Lessons Learned From Digitization Journeys at Atomico, Balderton, Earlybird, Inflection, and More
Insights from "Venture Intelligence Day" Part I of II
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Few weeks ago, I gave a keynote speech about āAI in VC: Outlook for 2025ā at Vestberryās Venture Intelligence Day in Prague. For those of you who couldnāt join, I shared my slides here. Shortly after publishing them via the newsletter, I received several DMs asking for key insights from the other conference sessions.
Thankfully, our friends at Vestberry agreed to make the exclusive recordings available for our DDVC community via our platform āThe Labā. You can get 40% discount on your membership today and access all Venture Intelligence Day recordings here (together with tons of other resources such as the recordings of our Virtual Data-Driven VC Summit 2024)
For those of you wanting the key takeaways of the respective sessions, Iām happy to write this episode and share brief summaries including select video snippets from the different talks. Given the density of information, I split it up into two episodes. Today is part I of II.
Itās for everyone interested in the state of digitization in venture capital, and how data & AI change the game end-to-end across the value chain, from sourcing over screening to portfolio value creation, fundraising, LP and investor relations, to back office functions, and more.
10 Key Takeaways (Part I of II)
āPortfolio intelligence is a missed opportunity for VCsā
āStrong need for a single source of truthā
āJust as it is now impossible to envision working without spreadsheets, soon, you will find it unthinkable to make decisions without structured dataā
āImportance of setting clear data expectations with portfolio companies early onā
āAt later stages with fewer opportunities and higher investment volumes, white glove service and personal interactions remain important. At earlier stages with more deal volume and smaller investment volumes, however, automating interactions is the only way to goā
āImportance of starting with SaaS solutions and refining these before building proprietary toolsā
āItās key to create a data-driven culture through systematic training and transparency, fostering an environment where data supports every layer of decision-making across investment, fund administration, and LP relationsā
āItās a big challenge to align technical and investment teamsā
āWhen asked about building vs. buying data systems, all agreed that a hybrid approach works bestā
āWhile tools can be bought, the core system should be built to reflect each fundās unique strategy and workflowā
Session Snapshots (Part I of II)
#1 Future of Portfolio Intelligence
Marek started his talk with a strong statement: āPortfolio intelligence is a missed opportunity for VCsā. He emphasized that while much time and energy go into deal sourcing during a fundās initial phase, the ongoing management of investments over the following 4 to 10 years is equally critical yet often lacks a data-driven approach.
Hereof, Marek outlined the fundamental āshift from merely collecting data to truly leveraging it for value creation within portfolio companies.ā He highlighted the āstrong need for a single source of truthā for portfolio metrics and performance data, noting that ā60% of companies fail because of cofounder issues,ā making it essential to identify and act on early warning signals.
With this data, VCs can ādouble down onā the right companies and support them strategically. This approach, Marek said, enables VCs to not only manage risks but also actively assist portfolio companies through market insights, strategic partnerships, and hiring.
āJust as it is now impossible to envision working without spreadsheets, soon, you will find it unthinkable to make decisions without structured dataā he said, envisioning a future where data and AI become core strengths that set VCs apart.