🔥Brutally Honest Truth About Transforming a VC Firm With AI & Automation
Learnings from 8 Years + What I Would Do Differently Today
👋 Hi, I’m Andre and welcome to my newsletter Data Driven VC which is all about becoming a better investor with Data & AI. ICYMI, check out some of our most read episodes:
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Following last week’s episode on why now is the best time to start leveraging AI for investing, I’d like to share my personal retro after 8 years of pushing AI & automation into VC and the honest truth about what I would do differently if I’d need to start all over again today. No fluff - promise.
Let’s jump in!
Don’t reinvent the wheel: Learn from peers!
One of the most surprising patterns I keep noticing is how many people try to solve a problem entirely on their own—despite the fact that it has already been solved before. Peer exchange? Community? Internet? Seems like untapped land for too many people. Specifically when it comes to tech for VC.
Over the past years, I’ve seen majority of VC firms operate in the dark. Because it’s “their secret sauce”. Sure, dream one. Your web scrapers, entity matching, and access to highly innovative sources like Crunchbase and Harmonic is really cutting edge.
In all honesty, 95% of what I see investors, product leads or engineers at VC firms trying to solve for has already been solved, and it’s really not a differentiator at all. The ones who have solved it, oftentimes openly speak about is as they know it’s far from secret sauce.
To foster peer learning and community exchange, I’ve had various thought leaders from our community share their learnings and what they’d do differently today. For example, we had Alex Patow from Inflection VC (and ex EQT) share his learnings of building tech with limited resources for a micro VC fund or Ali Almufti from BlackRock writing about forecasting for private market companies. We also had more than 100 speakers from firms like Accel, Atomico, Index, Moonfire, Seedcamp, and more share their learnings and behind-the-scenes at our virtual and physical summits & roundtables - most of them recorded and available here.
10 do’s and don’ts to bring AI & automation into VC
Condensing all the valuable learnings from our DDVC community and pairing it with my own journey across the last 8 years of transformation Earlybird VC with AI & automation (with significant resources and a dedicated engineering team), here’s the brutally honest truth, lessons learned, and how I would start if I’d need to do it all over again today - all packaged into 10 items:
1. Community: Join like-minded peers. You’re not alone.
When I embarked on my tech for VC journey in 2017, I felt lonely. Everyone told me “it’s not possible” and “others have tried” but nobody - except my partners - encouraged me to push for change. The industry was just not ready.
Fortunately, I trusted my instincts, similar to a few other lonely soles I met during my PhD and first years as an investor. We began connecting, exchanging our struggles, brainstorming, and contemplating the future of venture capital through one-on-one discussions. Gradually, these conversations evolved into small WhatsApp and Slack groups, which have been expanding steadily over time.
These groups still exist and are great, but it’s super fragmented and thus we decided to launch The Lab last year as a dedicated resource hub and community for like-minded peers. With 150+ members ranging from micro & solo GPs to early-stage, growth, and multi-stage firms, the community keeps growing fast and hopefully establishes itself as the central point to co-innovate when it comes to tech for private market investing.
2. Sponsors: Get top-down support from your GPs.
Once you surround yourself with like-minded peers, you will quickly understand where the industry is heading and what you can do to not miss the train. Once the direction is clear, you need buy-in from your GPs as every investment in tools and engineering HR will cut from their profits.
So better have a convincing story as the lack of top-down buy-in makes it a no-starter. Make sure you’re all on the same page. How much are you willing to spend? What are the expectations?
3. Focus: Define what you actually want.
Many funds don’t take the time to craft a plan and out of a perceived pressure rush into doing something for the sake of doing something. They start buying datasets and comparing CRMs without actually knowing what they want. Don’t be one of them.
Take a step back and define your firm’s strengths and weaknesses. What works well and what can be improved? Thereafter, define where you want to go. What is your north star? Once you know where you are and where you want to go, ask yourself on how you can close the gap.
While for some the north star is increased deal flow and coverage, it’s better prioritization and access for others. Define your goals and be critical about which initiatives contribute to your ultimate goal. Don’t get lost in the noise and just try to copy what others have done. Even worse, don’t be reactive to the masses of tool providers reaching out. Be proactive, do your home work, know what you want. Ignore the rest. Focus. Focus. Focus.
4. Metrics: Measure what matters to ensure you’re on track.
Once you’ve defined your north star and initiatives on how to close the gap, make sure to introduce metrics to track your progress. What is your North Star Metric? For example, if you decide you want to increase deal flow coverage, your metric should be something like hit rate or miss rate, as described in “Measure What Matters to Improve Coverage and Performance”.
Better prioritization? Measure funnel conversion rates. Better access? Measure outreach response rates. Better deal winning? Measure term sheet acceptance rate. Better portfolio value creation? Measure founder NPS. You get the point.
VC has long feedback cycles and we cannot afford to wait a decade before knowing whether our initiative is on track. Without the right operational metrics, you’ll be tapping into the dark and risk losing the trust of your internal sponsors, both from the investors/users and the GPs paying the party (see 2. above).
5. Owner: One person in charge to lead your initiatives.
Now that you know what is possible, have internal buy-in from your sponsors, budget, defined your goals, and know how to measure what matters, you can finally move towards implementation. If you’re reading this, the person in charge is most likely yourself. It was the same for me and between 2017 and 2020 I built the whole tech stack for myself. Product lead, developer, data wizz, and user all in one.
While this initially seemed like a bottleneck, it was the best that could happen to me. Firms that hired their first “techie” too early missed one critical aspect: The person in charge needs to be someone who understands both worlds - investment and tech. If this is not you, then hire someone hybrid who fills this requirement or alternatively hire an engineer and have her shadow the investment team for several months.
Do you actually need a dedicated person for your tech transformation and tool stack? Yes. 100%. Make versus buy with hundreds of tools available and hard to change workflows in place is just not possible to master part time. Trust me.
6. Hire: Beware of hiring deep experts too early; scrappiness and hustle > seniority and expertise in the beginning
This is a controversial one and I’m sure not everyone will agree but from my experience of having hired numerous freelancers and internal engineering profiles over the years, I’m convinced that it’s more about intrinsics than specific experience.
Digitizing an investment firm is in the first place about understanding the existing processes, defining the target state, and translating this into technical requirements. As fancy as all the AI and data stuff might sound, however, it requires more of a scrappy 80/20 hustler mentality than 100% deep and flawless expertise.
Find a Swiss army knife. Someone who has a couple of years of experience developing full-stack data applications. From collection over ingestion to processing and displaying. Someone with an entrepreneurial product mindset who has seen as much as possible. You don’t need this ivory tower NLP PhD with 5 A+ papers. Rather the opposite, you need someone who wants to get her hands dirty.
7. Make vs buy
First off, don’t look at the wrong role models.
It’s tempting to compare yourself with super advanced Data Driven VCs but the reality is that most of these firms - including ourselves at Earlybird VC - started the digiztation journey many years ahead and decided to build because buy was just not available. At least not to the functionality and quality we needed.
Today, the world looks different. Very different. Lots of tools are available off-the-shelf and it can be a great advantage to start with a white sheet of paper.
But how should you start? How can you balance speed and dependency?
In short, only if it’s available to buy and no longterm moat, go buy it. Otherwise, build and if it’s neither longterm moat nor really required for your north star, disregard it.
Once you know which components to buy and which ones to build, don’t start testing all available solutions yourself. Talk to the DDVC community and see what others use. It’s a good time to be a lemming here as crowd intelligence oftentimes drives you to the 2-3 most promising solutions. As part of The Lab, you’ll get full access to our beta version of VC Tool Finder, where you can anonymously compare your tool stack to firms with similar characteristics.
8. Push vs pull: Getting the right user culture
Assuming that buy vs build got resolved and you put together an MVP of your internal stack, it becomes all about user feedback, fast iteration, and change management. Sounds easy, but it isn’t.
On the highest level, there are three buckets of blockers: 1) lack of budget and top-down buy-in, 2) technical feasibility, which contains finding, selecting, and attracting the right engineering talent, being smart about make vs buy, and implementing properly, and lastly 3) the wrong culture and lack of bottom-up buy-in.
While 1) and 2) seem more critical than 3), I can tell that most firms have failed their digitization journey primarily due to 3). Changing the culture in VC, a rusty old-school cottage industry that has seen close to zero change since its inception in the 1950s, is hard. Very hard. I really can’t overstate how hard.
It’s surely easier if you launch a firm with data & AI in its core from day 1, like Signalfire or Moonfire, but unfortunately, the majority of firms out there already exist without this DNA ingrained. So how do you transform the culture of your firm to convert your biggest critics, i.e. the VPs and Principals who already hate to enter the notes into your CRM system, into raving fans of your data platform?
Well, unfortunately, I don’t have the silver bullet but I can assure you, you better find out early enough. On that note, I also heard from several other firms that the data leads launched their platforms too early (in the 0 to 1 manner) and their investment team members started to focus on the shortcomings instead of the wins, quickly losing trust and choking off the whole project.
In most cases, you’ll only have one shot. There is no way to replace your user group if the first attempt fails. As a result, I see two approaches:
Go narrow in scope: Define one or few workflows that you’d like to transform, define the north star metric and start iterating fast. Then expand from here.
Go broad in scope: If you plan to launch a more holistic platform all at once, then different to the startup playbook, it’s wise to take a bit longer before launching premature products to not lose trust and get it right with the first attempt.
In today’s fast-moving world of AI, I’d clearly recommend the first but if you’re already on track building a more holistic solution, take your time and prepare for a proper launch with clear expectations all around.
9. Start simple
It’s easy to get overwhelmed by the data & AI noise. Even more important to stay focused, not lose sight, and take one step at a time. Know what you want, focus, iterate fast, and finish. Don’t start too many things at the same time, but limit yourself and prioritize accordingly. It’s better to get a few things really right versus many things half-baken.
With modern AI tools, it can be fairly easy to create a wow moment with a ChatGPT generated investment memo or a simple Zapier or n8n automation flow that processes website inbounds end-to-end.
10. Create redundancy: No single point of failure
As your initiatives come to fruition, zoom out from time to time and look at the big picture. Are you still on track? What does your North Star metric tell? If all lights on green, adoption of your platform is growing, more deals are coming in, coverage is increasing, access is getting better, efficiency is up, and the engine overall is just running well, zoom out and look for the points of failure.
If done right, this initiative will become core for your business sooner than you expect. Even more important to stay paranoid and identify points of failure early on. Is your product lead still happy? Your engineer got another offer? Database hacked and no copy? The list goes on, so be aware as the importance increases, you should create redundancy. Across the team as much as the stack itself.
Conclusion
It’s easy to get overwhelmed. By the tools available, the noise made by other funds, and the feeling that your firm is the only one left behind, forcing you to participate in the window dressing show. Most importantly, you’re not alone. The industry is still at day 1 but be ensured that the next 2-3 years will be key to adopt modern tools and be not left behind.
If I’d need to start all over again, I’d join a community to get inspired by peers, analyze what works well and what doesn’t for our firm, craft a north star, discuss my plans with the GPs and budget owners, carve out significant time for the next 12 months or hire a dedicated product minded Swiss army knife profile to take ownership, decide what to buy and what to build, buy what other funds bought and don’t reinvent the wheel, start narrow in scope when building, and take my team on the journey with quick wins.
If you found this episode helpful, share it with others and consider joining 150+ peers in The Lab to accelerate your digitization journey with hundreds of resources from VCSTACK.COM and beyond.
Stay driven,
Andre
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Good piece, but I think there’s a blind spot. “Build if it’s a moat” sounds nice, but in practice only a handful of mega-funds can afford to do that. Most firms end up on the other side of the advice: buying more SaaS tools and adding to the Frankenstack. What’s missing here is the third path: agentic systems that execute workflows directly, collapsing the need for stitching tools together. That feels like the bigger change ahead...