Generative AI for Analysts

Abstract We study how generative artificial intelligence (GenAI) reshapes financial analysts’ information production. Using the 2023 integration of GenAI into FACTSET as a plausibly exogenous change in AI access, we find that FACTSET-associated reports become markedly richer—featuring 26% more distinct information sources, 24% broader topical coverage, and 21% more analytical methods—while also improving timeliness. However, these gains do not uniformly improve decision quality: relative forecast accuracy declines when analysts face greater information-processing demands. Yet, a machinelearning benchmark processing the same observable inputs shows no analogous deterioration, pointing to a human processing constraint rather than poorer underlying information. Placebo tests using other data vendors make a common platform-wide technology trend unlikely. Overall, GenAI relaxes information-acquisition constraints while making human attention a more important bottleneck.

Introduction. Financial analysts play a central role in the functioning of capital markets. As key information intermediaries, they collect, synthesize, and interpret complex and often ambiguous financial data, transforming raw disclosures and market signals into actionable insights for investors and firms. Yet, this critical role is inherently constrained by information processing costs, as analysts must filter vast quantities of data under tight deadlines and cognitive limits. The advent of generative artificial intelligence (GenAI) promises to transform this landscape. By automating data collection and processing, and even report drafting, AI can expand analysts’ access to information and enhance productivity. Indeed, major brokerages have begun deploying AI to streamline workflows, aiming to reduce repetitive tasks and augment analysts’ analytical reach.1 However, whether these tools enhance analysts’ ability to extract value-relevant insights or instead overwhelm them with excessive, unfiltered data remains an open question.

This paper investigates how the integration of GenAI transforms the information production of financial analysts. We ask: Does GenAI enhance analysts’ productivity and the informational value of their reports, or does it introduce new frictions that erode informational precision?

This question is both timely and consequential. While practitioners and policymakers celebrate AI as a breakthrough in cognitive automation, regulators have warned that AI could “introduce new biases in financial decision making” and obscure accountability in investment advice.2 Investors, too, are increasingly aware of the risks of relying on AI-generated investment advice (Christ et al., 2025; Blankespoor et al., 2026a). Despite these concerns, prior studies have primarily documented the benefits of AI, showing that it improves information processing across a wide range of market participants (Bertomeu et al., 2025; Bradshaw et al., 2026; Cheng et al., 2025; Ecker et al., 2025; Chang et al., 2026; Sheng et al., 2026). We aim to bridge the gap between this optimistic tone in the literature and the skepticism expressed by practitioners. To do this, we provide a systematic analysis of how a domain-specific GenAI tool—integrated with proprietary data and tailored to analysts’ workflows—affects the structure, quality, and market impact of sell-side research. Our findings reveal a nuanced story: while AI enriches analyst reports, these gains do not uniformly improve decision-making; rather, the net effect depends critically on the analysts’ information processing capacity and workload constraints.

Related work. Our paper contributes to three distinct strands of literature. First, we contribute to the growing literature on how GenAI affects information processing in financial markets and knowledge work more broadly (Bertomeu et al., 2025; Bradshaw et al., 2026; Blankespoor et al., 2026a; Cheng et al., 2025; Cai et al., 2025; Chang et al., 2026; Ecker et al., 2025; Sheng et al., 2026).

The prevailing evidence in this literature documents the benefits of AI adoption: improved forecast frequency, enhanced trading returns, and greater market efficiency. Our paper offers a counterpoint by showing that these benefits are not unconditional. We focus on a setting where analysts face significant attention constraints and find that, under such conditions, AI can reduce forecast accuracy rather than improve it. A second, and equally important, distinction is that prior studies largely examine generic, widely available AI tools such as ChatGPT. By contrast, we study a domain-specific AI platform integrated with proprietary financial and market data, tailored to the specific needs of sell-side analysts. This is precisely the type of proprietary AI capability that practitioners identify as lacking in their organizations but critically needed to generate differentiated insights (Christ et al., 2025). To our knowledge, we are the first to document the effects of such a domain-specific, data-integrated AI tool on the structural dimensions of analyst research production.

Second, we contribute to the literature on limited attention and information processing costs in capital markets (Sims, 2003; Hirshleifer and Teoh, 2003; Blankespoor et al., 2020; Hirshleifer et al., 2019; Driskill et al., 2020). With the popularization of AI, an open question in this literature is whether AI alleviates or exacerbates the cognitive constraints imposed by limited attention. Our findings provide a clear answer: AI does not magically resolve attention constraints. On the contrary, when analysts face high workload and information demands, AI- generated information compounds their cognitive burden, leading to lower forecast accuracy.

The same AI tool that enhances report richness and timeliness under routine conditions can become a source of distraction when attention is strained. By documenting this heterogeneity, we show that the net effect of AI on decision quality depends critically on the cognitive state of the user. Moreover, we show that this overload spills over to investors, who exhibit weaker market reactions to AI-assisted analyst reports, consistent with the idea that the cost of processing complex, mixed signals extends beyond analysts to the broader market.

Method. Our empirical setting is the December 2023 introduction of MERCURY, FACTSET’s GenAI platform, which integrates large language models (LLMs) with proprietary financial databases to support auditable natural-language queries, automated visualization and pitchbook creation, and portfolio analysis.3 Because MERCURY became available to existing FACTSET subscribers at no additional cost, its introduction created a discrete change in the AI capabilities bundled with an established data platform. We exploit this timing in a difference-in-differences design that compares changes in reports associated with FACTSET to changes in reports using alternative platforms. Importantly, we observe FACTSET citations rather than analysts’ direct clicks or prompts in MERCURY. Our estimates should therefore be interpreted as the incremental effect of exposure to FACTSET after its GenAI integration, rather than as the treatment-on-the-treated effect of verified MERCURY use. This distinction also makes our estimates conservative if some control reports use other GenAI tools.

We construct a comprehensive dataset of 46,853 report-firm pairs issued by U.S. analysts between January 2022 and October 2024. To systematically quantify how analysts acquire and process information, we employ GPT-4o-mini, a state-of-the-art language model for the extraction of structured content (Hurst et al., 2024). The model parses the full text and embedded objects (tables, figures, and appendices) of each report to identify three key dimensions of information richness: (i) the number and type of distinct information sources (textual, tabular, and visual), (ii) the breadth of topical coverage (firm-, industry-, and macro-level), and (iii) the analytical methods employed (historical analysis, valuation, and forecasting). Importantly, analysts’ explicit source citations allow us to identify whether a report relies on FACTSET at the report level. We combine this observable platform citation with the discrete timing of MER- CURY’s introduction, rather than relying on stylistic AI-detection software to infer GenAI use.4 The resulting combination of structured report measures and observable platform citations allows us to study comprehensively how access to a domain-specific, AI-integrated data platform changes analysts’ information production.

We first describe the adoption patterns and trends of the FACTSET platform within our sample period. Prior to its AI integration, FACTSET is primarily used by analysts employed at smaller brokerage firms and those with less experience, consistent with FACTSET being a budget-friendly choice relative to more expensive vendors such as BLOOMBERG. Further, analysts who graduated from elite universities and those covering larger portfolios are more likely to use FACTSET. After the AI integration, we observe a marked increase in both the prevalence and intensity of FACTSET usage, proxied respectively by the percentage of reports citing FACTSET and the share of citations to FACTSET among all platform citations in the reports.

Both these percentages rose from a pre-AI level of approximately 25% to nearly 50% following the integration. Notably, analysts from smaller brokerage firms and those working with larger teams disproportionately increased their use of FACTSET, while other analyst characteristics remained relatively stable around the integration date.

We next examine how report content changes after FACTSET’s AI integration. Our baseline specification absorbs report-date shocks, firm-year and broker-year changes, and time-invariant analyst characteristics. Relative to other reports, post-integration reports associated with FACT- SET exhibit significant increases in information sources, topical coverage, and analytical methods. The magnitudes range from 21% to 26% relative to pre-integration levels, with the largest increases in figure sources, industry topics, and forecasting methods, closely aligned with MER- CURY’s visualization, information-retrieval, and analytical capabilities.