AI isn't just transforming the companies VC firms invest in, it's reshaping how venture capital itself operates.

It’s reflected in almost every conversation I have with firms these days. In fact, the lion's share of my consulting work has re-focused on helping venture firms with their strategy, approach, and implementation of AI tools and data efforts.

The question is no longer whether data and AI belong in your firm's platform strategy but rather how your firm can leverage AI to gain an edge in sourcing, diligence, portfolio management, internal operations, and, most importantly, fund returns.

An important caveat: I deeply believe that venture capital will always remain a fundamentally "artisanal" business. All the AI tools in the world won't change the essence of the VC model: super-smart people making calculated, often high-risk decisions with high conviction in pursuit of outsized returns. Yet, the right data and tools are establishing the modern era of "augmented VC", a term coined by Dr. Andre Retterath,  Partner at Earlybird Venture who also publishes the super insightful newsletter "Data-Driven VC" (you should subscribe, if don’t already!). His perspective is that data and AI stand to complement human intuition rather than replace it.

My goal with this newsletter is to layout my framework for establishing AI as a superpower in your fund. Specifically, I'll cover:

• The data + AI equation and building your firm’s data foundation

• Five specific AI use cases delivering results for venture firms today

• The build vs. buy decision for AI tooling

• How to resource the AI opportunity at your firm

Let's dig in!

Yes, Data + AI Fall Into the Platform Function

Many years ago, I was talking with a head of platform who told me, "Cory, I have become irreplaceable at my firm — they’re never going to fire me." When I asked why, he said, "Because I'm effectively the firm's Salesforce admin, and no one else knows how to manage it."

To this day, I always think of this conversation, and it has reminded me of the role that platform has always played in leading, designing, and managing the tooling that enables firms to operate effectively. Today, that role remains the same, the tools have simply evolved from a basic CRM to significantly more powerful (and more complicated) systems.

And that is where data and AI fall: they are simply the newest and perhaps most alpha-generating tools in the VC arsenal. Given platform’s traditional role in managing firm-wide tools and systems, platform remains to be the natural owner for evaluating, implementing, and managing AI capabilities that directly enhance fund performance.  Now, the challenge is for firms to bring on the right platform professionals with the right skill sets and experience to lead these teams (such as data science, product and engineering), or for existing platform leaders to do some turbo-learning and get up to speed on this rapidly evolving need.

Data + AI = Superpowers for Venture

So, why should venture funds lean into the role of AI? The reality is that this is about unlocking potential and providing your team the power to do more with less. This does not mean buying ChatGPT Pro subscriptions for all investors and calling it a day; it requires a whole lot more than that.

Effective AI implementation demands a careful and thoughtful approach to assembling the right data and the right tools with a cohesive strategy focused on achieving specific objectives. 

Let's break that down:

1. Start with the data

Without clean, high-quality, and relevant data, AI is like an engine without fuel - powerful in design but unable to perform.

Firms that are gaining speed from the pack are those that treat data as a core asset rather than an afterthought. They're systematically capturing, organizing, and enriching their data with rigor.

To build genuinely useful AI applications that move the needle for your fund, a strong data foundation is a non-negotiable. 

2. Find or build the right AI tools.

The tools you choose are how you make the data you gather usable. Whether it's buying off-the-shelf solutions, customizing with integrations to your stack, or building entirely custom in-house capabilities, this is where your fund’s edge is created.

Never lose sight of the fact that the purpose of data and AI is to deliver tangible value. Many firms implement AI tools that look impressive but deliver marginal value. The winners deploy targeted solutions that solve specific problems that investment teams face daily.

3. Unlock superpowers.

The outcome of this equation - the right Data + the right AI tools = Superpowers - is the real leverage that unlocks transformative capabilities for your firm. 

These powers include spotting patterns no human would catch, reaching companies earlier and with more conviction, making faster and better decisions with less friction, and scaling your platform's impact tenfold without a corresponding increase in headcount. 

Not all superheroes can fly the very first day they get their capes, so don’t expect to roll out tools and have said superpowers on day 1. Success is as much dependent on your approach to behavioral change management with your team as it is on the technology itself. For this to work, your fund needs to thoughtfully invest in implementation, team training, and ongoing support to ensure an understanding of what superpowers are available and how best to use them.

When implemented properly, this combination doesn't just incrementally improve your processes, it fundamentally reshapes what your team can accomplish. 

The firms that get this right will inevitably be more agile, faster, and more dynamic than their competitors.

Organizing Your Data and Infrastructure

Data is the gold that powers your AI initiatives, but here is where most funds run into their first challenge. The reality is that most firms don't have all the data they need, or what they have is poorly organized, making the work ahead substantial.

From identity resolution to data cleanup, deduplication, and security, getting your data warehouse in order is complex. Despite these challenges, clean data is the most defensible and powerful asset, if activated well. The funds that successfully build unique datasets create a competitive advantage that compounds over time.

The question is: Where do all these data sets come from? In general, there are three categories of data available to firms.

The first category is purchased third-party data. With most funds buying and using the same datasets – company information, traction metrics, and team insights – the data itself is a bit more of a commodity these days, but the differentiation lies in how you decide to interpret and utilize the data. How you weigh different signals like headcount fluctuations, website traction patterns, or founder demographics is where the proprietary magic gets unlocked. Here, you can apply bespoke insights to deliver intelligence at scale with standardized commercially available datasets.

What's exciting today is the expanded data universe now available to venture funds beyond what's commercially available. This leads us to the second category, what I call "dynamic data." This is where firms can design custom data schemas and use third-party tools, web scrapers, and inference prompts to structure previously unstructurable information. 

This approach augments commercial datasets with data uniquely aligned to your investment hypotheses. While still utilizing publicly accessible information, you're transforming it into formats no vendor could offer off the shelf. You might analyze founders' writing patterns across platforms, track customer sentiment through reviews, or develop novel approaches to evaluating product-market fit using signals from specialized forums.

The third category is proprietary data; the data that lives inside your firm. What previously was buried in inboxes and cloud folders is now valuable input to your data arsenal. This includes your internal company financials, research reports, meeting notes, historical analyses, and other information your competitors can't access, giving you a significant advantage when evaluating opportunities.

Ultimately, building a proprietary approach to data is like bringing a gun to a knife fight. The funds that invest here first will build moats that become increasingly difficult for competitors to cross as their data advantage compounds.

Five Critical AI Use Cases in Venture

With your data foundation in place, the next question becomes: How do you transform this raw material into actionable capabilities that drive returns? 

There are nearly an infinite set of ways that firms are implementing AI tools, and even more ways that firms are fantasizing the short-term and long-term potential of the opportunity. To bring a bit of structure to an overwhelming set of applications, I’ve developed a simplified (albeit incomplete) framework of the use cases for AI in Venture: The VC AI Tooling 5x5.

This model outlines five functional areas of a VC firm where AI tools can have an outsized impact and five distinct types of jobs these tools can do within each. Altogether, 25 use cases that, when implemented correctly, drive operational leverage, sharper decisions, and ultimately contribute to stronger returns.

If you find this framework slightly overwhelming, don’t worry – that’s by design. As with many other frameworks I’ve introduced, the purpose of this is NOT to recommend that your firm have coverage of all 25 items.  Rather, the opposite.  

The best way to utilize this framework is to review each of these 25 items independently to determine which will be high impact and important for your firm and which are simply not relevant.  If you walk away feeling like there are 3 or 4 items within the framework that you want to attack, then it has done its job.  

Remember, you can’t do it all, and by this time next year, the framework will likely turn into a 5 x 50, so start small and focus on where you can really find leverage.

Now, let’s look at each of these five functions (Sourcing, Diligence, Internal Operations, Portfolio Management, and Network Activation) and explore the five ways AI is poised to deliver value in each. 

Note: If you're short on time, feel free to skim the next section; it's valuable but detailed and can be bookmarked as a reference for future use.

1. Sourcing

Sourcing has, historically, been one of the hardest and most time-consuming activities in venture. 

Whether your fund’s approach relies on high-velocity cold outreach to CEOs or laser-targeted thesis-driven engagements, sourcing is always hard. It is one area where AI can create truly meaningful leverage for investors. 

With the right tools, your firm’s sourcing efforts are no longer dependent on how many coffee meetings you can take in a week but rather on how well-trained your team of AI agents is to augment your effort and provide you with the right information to find the right companies at the right time.

• Thesis and Trend Development relies on AI tools to help investors learn about markets, conduct research, track trends, and form opinions faster and more comprehensively than ever possible. This involves aggregating large swaths of insights (think podcasts, newsletters, news articles, social media insights, analyst reports, academic papers, you name it) and transforming initial ideas or queries into fully formed theses around promising investment areas and market directions.

• Company Discovery provides universal search capabilities to find and unlock companies that are building products or innovating in markets based on any query you can imagine. This makes possible what Google never could - it’s not focused on helping you find who has the best SEO, but who serves the customer you have in mind with the product you’ve been dreaming of. This discovery isn’t a one-time thing; today, you can seed a search Agent with a query of interest and have it notify you every time it finds a compelling new market entrant or an established player pivoting into your area of intrigue.

• Signal Tracking surfaces data and insights to find what you’re looking for. This is a matter of tracking quantifiable signals using data we previously discussed (headcount growth, personal movement, web traffic traction, github repo growth, press mentions, funding announcements) and using these signals to proactively help you find the notable moments of inflection (and deflection) to know when companies are trending and ensure you get in front of them at the right time.

• Company Scoring requires understanding what you’re looking for in a company based on your firm’s investment thesis and fit and training a data model to provide a rating score based on a company’s quality, fit, or potential alongside your firm’s requirements.

• Founder Discovery is relevant primarily for early-stage funds that are looking to track people before they even start companies. For example, to see when a brilliant PhD student at a university research lab will graduate and start a company, to find out when a talented technical leader at a marquis company will go out on their own, to track company inception the day a domain name is registered or an incorporation filing is made, or to track what a “stealth mode startup” is actually doing before a founder officially comes out of stealth. The idea is to uncover gems before other firms do by monitoring and tracking great people that you believe will build great companies.

2. Investing

Investing remains the art of venture and one area where humans will always be in charge, but it is also the part of venture most critical to get right. This is precisely why AI can be a valuable support - helping speed up decisions, providing deeper insights and analysis, and supporting the investment team in reaching the most informed decision possible. 

AI can operate at a scale and level of comprehensiveness that humans simply couldn't accomplish alone. When implemented well, AI won't just help you move faster, it will help you move smarter, surface insights that might otherwise be missed, and establish a higher baseline quality for every investment decision.

• Deck Review tools allow you to screen inbound pitch decks at a faster pace to provide instant analysis and determine if a company may be of interest to your fund or not. You can have these tools automatically input opportunities into your CRM, summarize deals, pull out general insights, recommend questions on where to dive deeper, flag risks, and prepare you to meet with founders with more context.

• Financial, Legal and Data Room Analysis is where you can get real leverage by leaning on AI tools to perform analysis in minutes that previously could have taken days without missing any context. Such tools can organize P&Ls, financial models, cap tables, legal documents, contracts, disclosures (you name it) to detect red flags and surface other important details. Beyond organization, they can predict runway, generate growth projections, assist with valuations, review contracts and commercial agreements, highlight potential concerns, and identify areas requiring deeper diligence investigation.

• Founder Profiling will help to challenge or confirm your perspectives on a company’s founders and leadership teams. These tools can analyze broad sets of data around people - such as their social media content, articles on Medium or blogs, interviews, speaking engagements, pretty much anything available online as well as meeting transcripts and any other input you can get access to. The result is a far superior analysis and understanding of a person's communication style, behavioral traits, and personality type to highlight strengths and surface areas of concern.

• Competitive Benchmarking compares companies you’re interested in with their peers, both incumbents and emerging players, to understand the market dynamics, identify competitors, provide insights on a company’s strong points, and highlight the potential risks. These tools can enable a better picture of a company’s competition, market position, comparative traction, and overall potential opportunity.

• Memo Generation & Drafting helps to synthesize diligence notes, data, and all other insights gathered by your team to expedite the process of drafting memos that your investment team can edit and polish. This results in getting deals in front of your IC quicker and with a more comprehensive perspective to inform decision-making.

3. Internal Operations

Let’s be honest, the internal operations of your average VC firm is often held together by duct tape, an irreplaceable EA (or 2), and a chain of hacked together systems that are one Zapier bug away from imploding. To make matters worse, for most funds, the idea of a single shared knowledge repository that you can turn to as a reliable source of truth and context is a far-off and unrealistic pipedream.  

But AI can change that. With the right tools, you can now stitch together disparate, incomplete, unorganized, and, at times, incomprehensive notes, documents, and systems to create a shared knowledge base with well-functioning systems that help your small team operate like a much more sophisticated and efficient one. 

Done right, AI makes your entire team sharper and more unified and saves a hell of a lot of time and headaches in the process. Unlike the other four categories, many of the solutions in this category are not unique to venture. The trick here is to find tools that are flexible enough to understand the unique context of venture so that they work for your specific needs. 

For example, implementing an AI writing assistant that’s trained to help BDRs send sales emails won’t work well in crafting the highly personalized, thoughtful, and detailed outreach emails that VCs aim to send.

• Call Recording & Summaries are tools we all know - they join your Zoom meeting (or come as cringe-worthy wearables for in-person interactions) to capture, transcribe, and summarize conversations. They automatically push structured, searchable notes to your team's systems, preserving meeting details while recommending next steps and highlighting key takeaways

• CRM Maintenance & Data Hygiene has always been the bane of every organization’s existence - especially VC firms. But the beauty of AI is that it now provides powerful tools that can help to automatically and very intelligently update your CRM data, ensuring you have accurate and complete information on contacts and accounts, input notes, track relationship insights, and more.

• Firm Knowledge Base potentially provides one of the biggest areas of opportunity for a venture firm, or any organization for that matter.  By securely connecting AI tools to your internal systems and proprietary information - files, notes, and databases - you create a firm-wide superbrain. Questions like "What company did X?" or "What percentage of Company Y do we own?" or "Who was the founding CTO of Company Z?" can be answered instantly. Today, these questions might require frustrating time spent trying to remember, searching through files without success, or asking team members only to discover that no one recalls the answer.

• Meeting Briefs serve as a very useful tool for your team. Your average daily schedule likely looks like a block of back-to-back meetings with little time to breathe, let alone thoroughly review your team’s notes and do proper research to ensure you come into a meeting with any stakeholder well prepared. These tools can use all of your firm’s internal information alongside what can be found on the internet to give you a simple brief with guidance, background information, and suggestions on topics to cover and the right questions to ask.

• Writing Assistants become your magic weapon when they can start understanding your voice and use that pattern recognition to help you draft everything from emails to memos to LinkedIn posts and even text messages to your significant other (well… maybe not that one). The idea is to give yourself real leverage so the blank screen problem disappears. You'll spend time crafting and editing messages rather than struggling with where to start, turning routine emails into a quick skim-and-send operation.

4. Portfolio Management

The reality of portfolio management is that in between the strategic conversation and major inflection changing breakthroughs is a whole lot of mundane work and blind guessing.  

This is another area in which, if done right, AI can flip the dynamic and make you not only a more informed investor but also a more valuable board member to your portfolio companies. 

Rather than reacting to fires or waiting for updates, firms can maintain a live pulse on company health, performance metrics, and early signals of risk or opportunity, all without needing to exhaust companies with constant requests for information.

• KPI Monitoring can pull live metrics directly from your portfolio companies’ financial and HR systems to provide you with far more detailed and centralized insights on how companies are performing. Tools today help not only bring in that data but also help to standardize it, compare it to benchmarks and expectations, and raise red flags when needed, such as if burn is increasing faster than expected. This can also be paired with tools that can ingest company email updates, board packs, and decks and keep everything in one place, minimizing the need to chase CFOs with data requests.

• Sentiment & Signal Analysis proactively scans signals across the web like hiring velocity, product feedback and ratings, employee reviews and LinkedIn activity, competitor announcement, and more to assess a company’s momentum and help raise visibility of any risks or concerns far before hearing about them in your next board meeting.

• Modeling & Capital Planning enables teams to run company projections and scenario projections at a scale and velocity that simply is unthinkable even by the best investment bankers. AI tools can help investors to project dilution scenarios, play out thousands of returns models, dynamically recommend reserve strategies, and much more. These provide real power on the company and fund level to provide more insights on what your ultimate returns may look like and how to best construct your fund to optimize outcomes.

• Board Intelligence is, in my opinion, one of the most exciting potential opportunities for AI tooling. Imagine if rather than just having one investor from the firm on each company's board, you have that one investor and an entire team of AI Agents who have a perfect memory, decades of insights and perspective in the blink of an eye and who are an expert at every single function required to build and exit a company. The concept involves continuously feeding all of your collective intelligence on a portfolio company into an AI model - from initial diligence documents to board minutes, financials, decks, customer references, employee exit interviews, call transcripts, and anything available online. This gives you all the necessary information in a single query. Picture sending next quarter's board deck to your AI “team” the morning of the meeting and receiving, within minutes, a comprehensive list of questions, concerns, highlights, and recommendations that make the meeting more productive and efficient, eliminating the need to rehash familiar topics.

• LP Reporting tools focus on making the mundane, repetitive work of compiling, standardizing, and formatting quarterly updates far less time-consuming and more accurate. AI tools can remove friction for your team by making reports feel almost self-prepared, while also assisting with the more creative aspects - coupling data with relevant insights and portfolio narratives in a consistent, compliant, and straightforward way. 

5. Network Activation

Of all the categories of value add that venture firms can offer to their portfolio, a firm’s network is perhaps the most valuable of all – yet it is also the MOST challenging to manage, search, and activate. 

Your network is simultaneously your diligence expertise, your talent pipeline, your advisory team, your customer target, your brand amplifier, your founder pool, your investment syndicate, your liquidity enablers, and so much more. 

Too often, firms rely on memory, spreadsheets, and gut feel to figure out who in their network to activate and when. I’ve been waiting for nearly a decade to see this change, and it feels like we are finally edging towards AI doing just that. These tools can transform your network from a stale database into a searchable, context-rich knowledge base, one that enables you to understand every contact in your CRM on a far deeper level than a manual tagging system or procured dataset ever could. 

For platform teams, this is possibly one of the most exciting unlocks, but the power and implication of this new superpower are far more widespread than just your platform efforts.

• Relationship Mapping and Querying ensure that your team has a single unified place where your firm’s relationship knowledge lives. By feeding all of your data into a single system with context across your emails, calendar, LinkedIn connections, and (when possible) your WhatsApp and text messages, you have a single map to provide the source of truth of who you know. That then gets overlaid with enrichment and intelligence that today’s AI tools are starting to build muscle for (though candidly, I haven’t found any tool yet that is as smart as I’d want it to be here… but I’m confident in 12 months time that will change). Once we have the right tools and the right data, you should be able to say, “Who do we know that is an expert in X to help with diligence?” or “Who in our network would have worked with Y to provide a reference?”.

• Talent & Advisor Matching tools not only help find the best people in your network when companies request hiring assistance but also work proactively. Imagine a system that automatically checks your portfolio companies' job boards weekly, maps their priority job openings against your firm's network, and transforms the process of facilitating targeted candidate introductions into something nearly automatic and painless.

• Customer Introduction tools align your portfolio’s ICPs with your firm’s network to help surface high-value intros to the right people at the right companies to close deals. Some tools directly connect to your portfolio company's CRM, matching their top opportunities with your firm's relationships. Others can identify potential targets based on product offerings, enabling focused and high-value introduction recommendations.

• Event Invites & Planning aims to solve the age-old grueling process of saying “who should we invite to this event”? As simple of a question as it sounds, it's actually quite hard to manually figure out which founders in X industry you should invite to a curated dinner in Y city. The goal for tools here is to help surface information from your firm’s network to recommend attendees and support the whole planning process, from drafting invitations to optimizing seating arrangements based on mutual interest to engineer serendipity at scale.

• Investor Mapping provides immediate value to portfolio companies when they’re trying to generate a list of which investors they should target and reach out to for their next fundraise. The idea of these tools is to help your portfolio go deeper and know not only what firms are good fits for their next round, but what specific individuals based on stage, industry interest, and even account for conflict of interest checks - and then make intro path recommendations with the knowledge and insights of your firm’s current relationship map.

As I warned you, the last 25 bullets of information were probably both exciting and overwhelming to digest. Remember, this isn’t a laundry list of what to do – it’s a structured menu of where you may want to consider deploying AI tools within your firm, and hopefully one that also helps you identify areas that you certainly don’t need to focus on.  

One other thing to note, not every one of the 25 use cases explained above exists in their full potential today. Some are anecdotes of tools I know of that are being actively built in a basement in Brooklyn as we speak; others are abstractions of current products with my dream set of new solutions and features to make them even better and more useful. Whether these all exist right now or not matters less since I am confident that in the next few weeks or months, every one of these will be better than they are today and continue to revolutionize the work that venture firms are currently doing.

The next question to conquer is: Where will you start?

The Tools Behind the 5x5

Now that we've reviewed these 25 use cases, you're likely thinking… that’s swell, but what products can I actually use to implement these strategies? You're in luck. I've put together a dynamic market map to accompany this post, listing over 130 (yes, you read that right) tools and data providers currently delivering excellent solutions for each of these use cases.

This landscape will constantly evolve, and I'll do my best to keep the map updated over time. If you know of tools I've missed, please reach out so I can add them to the market map.

With so many options available, it’s vital to determine which of these existing solutions could work for your fund and which you should attempt to build custom. Which brings us to our next topic… 

Build vs. Buy

Many VCs overestimate which tools actually require custom development and may end up wasting precious engineering and data science resources recreating solutions that already exist. Additionally, technology is advancing at a breakneck pace, so even if you can’t find the perfect tool for what you’re looking to do today, it might be more efficient to wait for the right tool to come to market than to build it yourself.

Let's be clear: VC funds are in the investment business, not the product business. Getting distracted by building elaborate tools diverts focus from your core competency.

First, identify what capabilities are genuinely mission-critical to your firm's competitive edge. Ask yourself how truly special and proprietary these needs are - if it's something no one else would conceive of or no existing tool could be trained to handle, like a proprietary deal scoring algorithm or voting agent, it might warrant in-house development. 

For everything else, thoroughly investigate off-the-shelf solutions. Many sophisticated platforms can be configured to your specific requirements. And of course, work closely with your legal counsel or GC to review the terms of service and privacy policies of any platform you decide to use to ensure that they are not selling your data or exposing your secret sauce (or sensitive portfolio data) in a compromising way. This disciplined approach allows you to move faster in a more agile way and ensures that you remain focused on what truly differentiates your fund.

How to Resource this Opportunity

The power of AI and the introduction of new data unlock an opportunity in venture that was previously impossible. 

This will transform the VC industry landscape dramatically in just a few years, creating unprecedented value for forward-thinking funds. The critical factor, however, is how you resource this initiative and ensure you have the right leadership and execution muscle at your firm.

While your team may be brilliant, having your junior associate act as your part-time AI hacker simply won't cut it (it's kind of like assuming a member of your team who did a 3-month internship in Meta’s talent team is equipped to be your firm’s senior talent partner).

This opportunity is incredibly strategic and significant, warranting a professional who understands these complex tools and can bring a product mindset to make them successful at your firm. And yes, they'll be expensive, but the ROI will justify the investment.

This doesn't necessarily mean building a team of 8-10+ data scientists and engineers (though firms like Sequoia, Lightspeed, SignalFire, and EQT have already done that). For many funds, a single head of product or senior data scientist might be sufficient. In fact, I can report that engineering and data science roles are the fastest growing and most in-demand positions across venture right now, outpacing hiring for any single platform function or even investment role.

What I am seeing is that forward-thinking firms are treating their platform teams as their product teams, with senior strategic platform leaders serving as the fund's Chief Product Officer.

Whatever your approach, you need someone on your platform team owning, leading, setting, and executing your AI strategy.

The Future of AI-Enabled VC Platform

Remember, the firms gaining an edge today aren't just adopting exciting new AI tools; they're developing comprehensive strategies for how data and AI augment their core investment and platform functions.

This is a make-or-break moment for venture platform teams. The decisions you make now will determine your competitive position for years to come. Those who invest strategically in data and AI capabilities today will create an advantage that competitors may find impossible to overcome tomorrow.

This post is brought to you with the help of Yaffa Abadi of Abadi Brands, a premier personal branding firm for leading executives and VCs.