Assigning deal sourcing to junior team members or separate biz dev teams is a critical flaw. CEOs and business owners are less likely to engage with non-decision-makers, meaning firms miss out on the best opportunities which require top-level engagement.
The common 'let me pick your brain' approach offers no value to the target. Successful relationship-building starts by giving something valuable, like a well-researched thesis on their industry, which demonstrates credibility and provides mutual benefit.
A common and embarrassing fumble in private equity is having a junior employee cold-call a target who already has a strong, active relationship with a senior partner at the firm. This signals a lack of internal coordination and damages the firm's reputation.
AI is not making CRMs obsolete. Instead, it's adding an intelligent action layer on top of the system of record. This layer uses AI agents to automate workflows like sourcing and deal qualification, transforming the CRM into an active, strategic partner.
Unlike VCs who map the entire human landscape of potential founders, PE firms often focus only on active processes. PE should aim to build a trusted relationship with the owner of every single company in their target universe, long before a transaction is contemplated.
The biggest misuse of new technology is simply increasing the volume of automated, low-quality outreach. The true value lies in using AI to handle tedious tasks, freeing up professionals to be more human, present, and build deeper, trust-based connections.
VC sourcing relies on individual partners' networks to connect with founders. In contrast, PE often uses a hierarchical team (associates, VPs) to manage relationships with intermediaries like bankers, focusing on a smaller number of high-conviction deals.
Affinity CEO Ken Fine recounts choosing a lower-valuation investment from a firm he'd built a relationship with over 18 months. The trust and familiarity created a lower-risk, more attractive offer than a higher bid from an unknown firm.
Old CRMs required users to edit information before entry, creating friction and data loss. The new paradigm is to automatically ingest all raw data (emails, notes) into a 'data lake,' with an AI layer then extracting insights and eliminating manual work.