Corporate Associate
Run due diligence on a target company's material contracts
What You Do Today
Review hundreds of contracts in a virtual data room, flag change-of-control provisions, assignment restrictions, consent requirements, and unusual termination triggers. Summarize findings in a diligence memo.
AI That Applies
Contract analysis AI reads entire data rooms, extracts key provisions across standard categories, flags anomalies, and generates structured diligence summaries with clause-level citations.
Technologies
How It Works
The system ingests entire data rooms as its primary data source. NLP models process the text input by identifying entities, classifying intent, and extracting the structured information needed for downstream decisions. The output — structured diligence summaries with clause-level citations — surfaces in the existing workflow where the practitioner can review and act on it.
What Changes
The first-pass review that consumed weeks of associate time is compressed to hours. AI surfaces the 15% of contracts needing careful human review.
What Stays
You still assess materiality, determine which flagged provisions are actually deal risks, negotiate indemnity language, and advise the client on whether to proceed.
What To Do Next
This section won't tell you what your numbers should be. It will show you how to find them yourself. Every instruction below produces a real, verifiable result in your organization. No benchmarks, no projections — just the steps to build your own evidence.
Establish Your Baseline
Know where you are before you move
Before adopting AI tools for run due diligence on a target company's material contracts, understand your current state.
Without a baseline, you can't measure whether AI actually improved anything. You'll adopt tools without knowing if they're working.
Define Your Measures
What to track and how to calculate it
Time per cycle
How to calculate
Measure how long run due diligence on a target company's material contracts takes end-to-end today, then after AI adoption.
Why it matters
The most visible improvement is speed. If AI doesn't save time, question whether it's adding value.
Quality of output
How to calculate
Track error rates, rework frequency, or stakeholder satisfaction scores before and after.
Why it matters
Speed without quality is just faster mistakes. Measure both.
Start These Conversations
Who to talk to and what to ask
your VP Operations or COO
“What data do we already have that could improve how we handle run due diligence on a target company's material contracts?”
They're prioritizing which operational processes to automate
your process improvement or lean lead
“Who on our team has the deepest experience with run due diligence on a target company's material contracts, and what tools are they already using?”
They understand the workflow dependencies that AI tools need to respect
a frontline supervisor
“If we brought in AI tools for run due diligence on a target company's material contracts, what would we measure before and after to know it actually helped?”
They see the daily reality that AI tools need to fit into
Check Your Prerequisites
Confirm readiness before you invest
Check items as you confirm them.