AINL#016 Augmented Intelligence in Investment Management Newsletter
Welcome to the 016 Edition of the Newsletter on Augmented Intelligence in Investment Management. 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 noise.
AINL#16 SYNTHESIS
What do these recent developments mean for investment decision-makers?
1. Leverage AI Strategically, but Safeguard Against Cognitive De-skilling
The accelerating deployment of AI in financial intermediation, trading, and supervision offers measurable gains in operational efficiency and predictive analytics. However, over-reliance on generative AI tools—particularly LLMs—may impair cognitive rigor and judgment calibration among investment professionals. As demonstrated by Kosmyna et al. (MIT, 2025), AI-assisted work reduces neural engagement in brain regions associated with executive function and creative reasoning, potentially leading to a deterioration in tacit knowledge accumulation. Investment firms should therefore treat AI not as a cognitive substitute but as a decision-augmentation layer, ensuring that analytical skill formation and domain expertise remain core to portfolio management capabilities (Article 2; Article 1).
2. Prioritise Explainability Over Raw Predictive Power in AI-Augmented Decision Systems
While many investment professionals employ AI to enhance information processing and forecasting accuracy, recent findings by Vasileiou et al. (2025) underscore a critical human-AI interface issue: users prefer explanation-based belief revision over purely data-driven outputs. This behavioural tendency is pivotal for financial professionals making high-stakes capital allocation decisions under uncertainty. Incorporating explainable AI systems—rather than opaque black-box models—can improve interpretability, reduce resistance to model-driven insights, and align more effectively with the cognitive heuristics of investment committees. Thus, the emphasis should shift from purely quantitative optimisation to cognitively coherent, explanation-rich AI support tools (Article 3).
3. Exploit Supervisory Innovation as a Signal for Market Readiness and Strategic Positioning
The widespread experimentation with generative AI in financial supervision, as documented by Prenio (BIS, 2025), marks a shift in regulatory technology adoption from exploratory to applied phases. This shift offers astute investors a dual benefits. First, as a signal of regulatory directionality and oversight readiness for AI-integrated financial products, and second, as a benchmark for operational scalability of their own AI deployments. Moreover, as highlighted at VivaTech 2025, Europe’s pathway to strategic autonomy hinges not on invention but on the efficient commercialisation of innovation through deep capital markets and decision-making excellence. Professional investors must interpret supervisory uptake as both a validation of AI’s institutional credibility and a catalyst for rethinking their positioning across market depth and investment governance quality (Article 4; Article 5).
TOP 5 ARTICLES
ARTICLE ONE
Report on Artificial Intelligence in Finance
ARTIFICIAL INTELLIGENCE | Foucault et al. | CEPR, IESE | 2025 | Report
Important Development
Drawing on recent academic research and empirical evidence, the report examines the fundamental transformations induced by AI and the policy challenges they raise. It is centred around three main themes: (1) the use of AI in financial intermediation, central banking and policy, and regulatory challenges; (2) the implications of data abundance and algorithmic trading for financial markets; and (3) the effects of AI on corporate finance, contracting, and governance. Across these domains, the report emphasises that while AI has the potential to improve efficiency, inclusion, and resilience, it also poses new vulnerabilities that call for adaptive regulatory responses.
Why Relevant to You?
As mentioned in the report, even compared to other technological revolutions such as electricity or the internet, the adoption of AI is proceeding at an unprecedented pace. The report offers a comprehensive and up-to-date discussion on potential opportunities and challenges associated with the use of AI in finance and financial regulation. This kind of information should be especially useful to managers and policy makers, who seek to position themselves in this rapidly evolving environment.
ARTICLE TWO
The Hidden Cost of AI Productivity
ARTIFICIAL INTELLIGENCE | Kosmyna, N, et al. (MIT) | 06 2025 | Article
Important Findings
This paper explores how the human brain engages differently when writing with or without the help of large language models (LLMs). Using EEG data, the researchers compared neural activity in participants who wrote essays either entirely on their own or with AI assistance. The paper raises important questions about the long-term implications of AI use for human cognition, creativity, and skill development.
Why Relevant to You?
The paper shows that using AI tools like LLMs reduces deep cognitive engagement during tasks like writing. While AI assistance boosts efficiency, it appears to lessen brain activity linked to creativity, memory retrieval, and executive control. This trade-off could have long-term effects on skill development and innovation, especially in knowledge-based industries. The paper’s neuroscience-based approach adds depth to discussions about AI’s impact, moving beyond productivity to examine how it changes brain function.
ARTICLE THREE
How To AI-Assist People Revising Inconsistent Beliefs?
HUMAN & ARTIFICIAL INTELLIGENCE | Stylianos Loukas Vasileiou et al. | 06 2025 | Article
Important Findings
Understanding how humans revise their beliefs in light of new information is crucial for developing AI systems which can effectively model, and thus align with, human reasoning. Empirical evidence from cognitive psychology suggests that people follow patterns different from theoretical ones when presented with conflicting information. This paper presents three comprehensive user studies showing that people consistently prefer explanation-based revisions, i.e., those which are guided by explanations, that result in changes to their belief systems that are not necessarily captured by classical belief change theory.
Why Relevant to You?
This has implications for organisations working with AI systems designed to support interactions with humans. This can go from educational contexts to guided decision taking from finance professionals. Rather than working with conversational AI agents providing information for decision taking, it is more effective to convert towards explanation-based systems to better align with human cognitive processes.
ARTICLE FOUR
Starting With The Basics: a Stocktake of GenAI Applications in Supervision
HUMAN & ARTIFICIAL INTELLIGENCE | Prenio | BIS | 2025 | Paper
Important Findings
The paper provides an overview of the state of gen AI applications in financial supervision based on a survey among 42 authorities conducted in 2025Q1. It finds that twelve of the responding authorities are already using gen AI, while twenty authorities are either experimenting with the technology or are in a development phase. The most prominent use cases concern the automation of supervisory processes, financial risk assessment, risk horizon scanning, and ESG reporting. Main challenges in adopting gen AI in supervision include IT infrastructure, data security concerns, lack of technical skills, missing access to the technology, legal risks, and a lack of clearly defined use cases.
Why Relevant to You?
Over the past years use case studies on the use of modern technologies in financial supervision have become a valuable source of inspiration for practitioners. To our knowledge, this is the first study, which specifically looks into the use of gen AI by supervisors. Moreover, since supervisory agencies are large organizations which face similar operational challenges as any other modern company, the paper might also provide useful insights for non-supervisors.
ARTICLE FIVE
VivaTech 2025: Europe’s Bold Play for Strategic Autonomy
HUMAN & ARTIFICIAL INTELLIGENCE | Markus Schuller | June 2025 | Article
Practical Experimentation
Unsurprisingly, Artificial Intelligence dominated the agenda—more precisely, Agentic AI, including both single-agent and multi-agent systems. While the theme itself offered little novelty, what stood out was the shifting tone of the event. VivaTech felt less like a gathering of nerds and more like a forum for applied innovation. The crowd was noticeably more senior, more managerial, more corporate—suggesting a maturing ecosystem, transitioning from experimental phase toward commercialization.
Why Relevant to You?
Innovation has never been Europe’s primary constraint – see Catriona Marshall , Danae Kyriakopoulou, or Werner Wutscher. The bottleneck lies in scaling innovation commercially through deep, integrated financial markets – see Uli Grabenwarter, Gerd Gigerenzer et al. European strategic autonomy will stand or fall based on two critical factors:
> Market Depth Across Corporate Development Stages
> Superior Investment Decision Quality at Scale
