | PLOS Mental Health | 02_2025 | Paper
Important Findings
This new PLOS paper shows people could not tell the difference between the written responses of ChatGPT-4o & expert therapists, and that they preferred ChatGPT’s responses. This study with 830 participants showed that responses written by ChatGPT were often indistinguishable from those by therapists and rated higher in key psychotherapy principles. Notably, GenAI responses outperformed therapists in certain measures. Participants favored AI-coached messages over human-written responses, reporting that ChatGPT thoroughly explored relationship problems and provided realistic solutions.
Why Relevant to You?
The study underscores AI’s disruptive potential in industries like finance requiring emotional intelligence and trust. The ability of AI to selectively outperform human professionals in structured decision-making presents opportunities in AI-driven finance. As such, AI-driven sentiment analysis tools can enhance investment research. Companies specializing in NLP (Natural Language Processing) and AI-driven sentiment analysis for financial markets may be included in comprehending market complexity.
ARTICLE FOUR
Institutions, Technology and Prosperity
SUSTAINABLE INVESTING | NBER | 02_2025 | Paper
Important Findings
A framework for analyzing how institutions, market structures, and technological choices affect societal resource distribution and development across different historical periods, from colonialism to modern AI, from Nobel laureate Daron Acemoglu. This just published paper reviews the main motivations and arguments of his work on comparative development, colonialism and institutional change. A must-read.
Why Relevant to You?
Professional investors need to stay on alert about the adaptivity of the opportunity sets they intend to exploit. Acemoglu reminds all of us how small differences can amplify effects on prosperity and institutional trajectories of technological disruption. It cannot be highlighted enough, how relevant it is for investors to select the right assessment techniques and decision designs when observing, processing and concluding on this adaptivity.
ARTICLE FIVE
Measuring meaningful change. AI-enhanced impact measurement in philanthropy
ARTIFICIAL INTELLIGENCE | The University of Geneva | 2024 | Handbook
Important Findings
Researchers from the Fondazione AIS have analysed the practical implications of integrating AI into the indicator selection process for robust impact modeling. In a case study in children’s palliative care, they tested two approaches. A first model created theoretical ideal indicators and used an NLP algorithm to identify matching indicators. The second used NLP to extract the most relevant indicators from existing impact measurement tools and consolidated them. The results are promising, as well in contextual contexts, as in data contexts with large and rapidly changing indicators.
Why Relevant to You?
The systematic measurement of societal impact is becoming increasingly important. The weight of indicator selection and impact modelling will continue to grow. The results highlight the potential of use case based activation of AI. This in turn, stresses the importance of seeing AI models as accelerators for augmented decision making, rather than fully automated one-size-fits-all work.
