ai and chat gpt in corporate venturing
Venture building

How will AI and ChatGPT Change the Work of Corporate Venturing Units?

AI will fundamentally change how ventures are built, how ventures work, how start-ups are evaluated, and how investment decisions are made. Even though we are convinced that AI and ChatGPT have the potential for disrupting the field of corporate venture building massively, we are likewise convinced that AI will be mainly an additional add-on for corporate venture building.


AI and ChatGPT are omnipresent in the venture industry. Everyone talks about its impact on business models, the new possibilities for venturing and how potentially nobody will have a job in five years anymore, because a super smart machine learning engine will develop ventures from scratch. Do we believe this hype? Partially.

As a venture builder, we constantly observe the emergence of new technology closely, since technology is often a driver for new business models and start-ups. For example, we have established a systematic foresight process to detect so-called “trigger events”, which could lead to new business model opportunities for (corporate) ventures. A quite obvious trigger event which we did not have to search for, is the omnipresent discussion about AI as a follow-up to the release of ChatGPT in November 2022 and the release of ChatGPT 4 in March 2023. Its impact on society, the economy and the start-up ecosystem are evident and extensively discussed from ethical, political, legal and economic viewpoints. From a venturing perspective, especially the pace of acceleration in AI technology is staggering. The ability of AI computing power has doubled every 3.4 months since 2012, far exceeding that of Moore’s Law, which predicts a doubling of technological progress every 12-24 months (Crystalfunds, 2023). Such an unseen acceleration in technological development, which we can observe in AI will lead automatically to the acceleration of new business models and therefore venture cases. So we ask ourselves: how should venture units react actually on that?

We observed several hypes in the past, with very dramatic announcements about the game-changing potential for venturing: Blockchain, the Internet of things, genomics and robotics to name a few. But the actual organisational impact on corporate venturing units has in many cases been limited because the venture organisations did not integrate the new technological possibilities of the former hypes in their own processes but rather saw them primarily as strong investment opportunities. The hype about AI offers both for venturing: more investment opportunities and at the same time a fundamental change in the way venturing will be executed in the future. We tried to formulate three hypotheses, on how AI in general and particularly ChatGPT could change our industry in this article. In the upcoming series of articles, we will highlight more implications of AI for venturing.

Hypothesis 1: Process improvement: AI-based technology will highly facilitate the process of corporate venturing

AI technology has the potential for big improvements in the process of how corporate venture units work. With the right usage of the technology, they get helpful assistance along the entire value chain of venturing from the ideation of a business model concept, through the founding process to the scaling of the business (Chalmers, MacKenzie, Carter, 2021). Moreover, when having a portfolio of several active ventures AI can also assist the process of standardised administration of them and do profane tasks like email-based reporting, benchmarking and identifying the right KPIs. Gartner already predicted in 2021 that by 2025, 75% of VC firms will use AI technology for their own processes, starting with 5% in 2021 (Gartner, 2021). This is a prediction which gets enforced in the face of the recent accelerating development of ChatGPT. In the early stage phase of venturing, where ideas are formulated, comparison to existing business models and the subsequent definition of these business models are normally executed by a lot of manual research. In this stage we usually look into databases like Crunchbase, read tons of articles, whitepapers and studies and then extract the (biased) quintessence of what we found.

For all these tasks, AI can help to find the relevant sources more quickly, identify and extract the relevant content from those sources, and then provide its own interpretation of the results.

Venture builders can learn a lot from classic VCs, which have often already included AI technology in their workflows. One example is Hone - a small VC from Palo Alto, who partnered with AngelList to develop a machine learning algorithm based on the thousands of deals, which have been documented by the platform in the past 10 years. In addition to AngelList, data was sourced from Pitchbook, Crunchbase and MatterMark to enrich the database. From the created dataset they were able to identify more than 400 key characteristics such as funding raised, the founder's background, conversion rates etc. to deduct a success ranking for start-ups.

Another example from the VC world is Stockholm-based EQT Ventures, which has developed an internal AI framework called "Motherbrain" to guide its employee workflows. The platform is built on a proprietary database that includes data such as startup financials, site traffic, and team member job history, which is then used to rank investment opportunities (Foy, 2021).

Beyond building their own tool, venture builders can obviously integrate existing AI-based tools into their workflow. Some interesting tools for venture builders we discovered:

  • Consensus: to extract and distil findings directly from scientific research
  • Elicit: to find insights across 200 million research papers with GPT-4
  • seenapse: to generate hundreds of divergent, creative ideas
  • Dimeadozen: to validate your business idea
  • PitchBook's VC Exit Predictor: to discover and better evaluate investment opportunities across venture-backed companies

Hypothesis 2: Decision improvement: AI will highly improve the quality of investment decisions

In the number-driven start-up world, a lot of investment decisions and the decision about continuing a corporate venture case are normally done by a human board of investors. Still, a lot of (human) decisions in venture building are driven by numbers and KPIs. Here AI can assist by evaluating and analysing other start-ups, their business models and current funding streams, to see if the corporate venture builder is bidding on the right case and how a startup is performing. AI can collect, analyse and benchmark performance data better than any team of analysts. Moreover, the possibility to conduct complex statistical and regression analysis as an automated process generates a lot of added value for venture firms. In a nutshell, AI will help corporates assess risk more accurately by analysing data and identifying patterns. Corporate venture builders will additionally benefit from an accelerating amount of increasing data points and the increase of historical cases since AI-based tools will constantly enrich databases. Not only will the tools improve, but so will the quality of the data.

Hypotheses underlying the business model of a start-up can now be tested with a high level of confidence using AI systems. By using their existing data assets with AI, new ways are emerging to predict how customers or potential customers will react, for example, to a feature or price change (Chalmers, MacKenzie, Carter, 2021). Lastly, chatbots like GPT can assist in conducting due diligence by analyzing vast amounts of information on potential investments for corporate venture units. This can save time and resources, allowing corporations to move quickly on promising opportunities.

In a highly interesting research paper already published in 2021, you can learn how to build your own relational database management system (RDBMS) to get a set of exit and funding classifiers. Here besides using Crunchbase as a database, patents as an indicator of future success criteria of startups were used.

Source: Ross, Das, Sciro & Raza (2021)

Additionally there are many AI tools out there which support decision-making processes. With its intuitive AI platform, Ollie empowers teams to make better decisions using their existing data, faster. Ollie is similar to ChatGPT but for relational databases, meaning it can generate dashboards and answer ad-hoc questions in seconds, without requiring any coding. If you're a business owner, manager, or just an individual facing tough decisions, Rationale is a tool you should consider. With its latest GPT and in-context learning algorithms, Rationale helps weigh options by listing pros and cons, generating a SWOT analysis, conducting a multi-criteria analysis or causal analysis, and considering all relevant factors and your background. This way, you can make a rational decision that takes into account all the relevant factors. With Obviously AI, companies can leverage the power of artificial intelligence to predict revenue and business outcomes, all without writing a single line of code. This no-code platform provides business forecasting data that can be used to modify supply chain operations and create tailored marketing strategies.

Hypothesis 3: Staffing: Corporates will need new and the right skills to take benefit from AI

The width of use cases for AI, and the vast amount of information that AI can generate for corporate venturing, also contains the risk of misuse and getting lost in false details or even fabricated facts. Therefore we are convinced that senior knowledge with relevant experience will be crucial for the right training, usage and interpretation of AI in corporate venturing. Senior team members in a start-up or venture builder are trained to absorb big amounts of complex information and extract there the essentials. Experience and non-written knowledge are the key differentiators here. Therefore, we are convinced that higher skilled people become even more valuable for corporate venturing units.

A new category of jobs for corporate venture units and VC has already emerged: AI-related employees. These AI-related employees are usually trained data scientists who develop and use machine learning algorithms for investment screening and analysis. In a quantitative research paper published in November 2022 247 different job titles related to AI have been identified in the VC industry (Bonelli, 2022).

This variety of new job roles can be divided into three major groups:

  • Trainers who constantly improve algorithms by adding nuance to decision-making and interpretation
  • Explainers who bridge the technical gap between AI systems and business managers
  • Sustainers who will manage ethics and the ongoing management of the system

(Chalmers, MacKenzie, Carter, 2021).

Still, we are convinced that the final interpretation of the potential of an identified use case for a new business model will be done by humans. You can´t train AI with the necessary gut feeling experienced founders and investor have developed or as Jai Das the president of 5,7 billion heavy Sapphie Ventures formulates it: “I think the gut is never going to go away, but I think it’ll be much more driven by data and analysis than before. And you’ll have data to show that people who say I’m voting with my gut, either they’re right or not.” (Foy, 2021)


AI will fundamentally change how ventures are built, how ventures work, how start-ups are evaluated, and how investment decisions are made. Even though we are convinced that AI and ChatGPT have the potential for disrupting the field of corporate venture building massively, we are likewise convinced that AI will be mainly an additional add-on for corporate venture building. This service field's emotional and human components won’t make a difference in the short run. So AI-based technology has the potential to highly improve the work and decisions in venturing if rightly used.


written by
Georg Frick
Managing Partner