Below is an installment of the series, “From Algorithms to Innovation: AI Redefines the Frontiers of Global Finance” by Sarah Hammer, executive director at the Wharton School.
This forward-looking series explores the unique capabilities and unprecedented challenges of generative AI across the financial landscape. In this installment, Sarah Hammer dives into the impact of AI on the private capital investing and banking industries.
In a world where algorithms are reshaping industries at great speed, the investment landscape stands on the cusp of a significant transformation. Generative AI, once the stuff of science fiction, is now poised to unleash a new wave of change, with McKinsey projecting an annual value surge of $200-$340 billion in banking alone. Investment firms, therefore, find themselves at a crossroads, grappling with the task of how to integrate this capability while maximizing the human elements that form the cornerstone of investing. To truly grasp the magnitude of this shift, we must dissect how AI is redefining every link in the process of investing — the “investment value chain” — from number crunching to strategy formulation to client interaction.
The tasks typically performed by investment professionals are at the core of this value chain. These activities can be broadly categorized into those that are data-focused and those for which a human is essential. Data-focused tasks include financial statement analysis, financial modeling and valuation, undertaking due diligence, identifying market research trends, and tracking financial and economic indicators. These functions also include the creation of reports and presentations such as investment memorandums and pitch books. These activities often involve processing large amounts of information, identifying patterns in complex datasets, and summarizing output.
Human-centered tasks are those that could not take place without human beings. These involve fostering relationships with colleagues and working within a team, meeting and cultivating clients, maintaining awareness of the business environment, evaluating the firm’s overall portfolio and recommending adjustments, and conducting comprehensive risk analysis. They also include negotiating deals while managing complex interpersonal dynamics and leading the overall deal process. These activities rely heavily on emotional intelligence, experience, and the ability to navigate nuanced social situations.
AI is well-suited to undertake many of the data-focused tasks in the investment value chain. In fact, its competencies are developing even faster than Moore’s Law, which predicted exponential growth in technological capacity. AI can process vast amounts of information quickly, identify patterns in market data, and generate initial drafts of reports. Some firms are already leveraging these capabilities to enhance their operations. Blackstone has integrated generative AI into its investment process. KKR uses AI to improve worker productivity and assist deal teams in sorting through data when researching potential investments.
Vikram Mahidhar, Data and Digital Transformation Operating Partner at Apollo Global Management, notes that a key benefit of AI is its ability to make analysts more efficient in trend identification and data analysis. This efficiency could allow investment professionals to focus more on strategic thinking and complex problem-solving, potentially reshaping the investment value chain.
However, the human-centered aspects of the investment process remain challenging for AI to replicate. Building trust with clients, reading emotional cues during negotiations, and developing a nuanced understanding of market dynamics require human expertise. Investment conviction – the confidence in a particular opportunity based on experience and intuition – is also distinctly human and crucial in making investment decisions. Therefore, the notion that AI will soon surpass human capabilities in all aspects of the investment value chain remains premature and overly simplistic. It is an admonition of Moravec’s paradox, which observes that high-level reasoning requires surprisingly little computation, while low-level sensorimotor skills and perception demand far greater computational resources.
Implementing AI across the investment value chain also comes with challenges. Data quality is a significant hurdle, as private equity and venture capital firms often deal with messy, unstructured proprietary data. Successful firms will need to invest in data cleaning, establish AI governance processes, and identify suitable talent and technology partners to ensure they are employing AI safely and appropriately at each stage of the investment process.
Human oversight of AI remains crucial, as even sophisticated AI systems can make errors or produce misleading results, called “hallucinations,” particularly in the complex, nuanced investment world. Moreover, it is essential for firms to develop and adhere to a robust code of ethics, which requires uniquely human capabilities and judgment.
A final consideration is the cost of AI implementation. Firms must carefully evaluate the return on investment for each link in the value chain. To be sure, AI’s mere speed doesn’t necessarily justify its cost or guarantee better outcomes.
In the evolving business landscape, successful firms will effectively combine AI capabilities with human expertise throughout the investment value chain. As Carl-Magnus Hallberg of EQT observes, “Technology is becoming even more important across all business functions.” This highlights a need for a comprehensive approach to AI integration, touching all aspects of an investment firm’s operations.
The integration of AI may well lead to a reconfiguration of investing. AI could handle routine, data-intensive tasks, freeing human professionals to focus on strategic decision-making, relationship management, and complex negotiations. This shift might also create new roles, such as AI-human collaboration specialists or AI-enhanced strategic advisors, adding new links to the value chain and opening up exciting new possibilities in the industry.
Moreover, using AI tools could incrementally level the playing field, conceivably allowing smaller firms and new entrants to compete more effectively with established players across various stages of the investment process. Also, by carefully consolidating their own data and that of their portfolio companies, firms can gain new business insights and identify novel value-creation opportunities, potentially expanding the scope of traditional investments and generating a new wave of innovation-driven ventures.
As the investment industry evolves, firms must invest in upskilling their workforce to work effectively alongside AI systems. They’ll need to maintain a critical eye on AI outputs and understand the technology’s limitations and potential for errors at each stage of the investment process.
While AI will undoubtedly change how many investment firms operate, it’s unlikely to replace human investors entirely. Instead, successful firms will use AI to enhance human judgment and expertise, igniting a realm of possibilities. The future of investment lies in finding the right balance between technological capabilities and human skills, leveraging each to amplify the strengths of the other throughout the investment process.
Sarah Hammer is Executive Director at the Wharton School, Adjunct Professor at the University of Pennsylvania Carey Law School, a former federal and state government official, and a former asset management executive.