Key Takeaways

  • Winsupply oversees acquisitions across 680+ wholesale distributor locations with a corporate M&A team of four people, sustaining an 80%+ deal success rate.
  • Greg Holbrock applies internal audit discipline to dealmaking, pushing for continuous monitoring across an acquisition universe rather than static point-in-time document reviews.
  • Kison Patel rejects the use of AI to draft legal agreements, observing that machine learning models miss the defensive angles that seasoned deal counsel catches.
  • Centralizing diligence files into a queryable data platform eliminates segregated silos and preserves findings for post-close operations.

Deal Diligence Requires Continuous Monitoring

Greg Holbrock approaches corporate development through the discipline of an internal auditor. That background dictates how Winsupply screens targets and digests operational records. Holbrock runs acquisitions across 680+ wholesale distributor locations with a core team of four people, maintaining a transaction success rate above 80%. When deal teams run lean at that operating volume, unverified diligence findings compound fast.

Most buy-side teams treat diligence as an isolated sprint that concludes at signing. Holbrock argues that corporate dealmakers need an operating framework borrowed from corporate governance. “In the world of audit, you have an audit universe,” (play from 1:21:56) Holbrock explains. “You have continuous monitoring. That is the device and that is the mechanism that needs to be in place in M&A. What is the M&A universe continuous monitoring?” (play from 1:21:59)

Treating an acquisition pipeline as an ongoing universe changes how firms evaluate documents. Diligence tools that ingest seller files need to construct a permanent verification trail that follows the business into integration, well beyond static committee presentations.

AI Fails at Drafting Acquisition Contracts

While autonomous agents can extract facts from bulk disclosures, drafting definitive purchase agreements remains well beyond their capabilities. Kison Patel points out that deal teams deploy agents on cloud platforms across various departments to automate internal tasks. Deal execution, however, presents strict boundaries. When evaluating whether buyers should rely on algorithmic tools to generate transaction paperwork, Patel gives a definitive answer: “Would you rely on the AI to draft a legal? 100% no.” (play from 1:05:56)

Patel tested automated drafting against real transaction terms and watched the software stumble over nuance. While algorithms can organize boilerplate provisions, they lack the defensive instincts earned from broken transactions and litigation. “When you have counsel look through it, they have such a different lens,” (play from 1:06:26) Patel noted. Human lawyers anticipate how an aggressive seller might manipulate an indemnity threshold years down the road. Language models optimize for statistical probability, not legal risk.

Centralized Repositories Beat Siloed Deal Files

Centralizing fragmented records provides immediate value for buy-side platforms, keeping diligence findings accessible across the entire firm. Corporate acquirers often scatter target information across external legal drives, local spreadsheets, and email chains. When field operators assume management duties after closing, valuable discovery data disappears.

Holbrock views data consolidation as the primary friction point software must address. “I think the biggest value prop is you don't have data segregated in multiple areas,” (play from 1:21:05) Holbrock says. “You have a system from which you can query and ping and ask questions of.” (play from 1:21:10) A unified platform allows lean corporate development teams to pull verified answers on demand without waiting days for junior staff to comb through thousands of PDF pages.

Holbrock balances this mechanical speed with an auditor's skepticism. “The big thing with audit is how do you trust and verify,” (play from 1:00:54) Holbrock emphasizes, noting that teams must determine “how do we trust but verify all the information that's getting thrown out there.” (play from 1:01:08) Machine agents accelerate search, but deal teams must confirm the underlying evidence.

Why It Matters

Lean corporate development teams are using data aggregation platforms to review larger pipelines without expanding corporate headcount. This separation of workflow intake from legal judgment signals that private market buyers view automated tools as diligence search engines rather than automated decision-makers. As corporate buyers demand tighter verification before closing, platforms that centralize records and verify claims will outlast tools that attempt to replace human deal counsel.