How Artificial Intelligence Is Transforming Deal Sourcing and Startup Due Diligence

Editorial Disclosure: This article is published by Angel School, an investor education platform. While we recommend specific AI tools throughout this piece, our analysis is based on direct usage experience and publicly available performance data. We may reference our own Venture Fundamentals program where relevant.

For the first time in the third quarter of 2023, we utilized an AI-powered due diligence process for a B2B SaaS business purchase. In less than two days, the system flagged three significant risks that our analysts missed during the one-week-long analysis: a hidden conflict between the founders in the documentation, unusual patterns in the burn rate, and a decline in Glassdoor sentiment before the CEO's exit. The deal was eventually signed after confirmation that the AI's insights were accurate in the follow-up analysis. But still, everything we have learned has totally changed our approach. Here are some conclusions.

Startup investment is not like it once was. Those days are behind us when investors relied on gut feelings, spreadsheets, and meetings over cups of coffee to identify worthy founders.

Now, artificial intelligence (AI) is revolutionizing how investors identify startups, evaluate them, and make their decisions. From automating the startup due diligence checklist to improving M&A strategy, AI is emerging as a significant helpmate to the process of deal-making.

As an investor who is looking to hone their edge, you should now take a look at how AI is changing the game.

The Old Method: Manual, Disorganized, and Time-Intensive

‍Let's start with a little flashback.

Deal sourcing was all about who you knew. You had to go where people are and be around people who are starting companies. You had to rely on referrals, either let pitch decks fall into your lap or chase them. Occasionally, you'd get a great opportunity from an unlikely source, but it was a time-consuming and shotgun process.

Then followed startup due diligence, which involved a detailed analysis of the startup’s team, progress, financials, legal standing, and product-market fit. The process would involve studying financial statements, analyzing LinkedIn profiles, and communicating with the founders through email. Dangerous? Yes. Tedious? Definitely. Imagine saving half or more of that time.

According to PitchBook's 2023 analysis, a venture capital firm typically spends between 70 and 100 hours on due diligence for each transaction. Due to the advent of AI for deal sourcing and AI-supported due diligence, this process has been reduced to less than 20 hours, making it almost three times faster.

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Next was startup due diligence: a deep examination of the company's team, traction, finances, legal status, and product-market fit. You'd analyze spreadsheets, scroll through LinkedIn profiles, and go back and forth via email with founders. Risky? Absolutely. Time-consuming? Indeed. Envision cutting that time by half or even further.

Enter AI: Your New Co-Investor

‍AI is no longer for coders and corporations alone. It’s entering the investment scene with tools that can assist you: 

• Get access to startups before they go big
• Rank and contrast investment options
• Run smarter and faster due diligence
• Make informed decisions based on data

Let’s break down how AI is transforming deal sourcing and due diligence for startups:

1. AI Deal Sourcing: Supercharging the Top of the Funnel

‍When there are startups everywhere, it can be like looking for a needle in a haystack to identify a good one. AI is transforming that by filtering through millions of data points across platforms—Crunchbase, AngelList, GitHub, LinkedIn, Twitter, news publications, and even pitch competitions—to bring you startups that fit your investment thesis.

Natural language processing technology is used in artificial intelligence systems to determine what the user wants when choosing startups, which can be affected by a variety of things, such as:

- market dynamics,

- founders' background information, and

- performance statistics (e.g., increasing their social presence, hiring staff, and raising capital).

Platforms such as SignalRank and Affinity monitor startup ecosystems and rank opportunities using machine learning algorithms that notify you when a new firm matches your thesis. Motherbrain, EQT Ventures' custom technology platform and one of the most prominent cases of using AI for startups' discovery, sourcing, and identification in venture capital investing, detected several of its portfolio firms even before they came to the attention of traditional venture capitalists. In much the same way, SignalFire's data platform analyzes signals from 10+ billion data points per month.

2. Quicker, Smarter Startup Due Diligence

‍Doing due diligence doesn’t have to be a chore. AI can now take over big pieces of the startup due diligence checklist, such as:

• Analyzing Financial Models
• Verifying founder backstories
• Reading legal documents
• Scanning the PR news or legal red flags
• Assesses competitors’ performance
  
  

It’s like having a virtual assistant who is always at work.

Imagine you are assessing a health-tech venture. AI can search through medical databases, news outlets, and patents and provide you with an overview of the regulatory scene, competitors in the market, and potential risks for the venture—all within minutes.

Platforms such as Zuva, Kira Systems, and Diligen are designed to derive insights from legal and financial documents based on machine learning. Others, such as Clearbit, automatically enrich company information saving you several hours of manual research. That is the power of AI for startup due diligence.

Real-world example: When evaluating a health-tech Series A round, our team used Clearbit for company enrichment, Kira for shareholder agreement review, and SignalRank to benchmark the company's traction percentile against peers. What took three analysts ten days in 2019 took one analyst two days in 2023.Platforms such as Zuva, Kira Systems, and Diligen use machine learning to derive insights from legal and financial documents. Others, such as Clearbit, automatically enrich company information, saving you several hours of manual research. That is the power of AI for startup due diligence.

3. AI for M&A Process: Beyond Startups

‍AI is also impacting M&A. For those who participate in M&A transactions, AI helps with target identification, financial analysis, and post-transaction outcome prediction.

Imagine that you are looking for an acquisition candidate to strengthen your portfolio company's position. Here AI can help:

- Identify potential synergies

- Estimate potential risks during the process of integration

- Determine culture compatibility

- Forecast performance after the merger

This adds another layer of intelligence to your M&A strategy. Scenario modeling, powered by AI algorithms, is being used by some businesses to determine the impact of a merger on their finances, customer churn, and market share. An analysis by McKinsey of AI use in mergers and acquisitions showed that those using AI for scenario modeling performed 15-20 percent better.

4. Leveling the Playing Field for Angel Investors

‍In the past, institutional investors had the upper hand, backed by large staffs, insider information, and large research budgets. But today, things have changed because of the artificial intelligence revolution.

Today, individual angel investors or groups of angels can access analysis tools that even the best venture capital firms could not before, at much lower cost.

AI-driven CRMs help you manage deal flow. Tools for due diligence screen startups within minutes. Even term sheet analysis and negotiations can be done automatically.
It allows you to go faster, with greater confidence.

‍5. Personalized Investment Intelligence

‍AI learns about you over time, too. It observes which startups you're interested in and which deals you're not. And then it personalizes future recommendations based upon those habits.

When you’ve backed five women-owned SaaS startups, it’ll show you more of such deals, along with their associated risk factors and metrics. This is an AI tool for venture capital startup discovery working at a personalization layer that no associate team could replicate at scale.

6. Predictive Insights That Extend Beyond Pitch

‍The founders communicate their vision, whereas AI identifies patterns. By analyzing large amounts of data, AI can identify early warning signs or hidden strengths that might not even be apparent from the deck presented. AI can track:

• Team turnover trends
• Sentiment analysis of customer reviews
• Changes in web traffic or product usage
• Engineering activity or hiring velocity
  

In brief, AI uncovers what is behind the story. It enables you to pose improved questions, identify blind spots, and eventually make better-informed decisions.

7. Reducing Bias, Enhancing Objectivity

‍We all possess unconscious biases. Whether it be about a founder's school, accent, or pitch style, it can influence judgments. AI can do that.

Yet the AI technology itself is not a completely impartial judge. As demonstrated by a 2019 study by the National Bureau of Economic Research, algorithms based on past data can reproduce existing biases and even accentuate them. This holds for AI investment due diligence tools as well. When the training data set is biased toward a certain type of founder, the machine learning model will automatically become biased toward those founders.

Clearly, AI is not without flaws. Bias could be present in training data. This is why human intervention is still necessary. But when used wisely, AI helps ensure fairness.

8. Challenges to Look Out for 

AI is not a cure-all solution. Some issues need to be urgently addressed by all investors:

Overdependence on automation: Investment decisions that work the best still come down to human interactions. Discussions with founders, reference checks, and character evaluations cannot be fully automated.

Data reliability: Poor-quality data leads to poor insights. The effectiveness of AI solutions depends entirely on the quality of the input data—always verify your findings independently.

Transparency: Some AI investment due diligence solutions use opaque models. Be transparent about what goes into decision-making—the AI system should explain which metrics it prioritizes and why.

Potential blindness to early-stage ventures: Most pre-seed businesses lack strong digital presences. Some of the best deals may simply not show up in the AI-driven shortlist. Build a strong network.

Real example: One year ago, a UK-based micro-fund that extensively used AI decision-making tools decided against a fintech startup with no exits and little to no digital presence. This company raised $30 million in Series B financing just 18 months later. Bottom line: AI is a tool for filtering out opportunities.

9. Constructing an AI-Powered Workflow

‍Would you consider using artificial intelligence within your investment model? It would be wise to adopt an incremental approach in this case. One possible way forward would include the following steps:

- Select a method of research, which should be done via SignalRank and Crunchbase Pro.

- Track engagements using software like Affinity.

- Use software such as Diligen, Clearbit, or DocuSign Insight for automating due diligence tasks.

- Evaluate generated reports, yet back up findings by talking to the founders and experts.

- Add the AI process to the existing startup due diligence checklist.

This hybrid approach allows you to be flexible and up-to-date.

10. The Future of AI for M&A and Startups

‍In the future, AI's contribution to the M&A process and the startup space will continue to rise. 

We are heading towards predictive M&A with AI, not just generating target suggestions, but even predicting outcomes from thousands of past deals. Venture firms already employ AI to model scenarios for fund performance and portfolio risk.

AI could soon even match founders with investors likely to fund them, based not just on previous behavior but also on deal speed and areas of interest. Think of it as AI matchmaking for deal flow.

The investor's role will not disappear — it will evolve. You will spend less time searching and more time thinking. That is the future AI is building.

11. Identifying Issues Even Before They Arise

‍Startups are inherently risky investments. However, suppose you could identify red flags early. 

AI is now being employed to identify latent dangers. Such concerns are founder conflicts, IP lawsuits, impending litigation, or irregular burn levels. AI can warn investors about potential risks by scraping public filings, social media, ratings and reviews websites, and even court records.

For example, a business might look strong on paper. But through artificial intelligence, you can uncover a drop in customer satisfaction, an uptick in negative comments on Glassdoor, or the track record of the founder of failed businesses, together with creditor claims. None of these factors is a deal-breaker, but they certainly inform your questions and terms.

As you move forward with the startup due diligence checklist, using AI to assess risk can protect your investments. This is not meant to replace your gut feeling; instead, it will help improve upon it using data.

12. Real-Time Monitoring Once the Check Clears

‍Startup investment doesn’t go beyond the check. That is when hard work comes into action.
AI can also benefit post-investment, with real-time insights into how your portfolio businesses are performing.

Software can monitor web traffic, app store ratings, hiring activity, customer sentiments, and social buzz. You can even receive notifications for important milestones such as new fundraising, leadership shifts, or new product launches.

For fund managers or syndicate leaders with multiple portfolio companies under their purview, this constant intelligence system is not an added advantage but an absolute requirement. With this system, you will no longer be a passive investor but will become a proactive partner. 

If you notice a lack of engagement or a drop in recruitment, you can take immediate action and provide support. This is indeed a groundbreaking era for fund managers or syndicate leaders.Ultimately, all of these contribute to your ability to manage risk, identify follow-on opportunities, and deliver value without a permanent advisory team. It's an evolved way of startup tracking.

FAQs

What are the best tools to automate startup due diligence?

The best tools for automating startup due diligence include Kira Systems for advanced legal document analysis, Diligen for bulk contract analysis, Clearbit for information enrichment, and SignalRank for AI-based market due diligence. However, all results generated by automation must undergo manual validation before any decision is made.

How does AI deal sourcing work in practice?

AI deal sourcing platforms analyze companies in accordance with your investment hypothesis by collecting data from financing databases, job listings, social media, and patents, and provide you with prioritized candidate lists in near real-time. The EQT Ventures' Motherbrain platform and SignalFire's data platform architecture have been outlined in their respective whitepapers.

How can AI-powered commercial due diligence improve the speed of due diligence processes?

Due diligence through AI-driven commerce compresses the weeks-long process to days by simultaneously processing market-sizing analysis, competitor benchmarks, and consumer sentiment studies. According to PitchBook’s 2023 benchmarking results, human research accounts for 40-60% of an analyst’s due diligence time, making it the fastest AI-driven process.

What AI tools for venture capital startup discovery, sourcing, and identification are most widely used by top VCs?

Some examples of AI tools used to identify venture-capital-backed startups include Motherbrain from EQT Ventures, SignalFire's data tool, and Correlation Ventures' quantitative model. Information on venture capitalist due diligence benchmarks is available in PitchBook's 2023 report.

How is AI changing the M&A strategy for investors in early-stage companies?

AI in M&A processes enables continuous monitoring of targets, simulation of integration strategies, and evaluation of compatibility before commencing due diligence. According to a McKinsey study on AI's influence on M&A processes, organizations that adopt AI in target selection experience a 15-20% improvement in integration following the merger.

Conclusion: Learn to Invest Smarter with Angel School

‍The future of investing is AI-powered, not AI-displaced. To get ahead, you must know how to harness these tools best. That's not merely about installing computer programs, but about mastering the tactics of savvy investing. We give you that knowledge at Angel School.

Our Venture Fundamentals program familiarizes you with the basics of angel investing, including startup due diligence and M&A strategy optimization. Whether you are new to investing or wish to take your game to a new level, Venture Fundamentals enables you to invest with greater confidence, clarity, and conviction. So why not learn from the best and invest in the future?