AI hasn’t just transformed the VC investment landscape, it’s also quietly redefining how investors operate.
Funds are using AI to find and track targets, as well as build up internal conviction. Some have built prototypes that tag and analyse deal flow automatically, while others track portfolio signals and generate deal memos in minutes.
AI is moving from advantage to necessity. Soon, not using it won’t just mean falling behind, it will mean becoming irrelevant.
Venture has always been powered by networks, intuition, and conviction - the ability to read people, timing, and markets before the data does. That hasn’t disappeared. But what’s changing is the scale and speed of context investors must absorb to stay ahead.
Today, data moves faster than any network, and the signal is buried beneath mountains of noise. The edge no longer lies in having access (everyone does!), but in how quickly and intelligently that information is turned into conviction.
Yet here’s the paradox: in a world where fewer winners win bigger, relying purely on data can be just as dangerous as ignoring it. AI can widen your lens, but it can’t replace the instinct that recognises the outlier.
The funds that thrive won’t abandon intuition for data: they’ll weaponise both. They’ll use AI to expand their field of vision, and human judgment to decide when to make the bet.
Because in modern venture, context is infinite, but conviction is still scarce. Decisiveness matters now more than ever.
Among investors, the use of AI is still in its early days:
But the real danger isn’t skepticism, it’s stagnation. Staying in “experimentation mode” while others operationalise AI will waste time, resources, and opportunity.
When tools like Harvey and Legora emerged in the legal sector, ChatGPT was still new to that industry. Its reliability uncertain, security unproven, and its grasp of domain context limited. At that time, few legal teams were openly experimenting with generic AI tools; the risks around confidentiality and compliance were too great.
That environment created the perfect opening for trusted, purpose-built legal AI platforms. They offered what general models couldn’t: guardrails, context awareness, and integration with the workflows lawyers already lived in. And just as importantly, the legal profession itself was an ideal proving ground for large language models: a field defined by endless reading, writing, and interpretation of documents, mapping neatly onto what LLMs do best: process language at scale.
Legal professionals were never going to build their own “hacky” AI tools, so specialised systems filled the gap.
Venture capital, however, operates differently. Many investors are experimenting with LLM chats, custom pipelines, and internal copilots. Which means the bar for a vertical-specific AI solution in VC is much higher.
To justify its place, a purpose-built VC platform must do more than repackage what’s possible with off-the-shelf models. It must unlock a new use case, surface insights impossible to reach with generic tools, or streamline the investment workflow so dramatically that it becomes indispensable.
What’s the impact from all of this and why should you care? Venture deals move faster than ever and are accelerating - deals that once took weeks close in days. The constraint isn’t capital, it’s how fast you can find, understand and act.
To keep up, investors must find their startups earlier than ever, stay close to the ones they see potential in, and make conviction calls before consensus forms.
AI is the only way to stay truly alert - collecting and then connecting scattered information, tracking live opportunities, and surfacing what matters when it matters.
Because in today’s market, fewer winners win bigger, and the difference between catching one or missing it often comes down to a single thing: having the guts to make a call earlier than others.
The writer of this article, Heini Salonen (co-founder of Savantiq), has had one-on-one discussions with more than 100 VCs across Europe and beyond. Through these conversations, she’s seen firsthand how differently funds are approaching AI: some cautiously testing, others already building. This experience has made her one of the most active voices in understanding how AI will reshape the investment process and she’s always happy to continue the conversation with anyone exploring what’s next for their fund.
Author: Heini Salonen
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