How Long-Only Equity Investing Can Benefit from Agentic AI Research Platforms
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Wrapping Up Brightwave's Investment Research Spotlight Series
In the first edition and second edition of this newsletter, we did deep dives into the use cases for AI platforms across private equity and private credit investing. In the third series of this spotlight feature, we are pivoting to public equities with a focus on long-only, fundamental investing strategies. While public investors naturally have a wider scope of coverage than their private counterparts, AI platforms can add value by helping organize, summarize and synthesize the relevant information so that analysts can focus on the more important work of thesis generation and validation.
Institutional investors in public equities are entering a new era where agentic AI (autonomous, goal-driven systems built on large language models) promises to transform how fundamental research and portfolio management are conducted. Headlines herald this trend: Gartner ranks agentic AI among 2025’s top strategic technologies, and McKinsey has referred to agentic systems as the “next frontier” of generative AI. Unlike earlier AI tools, which mainly provided insights on request, these semi-autonomous analyst systems can take action by using chain-of-thought reasoning with respect to user requests. They can perceive context, plan multi-step tasks, and execute decisions with minimal human input. Embedded within the right platform with the right domain expertise, agentic AI promises to vastly accelerate research workflows.
Understanding Agentic AI: Beyond Traditional Chatbots and Analytics
The leap from simple chatbots to agentic AI is fundamentally changing what AI can do for investors. Traditional analytics with chatbots or search tools could answer questions, but generative AI agents are more able to fluidly move from “thought” to “action,” orchestrating complex workflows across digital tasks. In theory, this means an AI agent could learn to emulate much of an equity analyst’s process: gathering data across the coverage universe, analyzing it for changes or certain catalysts, and producing synthesis outputs without needing step-by-step prompts for each subtask.
What is exciting about this new frontier of AI agents is that their potential isn’t limited to regurgitating information – they exhibit generative reasoning capabilities. Domain-specific agentic AI in finance, for example, could mirror the way an analyst forms an investment thesis by connecting dots across financial statements, broker research, market news, and alternative data sources. By replicating the structure of human reasoning, AI agents act more like autonomous team members: they can form and refine hypotheses about a company, suggest conclusions (“Is this quarter’s margin compression a one-time blip or a sign of weakening pricing power?”), and even recommend next steps (like requesting more data or suggesting a deeper dive into a downside scenario).
Transforming Equity Research: 3 High-Impact Use Cases for AI Agents
Long-only investing has gotten tougher in recent years as the fight for alpha generation intensifies. Developing a compelling, long-term fundamental research thesis requires having a strong handle on not only the company itself (including both historical and future movements), but also larger industry and broader macroeconomic forces. Because positions are usually held in the context of a longer-term strategy, long-only investors need to be able to balance short-term catalysts and quarterly swings with their conviction in longer horizon returns. This is a matter of human analyst judgment and skill, and a place where AI can affect long-only investing workflows. By automating and accelerating the manual work of information organization, it frees up analysts to focus on the needle-moving strategy decisions.
Below, we explore a few tangible, high-impact use cases in the long-only equity research process:
Streamlining Earnings Season Management
For analysts with large universe coverages (50+ or 100+ names), AI agents excel at managing quarterly earnings data floods. If an analyst knows that there are 5 key factors they always want to evaluate for an industry post-earnings calls, they can rely on AI to do that quickly. LLMs can summarize earnings calls within seconds, highlighting critical details like revenue beats, misses, and management outlook. For a seasoned analyst, running through these summaries after the fact to spot outliers and identify which ones warrant further investigation is a great example of efficiently using human skill and experience.
Accelerating New Coverage Initiation
LLMs excel at semantically understanding the most relevant information for strategic tasks. With agents, this capability reaches new levels: platforms that understand the most important factors for company analysis can suggest, validate and iterate on report sections. This means that analysts can ramp up on more names, faster: a meaningful competitive edge in a market where being able to see and process more information can make the difference between a good and bad investment.
Early Risk Detection and Compliance Monitoring
Identifying and managing risks is a critical part of fundamental investing. These risks can come in a number of different form factors: a broker downgrading a rating, spending changes in a specific consumer segment, or even newly disclosed risk factors within a 10-K filing. While an agent may not be able to fully extrapolate the impact to the investment (yet), they can flag anomalies worthy of further examination. For a fundamental, bottom-up research analyst who is always trying to hold the complete picture of a company’s performance in his or her head, these flags are invaluable. On the compliance side, AI platforms can ensure research outputs meet regulatory standards by cross-checking that no unauthorized forward-looking statements or unsubstantiated claims slip through. Notably, agentic AI has already proven itself in analogous finance tasks like fraud detection and credit memo preparation, where multi-agent systems handle data gathering, analysis, and critique to produce a final recommendation. For an equity analyst, catching subtle risk cues early (financial or operational) that a human might overlook is better than knowing them too late.
Maximizing Analyst Productivity and Improving Decision Quality
The immediate promise of agentic AI in fundamental equity investing is a step-change in analyst productivity. While processes vary from firm to firm, equity analysts can spend 60+ hours a week scouring filings, transcribing management calls, updating models, and writing reports – a process described as rigorous but slow and prone to human bias and fatigue. By offloading much of the manual work to AI agents, research teams can cover more ground in less time. This efficiency means an analyst could follow twice as many companies or devote more time to high-level thinking, like debating investment theses and engaging with management teams, rather than parsing data line-by-line.
Initial deployments suggest substantial time savings. In one credit risk example, a McKinsey report observed that AI agents could reduce review and analysis cycle times by 20–60% while maintaining a solid quality level. These gains stem from AI agents’ ability to seamlessly traverse multiple data sources and aggregate information, as well as from their capacity to draft outputs that need only light editing rather than complete rewriting. The quality of decision-making can also improve alongside speed. AI agents can connect the dots across multiple documents to highlight patterns (e.g., a subtle deterioration in gross margins over several quarters across an industry) that inform better investment decisions. By deploying multiple specialized agents (one focused on accounting anomalies, another on macro indicators, etc.), an investment team gains a more holistic view of each situation, reducing the risk of blind spots.
Another critical benefit is that well-designed AI agents “show their work,” providing transparency into how conclusions are reached. Rather than outputting a black-box answer, an agentic system can break down a complex analysis into discrete steps and present intermediate results or citations. Well-built AI agents will clearly provide the chain of reasoning: for example, as part of a growth thesis, it might highlight key revenue trends identified, comparable valuation metrics, and excerpts from relevant news. This transparency and ability to click into the underlying sources is vital in investment research. It means analysts and portfolio managers can audit the AI’s outputs, build trust in the results, and satisfy compliance requirements that assertions are backed by documented research. Although AI can do much of the initial heavy lifting, humans still need to apply skepticism and domain expertise to make the final call.
The role of the human analyst is already starting to evolve into that of a high-leverage decision editor. When AI agents are able to handle initial data digestion and number-crunching, humans can shift from pure data gathering to directing and validating the AI’s outputs and acting as strategic reviewers rather than data retrievers. Analysts are able to spend more time thinking critically about assumptions, drawing on industry experience, and assessing qualitative factors like management credibility. Together, the whole can be greater than the sum of parts to make a more effective investment process.
Strengthening Portfolio Risk Management with AI-Powered Monitoring
Beyond boosting day-to-day productivity, agentic AI can materially strengthen portfolio risk management for long-only investors, especially within smaller teams that have limited headcount or no formal risk management process.
Risks that previously required manual identification (reading about a sudden regulatory policy announcement, a supply chain hiccup reported in trade journals, or even a sharp shift in consumer sentiment on social media) are signals that can be monitored by AI agents. This real-time vigilance helps ensure that risk factors are recognized early, rather than after the fact when the market has already fully priced in a change. AI platforms can also help in systematically comparing disclosures and data over time to pinpoint changes that could indicate rising risk. Changes to public filings disclosures, such as a warning about cybersecurity in its latest 10-K, can be flagged more proactively so that analysts can investigate further. Similarly, if an unusual number of insiders start selling stock or if credit default swap spreads widen for a portfolio company, an agent can pick that up from data feeds and raise a red flag. The collection of documents over which an AI agent can be deployed is both diverse and far-ranging, provided that the underlying platform is built in a way that supports financial services workflows and volume.
Building an AI-Augmented Future
As agentic AI continues to evolve, its applications to fundamental investing will almost certainly broaden and grow. It isn’t too difficult to imagine a future where domain-specific agents in finance are as ubiquitous as the Bloomberg terminal. By the time mass adoption is the norm, the opportunity will have diminished: forward-looking investment firms are already reorganizing teams and workflows to capture this opportunity. They are training analysts to work alongside AI (e.g. to craft effective prompts and to interpret AI output), investing in secure infrastructure for proprietary data, and updating compliance policies to govern AI use.
The winning formula in public equities is likely to be a human-augmented AI workflow that keeps analysts in the driver’s seat. The judgment, intuition, and domain expertise of seasoned investors remain irreplaceable, but they will be amplified by AI systems that provide instant analysis, breadth of coverage, and analytic rigor at scale. By embracing agentic AI thoughtfully – with a focus on transparency, oversight, and alignment to investment objectives – institutional long-only investors can reshape their workflows for the better. The result could be a future where fundamental equity investing is faster, smarter, and more resilient, with humans and AI agents working in tandem to achieve superior outcomes for clients. In an industry predicated on gaining insight and managing uncertainty, that partnership offers a compelling path forward into the next generation of investing.
