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The Role of AI In Venture Capital: Evolution or Replacement?


Venture capital is a high-risk industry, with a significant percentage of start-ups failing to generate sufficient returns. AI has emerged as a valuable tool to enhance venture capitalists' decision-making processes and mitigate risks. The integration of AI in venture capital can complement human decision-making, improve success percentages, and contribute to the overall growth and success of the industry.

Venture capital is a high-risk industry. And although venture capitalists possess a strong understanding of investments, markets, and risks associated, they still can make unfavorable decisions. Research conducted by Shikhar Ghosh, a senior lecturer at Harvard Business School, reveals that approximately three-quarters of the companies supported by venture capitalists fail in the U.S.

Investing in early-stage companies with innovative ideas or products has always carried inherent risks. Therefore, redefining the venture capital model is unnecessary, as it would undermine its essence. Instead, venture capitalism could enhance its capabilities by improving the evaluation of investment opportunities.

This is where artificial intelligence (AI) can contribute through data analysis, predictive analysis, portfolio management, due diligence, and deal sourcing. AI can complement human decision-making in this domain.

Why Is Venture Capitalism Risky?

Venture capitalists primarily focus on investing in early-stage entrepreneurs who possess innovative ideas, products, and a team with strong credentials. These entrepreneurs typically introduce new or lesser-known products or services to the market, addressing a specific pain point or business problem. Venture capitalists carefully evaluate the business idea and assess its potential for success. Based on their assessments, they decide whether or not to invest funds in the venture.

Venture capitalists generally prioritize backing start-ups rather than established businesses due to several reasons:

  • Influence and decision-making: As investors, venture capitalists have a significant say in how a start-up is managed. They can actively participate in key decision-making processes, including hiring key personnel and shaping product and business strategies.
  • Agile and fast-paced: Start-ups operate in an agile, aggressive, and fast-paced manner. Unlike larger corporations, they are not burdened by bureaucracy or red tape, allowing them to swiftly navigate the market and capitalize on opportunities. This agility is highly desirable for venture capitalists seeking to capture emerging markets.
  • Growth potential: Start-ups with innovative business ideas, if successfully executed, have the potential to rapidly grow and dominate the market. This exponential growth can generate substantial returns for venture capitalists.

However, history has shown that venture capitalism is fraught with risks due to a significant percentage of start-ups failing. Ghosh’s research conducted on over 2,000 companies that received investments from venture capitalists between 2004 and 2016 revealed several pertinent facts:

  • Around 75% of the firms backed by venture capitalists fail to generate sufficient returns to recover the invested capital;
  • The National Venture Capital Association estimates that 25% of the firms backed by venture capitalists ultimately fail;
  • Out of every 10 venture capitalist-backed firms, only three or four are able to return the investment, while approximately two produce substantial returns.

Venture funding is considered risky primarily because numerous factors crucial to a firm’s success lie beyond its control. There are several factors that could potentially lead to unfavorable outcomes:

  • Disruption of crucial partnerships: If a deal between the firm and a third-party organization, vital to the firm’s success, fails to materialize, the venture capitalist has limited control over the situation.
  • Poor product-market fit: Insufficient research and due diligence regarding the reception and demand for the product can lead to a mismatch between the offering and the target market.
  • Inefficient resource management: If the firm fails to optimize and effectively utilize its resources, including financial, human, and technological assets, it can hinder growth and jeopardize its chances of success.
  • High competition and profitability challenges: Operating in a highly competitive niche can make it exceedingly challenging for a firm to generate profits.

How AI Can Help Venture Capitalists

Many venture funding decisions have traditionally been influenced by gut feelings and limited research. However, according to Gartner, a leading research and advisory firm, it is projected that 75% of venture funding decisions will be based on data and analytics provided by AI rather than relying solely on intuition.

Patrick Stakenas, a senior research director at Gartner, explained:

This “impossible to quantify inner voice” grown from personal experience is decreasingly playing a role in investment decision making. The traditional pitch experience will significantly shift by 2025 and tech CEOs will need to face investors with AI-enabled models and simulations as traditional pitch decks and financials will be insufficient.

Provide Analytics

As venture capitalists and investors navigate the investment landscape, there is a growing shift towards quantitative decision-making, supplementing traditional reliance on gut feelings, experience, and understanding of a firm’s potential. AI tools play a crucial role in this transformation by consolidating data from various sources including LinkedIn, Crunchbase, PitchBook, Owler, and third-party data marketplaces. These tools present the data in a format that empowers investors to make well-informed decisions.

According to Patrick Stakenas, senior research director at Gartner:

This data is increasingly being used to build sophisticated models that can better determine the viability, strategy, and potential outcome of an investment in a short amount of time. Questions such as when to invest, where to invest, and how much to invest are becoming almost automated.

Predict Success or Failure

AI tools can help venture capitalists predict the success and failure probability of a firm based on data and other factors. For example, it can do due diligence on the product and market fit, market size, revenue, and profitability and provide the required analytics to venture capitalists.

Data-driven analysis can significantly improve the probability of higher returns on investment. AI tools can find data on the entrepreneurs and their credentials and ability to drive the firm to its goals. Quantitative decision-making can potentially improve the success percentages of venture capitalism.

A case study
Correlation Ventures is an innovative venture capital firm that uses AI tools for investment purposes. With $365 million in management for investments, it claims that its utilization of AI has significantly expedited investment decisions to under two weeks. At the heart of their investment process lies an in-house AI tool that ingests data from a pool of over 100,000 venture capital rounds. By analyzing information, the tool provides a comprehensive scoring system that guides the management in their investment decisions.

The Bottom Line

It’s difficult to determine the exact percentage of venture capitalists using AI to make investment decisions, but many have been moving in that direction. Investment decisions have traditionally relied on gut feeling and other non-quantitative factors, which may have contributed to the low success rate of venture capitalists.

The market for various products has become increasingly competitive, making it extremely challenging for entrepreneurs to generate profits. Data from the past decade reveals a significant number of startups that have failed to sustain their initial hype.

In this context, AI plays a critical role in identifying the right entrepreneurs for venture capitalists.


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Kaushik Pal
Technology writer

Kaushik is a technical architect and software consultant with over 23 years of experience in software analysis, development, architecture, design, testing and training. He has an interest in new technologies and areas of innovation. He focuses on web architecture, web technologies, Java/J2EE, open source software, WebRTC, big data and semantic technologies. He has demonstrated expertise in requirements analysis, architectural design and implementation, technical use cases and software development. His experience has covered various industries such as insurance, banking, airlines, shipping, document management and product development, etc. He has worked on a wide range of technologies ranging from large scale (IBM…