Many highly successful industries — private equity, talent acquisition, corporate strategy, or media, to name a few — share a few fundamental challenges: Reliance on intuition, networks, and experience for decision-making, long feedback cycles, and difficulty quantifying decision impact. 

There’s another somewhat secretive industry that shares these challenges yet has pioneered ways to circumvent them using data and AI: Early-stage capital.

Sid Rajgarhia, Investment Insights Lead, is leading this charge at First Round Capital. If your industry operates with the dynamics described here, this playbook outlines how VC’s approach to data can provide practical inspiration for refining your strategies and improving outcomes.

Challenge 1: Limited feedback

Many executives make several high-stakes decisions a quarter and rarely get feedback about whether they are good or not. VC firms face a similar challenge. Historically, they have only received decision feedback on the few companies they have actually funded because of the technical challenges of tracking the entire universe of companies they considered. This limitation hampers their learning opportunities.

Solution: Adapt by learning from every opportunity, not just successes.

Instead of only tracking portfolio companies, Rajgarhia has built a data system that enables First Round Capital to track data on every company they met, creating feedback loops from the 99% of startups they met but passed on to identify overall patterns and refine their decision-making over time. “The data architecture allows us to learn from everything we evaluate, not just the choices we make,” says Rajgarhia.

Organizations in every industry should implement a structured process for tracking and reviewing past decisions — whether it’s unselected job candidates, acquisition targets that weren’t pursued, or rejected strategic initiatives. Use this data to study the universe of decisions you’ve made, not just the ones that are easily observable. (e.g., That executive we did not hire, what did she go on to do?)

Challenge 2: Over-reliance on experience 

Industries like consulting, executive search, and entertainment often rely on subjective pattern recognition, where experienced professionals make decisions based on past encounters. However, individual memory and anecdotal insights have limitations. Furthermore, if you operate on a team, your pattern recognition does not extend beyond what you’ve seen to what your teammates have seen.

Solution: Scan for systematic patterns to enable smarter comparisons.

First Round Capital is using AI-driven semantic search tools that scan historical deal flow across the firm to analyze similarities and surface insights from that cohort. This allows firms to compare potential investments to previous cases using a vastly augmented and more structured approach, previously impossible. “AI doesn’t replace our human expertise,” says Rajgarhia. “It helps surface insights that would otherwise go unnoticed.”

Similarly, businesses in any sector should investigate how to implement AI-powered tools to find similar opportunities/ideas trapped in their enterprise databases. For example, an entertainment company might locate similar scripts that the studio may have seen before to determine their potential. Or a business could identify similar corporate M&A targets that have been previously evaluated. This structured method can improve your ability to identify hidden patterns, but also reduce individual bias in decision-making.

Challenge 3: Subjective, unstructured decision-making processes

In many industries, key decisions — such as executive hiring, investment approvals, or strategic pivots — cannot be entirely scientific because there is a lot that is unknowable at the time of the decision. These decisions are therefore driven by gut instinct.

Solution: Enable executive-level decision support for greater objectivity.

Rajgarhia’s system has reinvented how investment decisions are debated and decided upon by integrating AI models that help structure the discussion around key deal considerations and areas of disagreement between investors. The model looks at the criteria for making a new investment decision, information about the startup in question, and partners’ thoughts on the investment. It also finds areas where investment committees disagree and organizes discussions around important deal factors.

This approach represents a significant and novel step forward relative to the industry — bringing data and technology to bear in an otherwise intuition-driven process driving the deployment of hundreds of millions of dollars each year.

Any business today should be using AI-driven decision-support tools to guide the discussion to elevate or surface topics in high-stakes decision-making meetings. AI can also highlight where past decisions deviated from expected outcomes, ensuring greater accountability and more fact-based discussions.

Final Thoughts: Applying VC’s Data-Driven Mindset to Your Industry

Shifting from an intuition-driven approach to a data-augmented model doesn’t mean losing human judgment — it means enhancing it. Whether you work in executive hiring, private equity, corporate innovation, or media, these strategies can help you:

• Capture broader data sets to learn from decisions beyond just the ones you made

• Leverage AI-powered tools to surface patterns and insights faster

• Develop predictive indicators to shorten feedback loops

• Structure decision-making frameworks to reduce bias and improve outcomes

By incorporating these transformational lessons from the world of venture capital, your organization can start making even better decisions by chipping away at the age-old frictions in your industry that were once considered insurmountable.

Rajgarhia acknowledges that AI is a partner, not yet a replacement. “Using data strategically in our environment doesn’t supplant human expertise — it amplifies it.”

About the Author:

Douglas Laney is a thought leader, consultant, advisor, author, speaker, and instructor on data and analytics strategy topics. He is the originator of the field of Infonomics and the author of the best-selling book, “Infonomics: How to Monetize, Manage, and Measure Information for Competitive Advantage.“