How Investment Teams can Leverage GenAI

On the heels of a breakthrough year in 2023, generative artificial intelligence (genAI) delivered interesting, innovative, and even mainstream applications. While its capabilities are powerful and vast, understanding how to successfully harness genAI’s many use cases can often be complex and potentially obscure. 

It is expected that genAI will grow more than 20% annually from 2023-2030, burgeoning to $207 billion in market volume by 2030. With digital transformation here to stay, firms are increasingly exploring and implementing initiatives to best mainstream genAI, factoring in a growing list of logistical, strategic, compliant, and competitive factors.

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, everyday genAI use cases are evolving to create efficiencies, innovate workflows, and streamline internal and external knowledge within organizations. Identifying the right tools and methodologies to capture these benefits is critical for firms to gain the most value and remain competitive.

Perhaps the most prized use case of genAI is the ability to pair in-house research with the power of AI-layered technology to unlock insights and intelligence in real time and best capitalize on proprietary knowledge. 

Below, we explore how investment teams can harness the power of genAI, including the different solutions available in the marketplace, explore everyday use cases to extrapolate efficiencies and value, unlock the value of internal research and knowledge, and best practices to emerge as leaders. 

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 heading into 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 significant undertaking, and one that comes with many blind spots. Building large language models (LLMs) requires a significant investment of time, resources, and funds in order to effectively sanitize and curate large data sets. For model training alone, sophisticated technical resources are required (i.e., hardware, software, cloud services), in addition to onboarding personnel, performing routine maintenance and updates, and other considerations. 

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

Opting to procure and deploy a pre-built genAI solution can certainly seem like the path of least resistance, especially when weighing challenges and potential risks. An out-of-the-box model offers quick use and implementation, with no heavy lifting required, and is typically a more cost-effective route. 

A potential downside to this solution is the lack of customization options and the inability to capture information critical to decision-making, or meet internal compliance requirements. It is ultimately contingent on the complexity of the internal data and knowledge bank and desired uses, and can carry its own sets of risks. Additionally, pre-built solutions are designed for general use and are not trained on business-specific datasets that are relevant to your use cases. In some instances, relying on unverified content can lead to false but convincing responses (known as hallucinations) and can introduce serious reputational 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. It enables investment teams to have quick set-up and connectivity and 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. A partnered solution also delivers customization that is relevant to your firm’s specific needs and requirements, without large investments of time or resources.

Exploring external solutions generates questions around data security and data control, which are top of mind for managers. A strong external solution will claim compliance with global data protection and security standards (such as ISO and SOC), and have the ability to securely encrypt content and carry out routine testing.   

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

Everyday Use Cases

The challenges of sharing internal information can carry substantial financial impacts as a result of wasted time and suboptimal operations. Studies show that major US firms suffer annual losses exceeding $40 million as a result of everyday operational inefficiencies due to inadequate knowledge sharing.

Time is squandered searching multiple sources of information, and even then data is siloed across several platforms, and it is difficult to extract market intelligence. As resources become increasingly scarce and scrutinized, it is more critical than ever to accelerate information sharing, prevent duplication of work, and avoid making poorly informed decisions that can be costly in the long run. 

Often, investment teams are plagued with inefficiencies created by internal research and external content spread across disparate systems and teams working in silos. Firms lose the ability to swiftly pivot in response to market conditions and struggle to maintain a competitive edge as a result. 

GenAI has remarkably intricate, time-saving, and intuitive everyday use cases for investment teams to tackle this challenge. Some of these use cases include: extracting insights from large bodies of content to accelerate research, structuring and organizing data, surface trends and disruptors in the marketplace more quickly, reducing time spent on manual tasks and conserved resources, compounding knowledge faster than competitors, and back-office operations (i.e. Marketing and IT functions).

According to Boston Consulting Group, genAI stands to have a significant impact on the asset management industry through five primary uses:

• Enhanced Operating Efficiency: 10-15% anticipated improvement in operating efficiency, with some functions ranging as high as 40%–50%
• Personalization at Scale: Leveraging data sets to synthesize information, create content, and enhance the customer experience
• Knowledge Compounding: True cross-enterprise knowledge sharing, overcoming siloed structures, information tagging struggles, and streamlining processes
• Accelerated Research: Increase the speed and quality of investment research, and leveling the playing field for smaller managers who don’t have extensive research and data science teams 
• Coding Democratization: With natural languages serving as code for genAI, the need for technical and offshore resources (i.e. developers and coders) diminishes 

Recent findings from EY illustrate the surge in AI engagement, with 74% of PE-backed companies already using AI solutions in their transaction process or piloting potential solutions. Furthermore, findings show that genAI drove seven times more venture capital funding in the first half of 2023 compared to 2022.

Additional Reading: State of Generative AI & Market Intelligence Report 2023

Unlocking the Value of Prized Internal Knowledge

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 the external market research they rely on, making it easier to search and share.

By pairing proprietary, in-house research with the magic of AI-layered technology, teams can surface data, insights, and intelligence once buried within static documents, information silos, legacy platforms, and deficient knowledge repositories. 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. 

In addition to overcoming inefficiencies, genAI can help firms reduce reputational risk and loss of credibility with these information accessibility gaps. Disconnected sources result in duplication of work, potentially inaccurate output, and lost opportunities. 

AlphaSense’s Enterprise Intelligence is a solution 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:

• Upload and index 1TB of documents per day with APIs and third-party connectors
• Automatically integrate and tag your PDFs, SharePoint docs, CIMs, Excel sheets, and more
• Get quick context using genAI summaries with in-line citations—verifiable with one click
• Interrogate long documents with natural-language chat that goes straight to the source
• Keep IP secure with enterprise-grade data protection, ISO 27001 certification and SOC 2 compliance
• Easily control content access with simple user management and permissioning

Enterprise Intelligence allows you to enhance the value of your proprietary research using AI to search, summarize, and interrogate your proprietary internal data alongside a vast repository of 300M+ premium external documents. By centralizing siloed research workflows, you can make investment decisions with less reputational risk and significantly better results. 

Related Reading: How GenAI Revolutionized Market Intelligence in 2023

Guide to Enterprise Search

Gain the Competitive Edge with AlphaSense

AlphaSense’s leading AI-search driven market intelligence platform equips you with the insights, market intelligence, trends, and expert opinions to remove the complexity and guesswork of conducting your research. 

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 generates instant insights 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

Checklist: 4 Best Practices to Unlock Value from your Firm’s Internal Knowledge

Harness the power of genAI to centralize your investment research—start your free trial of Enterprise Intelligence today.