How Investment Teams Are Leveraging Generative AI to Enhance Research

Generative artificial intelligence (genAI) continues to deliver innovative and transformative use cases for the financial services industry. While its capabilities are powerful and vast, it can be challenging for firms to understand how to successfully harness generative AI in everyday workflows. 

Key findings from a recent BCG report indicate that while 72% of asset managers acknowledge that genAI will have a significant impact in their organizations, only 16% have fully defined a strategy to execute on.

To put structure around this transforming force, it is critical to understand the different solutions available to investment teams and how to best evaluate the benefits and potential downsides, with best practices to achieve optimal results. 

Across the industry, genAI is creating efficiencies and helping firms get more value out of their internal and external knowledge. Identifying the right tools and methodologies to capture these benefits is critical for firms to remain competitive and generate more alpha.

Below, we explore how investment teams are leveraging the power of genAI and explore key considerations for successfully incorporating this technology into investment workflows. 

The Build vs Buy vs Partner Dilemma

In our 2023 State of Gen AI & Market Intelligence report, over 80% of respondents planned to leverage genAI tools in their research in 2024. A foundational first step in this process is identifying a viable technology solution. Often, firms face a common dilemma when evaluating and potentially implementing a genAI technology solution: to build, buy, or partner? 

Building an In-House Solution

Developing a bespoke proprietary solution is a considerable undertaking, and one that isn’t right for everyone. Building large language models (LLMs) requires a significant investment of time, resources, and funds in order to effectively sanitize and curate large data sets.  Additionally, this option necessitates continuous maintenance to stay up to date. Building the model in-house also introduces data and privacy risks, and it can be challenging to scale without overwhelming in-house teams.

On the other hand—an in-house build allows for complete customization and tailoring to a firm’s specific needs and internal requirements, and maintains independence from a third-party provider.

Buying an Out-of-the-Box Solution

An out-of-the-box model can seem like the path of least resistance, offering quick use and implementation, with no heavy lifting required, and is typically a more cost-effective route. 

However, this solution comes with its own challenges, such as a lack of customization options, the inability to capture information critical to decision-making, or difficulty meeting internal compliance requirements. Its efficacy also relies on the complexity of the internal knowledge and use cases it’s trained on. Because many pre-built solutions are not trained on business-specific datasets, they can be prone to providing inaccurate yet convincing responses, which could introduce serious reputational and operational risks. 

Partnering with an External Solution Provider

This option marries the best qualities of an in-house build with the appeal of a pre-built genAI offering. An external partner will have already invested the time, resources, and have an existing development team in place to maintain and optimize their proprietary interface. This enables investment teams to have quick set-up and connectivity, as well as access to the most cutting-edge technology. 

Solutions like AlphaSense are built with an expansive universe of business content that cater to your everyday use cases, along with the global data protection and security standards that are crucial for risk mitigation. A partnered solution also delivers customization that is relevant to your firm’s specific needs and requirements, without excessive investments of time or resources. This option is much easier than building your own solution, and it’s also much more reliable and personalizable than a generic out-of-the-box solution. 

Additional Reading: Enterprise GenAI: To Build or Buy a GenAI LLM? 

Everyday Use Cases

Since its debut, genAI has presented remarkably intricate, time-saving, and intuitive everyday use cases for investment teams. They include: extracting insights from large bodies of content to accelerate research, structuring and organizing data, surfacing trends and disruptors in the marketplace more quickly, reducing time spent on due diligence, compounding knowledge faster than competitors, and supporting back-office operations (i.e. Client Servicing and Marketing and IT).

Market Research

GenAI is transforming the way investment teams conduct their market research by ensuring they quickly identify emerging trends and disruptors before anyone else. It streamlines and distills insights from company documents, industry briefs, lengthy regulatory filings, and other qualitative sources that are ordinarily time-consuming to scour and extract valuable insights from. 

An example of a purpose-built genAI capability is AlphaSense’s Smart Summaries feature. Drawing from a premium content universe, these generative summaries are designed to answer critical market intelligence questions with confidence. They provide key findings across earnings, analyst research, and expert calls—all verifiable within a single click, enabling you to get smart on a company, industry, or trend in seconds.

Related Reading: Generative AI in Market Research

Due Diligence

GenAI is accelerating the speed of sourcing and evaluating potential deals and investments. Sophisticated tools are rapidly emerging to scan and procure insights from reams of data in a fraction of time it would take a human to do the same job. 

By automating due diligence tasks up front, investment teams can significantly reduce the time spent on manual processes and re-allocate valuable time to analysis and idea generation.

Deal Sourcing

It is estimated that generative AI can reduce the amount of time spent on deal sourcing by at least 50%. For PE investors, genAI streamlines the deal sourcing process by screening thousands of potential target companies and shortlisting the most relevant ones using the firm’s selected screening criteria. 

This is creating tremendous efficiencies, vastly expanding access to information, and sharpening insights about both target companies and the macro conditions in which they operate. Increasingly, firms are leveraging natural language processing (NLP) to drive investment hypothesis creation, and more quickly create initial drafts of investment memos.

Portfolio Monitoring & Optimization

By streamlining market movements, events, and trends more fluidly, genAI helps managers optimize their portfolios in real-time. For instance, genAI can detect correlations between certain market events and performance, prompting an allocation adjustment. Ultimately, this drives more sound, well-informed investment decisions. 

There are also emerging AI-led algorithms that may optimize portfolios by analyzing historical data sets to identify optimal asset mixes and risk-adjusted returns. Even in challenging or volatile market conditions, asset managers can proactively take control of their portfolio by relying on genAI. 

Business Development & Client Communication

Through its innovative technology, client engagement can be optimized by 30%, and business development functions can see positive trends upward of 10%. GenAI is able to analyze large amounts of proprietary and external content to surface the most relevant market trends and themes, assess risk profiles, and assist in creating pitch decks tailored to investors’ needs and preferences. 

Business development teams are also leveraging collective knowledge to generate insights for client meetings on the road, as well as streamline communication with CRM teams. 

Related Reading: Generative AI Use Cases for Investment Firms

Optimizing Knowledge Discovery & Summarization

Perhaps the most coveted genAI use for investment teams is the ability to scale knowledge and unlock insights and intelligence in real-time. Teams can vastly increase the lifetime value of their market research by consolidating internal knowledge alongside external market research, for better knowledge discovery and sharing. 

Critical components of your firm’s market intelligence—internal research, investment memos, client deliverables, strategy presentations, and meeting notes—are often fragmented and inaccessible, resulting in lost opportunities and doubled work. 

AlphaSense’s Enterprise Intelligence is the answer to this challenge, serving as a secure market intelligence solution that layers AI search and summarization technology onto a consolidated library of both your proprietary internal research and premium market intelligence content. With Enterprise Intelligence, you can unlock the value of your firm’s prized internal knowledge and foster better collaboration across your entire team. Enhance the value of your proprietary research by using AI to search, summarize, and interrogate your proprietary internal data alongside a vast repository of 300M+ premium external documents. 

With features like Smart Summaries, Generative Search, and seamless integrations, you can extract crucial insights from both internal and external research in less time and with greater confidence. 

Instantly discover and verify insights while removing any unknowns, potential blind spots, and reputational risk. Keep your proprietary research secure with enterprise-grade data protection and SOC-2 compliance.

Related Reading: How Investment Teams Maximize the ROI of Institutional Knowledge 

Gain the Competitive Edge with AlphaSense

AlphaSense’s leading AI-search driven market intelligence platform combines premium external content with industry-leading AI capabilities that accelerate and enhance your research. Along with our Enterprise Intelligence solution that helps you get the most value out of your internal knowledge sets, AlphaSense equips you with everything you need for a comprehensive, competitive investment research strategy. 

Our cutting-edge features enable you to sift through the noise, accelerate your research, and bring efficiencies to your workflow: 

• Smart Synonyms™ is the backbone of the AlphaSense search engine, using natural language processing (NLP) to expand keyword and thematic searches beyond exact-match documents to include all relevant results.
• Sentiment Analysis extracts the tone and nuance that exists behind the surface-level meaning of a document or set of sources.
• Smart Summaries provides instant summarizations to reduce time spent on research during earnings season, quickly capturing company outlook, and generating an expert-approved SWOT analysis straight from former competitors, partners, and employees.
• Table Explorer eliminates the need to manually spread financials, automatically calculates key metrics, and enables you to instantly validate your numbers by viewing the original source of each number with a single click.

Check out this case study to learn how ODDO BHF, one of the largest private banks in Germany, used AlphaSense and its genAI capabilities to streamline insights and get the competitive edge.

Harness the power of genAI and competitively position your team—start your free trial of AlphaSense today.