In recent years, AI and automation technologies have grown rapidly, transforming industries with new algorithms and intelligent solutions. The VC and startup ecosystem is no exception — AI is now woven into the very processes that keep it running efficiently. However, investors remain cautious about full reliance on the technology. They describe their approach as a delicate balance: using AI to process vast data and automate repetitive tasks while keeping strategic decision-making firmly in human hands.
To explore how AI is reshaping VC practices, Vestbee spoke with leading regional investors — Roosh Ventures, J&T Ventures, and FIRSTPICK — about their adoption of AI tools in daily operations. For more on how VCs are adapting their strategies to navigate the rapidly evolving AI landscape and its regulations, check out our previous article.
1. Processing vast amount of data
In the fast-paced world of venture capital, speed, accuracy, and strategic insights are everything. Yet, the industry remains heavily dependent on manual data processing and human oversight, a bottleneck that slows decision-making and introduces the risk of human error, particularly when handling vast amounts of complex financial data.
“AI tools are highly relevant to the VC industry, enabling more efficient deal sourcing, due diligence, and portfolio management. They empower firms to analyze vast datasets, identify emerging trends, and streamline decision-making processes, ultimately enhancing competitiveness,” Andrew Tymovskyi, Principal at Roosh Ventures, emphasizes AI’s role in optimizing VC workflows.
Modern AI platforms can analyze thousands of pages in minutes, extracting key insights that would take analysts days to piece together. By handling the heavy lifting of data processing, AI frees up investors to focus on what truly matters — strategic analysis, big-picture thinking, and making smarter investment decisions.
2. Deal sourcing and screening
Sorting through thousands of startup pitches each month can be an overwhelming task, but AI-powered tools are making it significantly more manageable. Large language models can rapidly analyze key factors — such as market potential, team composition, and technological innovation — helping investors cut through the noise.
By automating the initial screening phase, AI allows analysts to focus on the most promising opportunities rather than getting lost in sheer volume. This AI-driven triage ensures that high-potential startups receive human attention, while lower-quality prospects are efficiently filtered out—making the deal-sourcing process both faster and more effective.
3. Market trend analysis
AI’s impact goes beyond screening. It can also help investors quickly identify emerging trends and disruptive industries. Advanced AI systems analyze diverse data streams from patent filings and regulatory shifts to social media discussions and competitor movements, detecting signals that might otherwise go unnoticed.
“AI is becoming incredibly important for VCs. It helps us analyze data faster, uncover trends earlier, and make better decisions overall. In a world where we’re bombarded with information, having AI tools to synthesize and surface insights is a game-changer,” says Andra Bagdonaite, Partner at FIRSTPICK.
4. Portfolio management
AI is not only changing how VCs find investments — it’s also reshaping how they manage their portfolios.By leveraging real-time data analysis and predictive modeling, AI enables investors to make faster, more informed decisions about capital allocation, follow-on investments, and exit strategies.
One of AI’s biggest advantages in portfolio management is its ability to continuously monitor and adapt. Machine learning models can track individual startup performance, market conditions, and risk factors, allowing VCs to dynamically rebalance their investments. It can also help in fostering synergies within the existing portfolio, uncovering opportunities for partnerships, acquisitions, or joint ventures that might otherwise go unnoticed.
“We’re using AI to refine our investment strategy and portfolio management. This includes tools for spotting trends, analyzing deal flow, and helping founders scale more effectively. We want to stay ahead by using the same innovative technologies we’re investing in — it keeps us sharp and gives us a real edge,” Bagdonaite explains.
AI tools used by VC investors
VCs are already integrating AI-driven tools into their daily workflows, leveraging a mix of off-the-shelf and proprietary solutions to streamline operations. Roosh Ventures, for example, uses:
- Fireflies, AI-powered tool for transcribing and summarizing calls, ensuring key insights are captured for future reference.
- Granola, AI meeting assistant that integrates seamlessly with CRM systems, streamlining workflows and data entry.
- Affinity, relationship intelligence CRM designed for VCs, helping track interactions with founders and investors.
- ChatGPT, AI assistant for drafting, brainstorming, and accelerating research processes.
- Claude, AI model for generating insights, analyzing data, and enhancing decision-making.
- STORM, AI-powered research and analytics tool designed for venture capital firms.
- Carried AI, AI assistant that automates investment research and portfolio management tasks.
- Harmonic, data platform for discovering early-stage startups and tracking emerging trends.
- Exploring Aviato, AI-driven tool for identifying stealth founders and uncovering high-potential startups.
Roosh Ventures not only uses these widely available tools but also tests solutions developed by their portfolio companies: Folk, a next-generation tool for building custom CRMs, and Upstream, a platform designed for team communication and task management.
Adam Kocik, Founding Partner at J&T Ventures mirrors this trend, leveraging innovations the fund invests in to stay ahead of the curve. “We have built our own proprietary AI matching tool, which helps us to analyze startups and match them with relevant co-investors or next rounds investors, of course we also use ChatGPT, Perplexity and Notetaker to streamline the information search and analysis, while experimenting with various other tools such as Claude or Gamma,” he tells.
AI biases and limitations
Despite AI’s growing influence in venture capital, skepticism remains high — especially when it comes to delegating decision-making to algorithms. People are irreplaceable in assessing investment opportunities, and for now, final decisions remain firmly under human control. Beyond philosophical concerns, there are practical limitations that hinder AI’s effectiveness in the VC industry.
AI models depend on the quality of the data they are trained on, but in venture capital, data quality is often a challenge. Unlike public markets with standardized financial reports, the startup world is much less transparent. Important details like financial health, deal terms, and competitive positioning are usually kept private, making it hard for AI to provide accurate insights.
Even as data becomes more standardized and improved, AI is still vulnerable to bias — and in venture capital, this can have serious financial consequences. AI bias, or machine learning bias, happens when AI models reinforce existing inequalities in funding and investment decisions. Over-reliance on AI models based on past trends could cause missed opportunities. To add real value, AI must be regularly audited and refined to support, not limit, the identification of groundbreaking startups.
AI is just that — a tool, emphasizes Bagdonaite, “It’s a new piece of software that can make our work more efficient, but it doesn’t replace the human element. Experience, context, and gut feeling still play a critical role in evaluating startups and making investment decisions.”
AI can save time, improve accuracy, and streamline analysis, but it’s not perfect. Venture investing is rarely just about numbers — it involves assessing vision, leadership, and market potential in ways that are difficult to quantify. This is precisely why FIRSTPICK has drawn a clear line when it comes to certain aspects of its investment process. “This is why we have decided to shy away from AI tools when making, for example, investment memos, as we believe that these require original thought,” says the fund’s Partner.
AI as an enabler and not a decision maker
The future of AI in VC will depend on how well the industry can balance automation with expertise, ensuring that technology enhances, rather than dictates, investment strategies. While AI will continue to evolve, the ability to identify and back groundbreaking startups remains on the human side.
