The rapid development of AI and other LLMs over the past year has bridged the communication gap between computers and humans. We no longer wonder what we could do with AI and instead we are now asking “What can AI do for us?”.

With all the new AI and automation tools available on the market the barriers for VCs to become data-driven has dropped. Some took the change while others are still just dipping their toes in the water, wondering why they should become data-driven. Some VC processes are easier to automate or delegate to the algorithms than others. So how has the VC industry taken advantage of the AI revolution so far?

Our team at VESTBERRY, along with Affinity.co, Deckmatch, Harmonic, and People Data Labs have partnered up with Dr. Andre Retterath from Data-Driven VC to examine the latest data-driven trends and initiatives, and gather the insights in a new exciting report called DDVC Landscape 2024. This comprehensive study, packed with insights from 190+ data-driven VC firms, delves into the current state of digitization in venture capital and its impact on the industry. In this blog post, we will offer a sneak peek into the report’s key findings and explore its significance for the VC ecosystem. If you want to read the report, you can download it here.

Why are VCs adopting the data-driven approach?

While a fifth of data-driven VCs (DDVCs) are still ramping up their initiatives, about a third already source more than half of their investments using modern tools. Using AI and other LLM tools empowers staff to work more effectively, increasing productivity by 40% and allowing funds to achieve more with fewer resources. Scaling through data and automation significantly enhances a VC’s ability to find new deals and monitor portfolio companies in the later stages.

How do VCs use AI in their workflow?

The results in the report indicate that VCs gain the most value from LLMs in their Screening, Due Diligence, and Sourcing processes. This is no big surprise, as the VC game is mainly about finding the needle in a haystack of good investment opportunities where many VC professionals rely on personal connections, experiences, and intuition. By deploying AI and data-driven tools, VCs can effectively broaden their scope and catch any outliers during the sourcing and screening process, maximizing their funds’ returns.

The report found that 35% of DDVCs say their data-driven tools are responsible for half of the deals sourced today. Imagine a “simple” setup where VCs use AI and automation tools to scrape and crunch data about potential companies from the internet to create comprehensive summaries for the investment team. Scaling through data and automation significantly enhances a VC’s ability to find new deals and, in the later stage, monitor portfolio companies. Conversely, non-DDVCs limit their scope and miss out on potential opportunities, effectively leaving money on the table. So, if higher returns are the reward, why are we not seeing everyone rushing to become data-driven?

The struggles of becoming data-driven

Scaling with automation or leveraging AI for efficiency in daily workflows usually requires a lot of effort and technical knowledge to set up and maintain. The most common roadblocks to becoming data-driven are finding the right tools and data sources and knowing what to do with the data to create usable outputs. It’s not a coincidence that data-driven VCs employ, on average, 5 data engineers to support their setup and efforts..

With the main struggle being finding the right tools, it can be helpful to see what the top data-driven VCs use in their tech stack as an inspiration. While DDVCs usually use an internally developed core stack, in most cases, it’s complemented by best-of-breed external solutions. The external solutions typically offer ways to consolidate, manage, and leverage huge datasets for specific processes like deal flow or portfolio monitoring. Here are the top tools hiding in the tech stack of data-driven VCs:

VCs typically gather data from external sources like Harmonic, People Data Labs, PitchBook, or Crunchbase when looking for the right data sources for deal flow and sourcing. Regarding portfolio monitoring and reporting, VCs have to rely on internally sourced data, usually stored in spreadsheets, Google Drive, and emails. Such tools are enough in the early stage of a VC fund; however, maintaining spreadsheets and consolidating data from multiple sources becomes chaotic and time-consuming as their operations grow. In most cases, VCs find themselves in a never-ending cycle, spending days creating reports and updating spreadsheets.

Fortunately, modern VC tools (as shown in the picture above) offer a significant upgrade from spreadsheets and drives. For example, using VESTBERRY for portfolio management & monitoring allows VCs to create a transparent source of their investment data and streamline tasks such as reporting, audits, KPI collection, and portfolio performance monitoring.

What’s more, centralizing all data in one place enables VCs to analyze their portfolio, identify underperforming companies, track trends, and uncover patterns within portfolioperformance—all possible without writing any code or relying on a data engineer. Book a quick call with our team and explore how your fund can become more efficient and leverage your data. We are happy to advise you on building your tech stack.