Industry Insight

Why Every VC Will Have an AI Analyst by 2026

Kruncher | June 10, 2025

The venture capital industry is at an inflection point. While VCs have been busy funding AI startups, AI companies captured a record 46.4% of total US VC funding in 2024¹, with global AI venture capital reaching $110 billion in 2024, marking a 62% year-on-year growth². But most firms are still running their own operations like it's still 2015.

By 2026, having an AI analyst will be as fundamental to VC operations as having a CRM or email system today. It’s not speculation, but an inevitable evolution driven by three converging forces that LPs need to understand as they evaluate fund managers.

The Data Explosion is Overwhelming Human Capacity

The numbers tell a stark story. Investment in AI companies drove over 70% of all VC activity in Q1 2025³, while deal volumes continue climbing. Today's VCs are drowning in information that would have been unimaginable just 5 years ago.

Consider what modern fund managers must process: thousands of inbound deals annually, 20+ data sources per company analysis, real-time monitoring of 15-50 portfolio companies, quarterly LP reporting across multiple investments, and constant market intelligence gathering. The surge in data sources has made managing information overwhelming.

Human analysts, no matter how talented, cannot scale to match this data velocity. The cognitive load has simply exceeded what traditional research methods can handle effectively.

Early Adopters Are Already Gaining Measurable Advantages

The competitive gap between AI-enabled and traditional funds is widening rapidly. Data-driven firms increased by 20% from 2023 to 2024⁴, and these early movers are seeing concrete benefits.

Firms using AI-powered deal screening can process 10x more opportunities with the same team size. Portfolio monitoring that once required dedicated analyst hours now happens automatically, with alerts only for material changes. Due diligence timelines have compressed from weeks to days for initial assessments.

Notably, Andreessen Horowitz launched a $1.5 billion AI-focused fund⁵, underscoring just how central artificial intelligence has become in venture strategy.

To say that AI investing is the game changer is only partially correct. It's in AI-powered operations.

One partner at a European fund reported their AI tools helped them identify three portfolio companies showing early distress signals that they would have missed entirely with manual monitoring. Another fund reduced their initial deal screening time by 80% while improving investment decision quality.

Technology Has Reached an Adoption Tipping Point

The barriers that previously made AI adoption prohibitive for most funds have largely disappeared. The democratization of AI tools makes it easier for firms of all size and stage to adopt AI across their deal workflow.

Unlike 5 years ago, when implementing AI required significant technical expertise and infrastructure investment, today's solutions are purpose-built for VC workflows. Modern AI analysts can be deployed in minutes, not months, with configurations tailored to specific investment theses and fund mandates.

The global venture capital management software market is projected to grow from $0.9 billion in 2023 to $2.5 billion by 2032, exhibiting a CAGR of 12.2%⁶. This growth reflects not just demand, but technological maturity.

The AI tools available today are mature, production-ready solutions handling real fund operations. Kruncher, for instance, now serves over 20 investment firms with automated portfolio monitoring and deal analysis.

Economic Pressures Are Accelerating Adoption

Fund economics are making AI adoption a necessity rather than a luxury. The demand-supply ratio for VC funding peaked at 3.5x in 2023⁷, meaning intense competition for quality deals. LPs are demanding better returns while funds face pressure to do more with leaner teams.

Close to two-thirds of venture capital fund managers said they expect exits to increase in the next 12 months⁸, but that optimism comes with higher expectations for operational efficiency and investment performance.

Traditional fund models relied on expanding analyst teams to handle growing deal flow. In today's environment, that approach is neither financially sustainable nor competitively viable. Intelligent automation, enabled by AI, provides the only realistic path to scaling analysis capacity without proportional cost increases.

Industry Transformation is Inevitable

History shows us how quickly essential business tools become universal once they prove value. Email, CRM systems, and financial software all followed similar adoption curves: early adopters gained advantages, competitive pressure accelerated adoption, and within 3-5 years these tools became baseline requirements.

AI analysts are following the same pattern, but what took a decade for previous technology shifts is happening in 24-36 months for AI.

78% of organizations now use AI in at least one business function, up from 55% just a year earlier⁹. While this data covers all sectors, VC firms who live at the intersection of technology and capital are adopting even faster.

What This Means for LPs

For limited partners evaluating fund managers, AI capability is becoming a predictive indicator of future performance. Funds that haven't adopted AI-powered operations by 2026 will face structural disadvantages in deal sourcing, evaluation speed, portfolio monitoring, and operational efficiency.

The smart money is in evaluating how sophisticated their AI integration has become. Can they process larger deal volumes? Do they spot portfolio risks earlier? Are their investment decisions backed by broader data analysis?

These represent fundamental shifts in how successful funds will operate.

By 2026, having an AI analyst will be table stakes for serious fund management. If you’re not in, you’re left behind.

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