Contract lifecycle: author from templates and clause libraries, negotiate and redline, route for approval and signature, capture the executed contract with its metadata, then track obligations, monitor performance against terms, and analyze the portfolio for risk and savings.
The 7 tasks — the nature of each, and the oversight it needs
Tag each task in plain terms — what kind of work it is and how hands-off it can run — before any mention of AI. The kind of work is what later decides which tool, if any, fits.
A language-heavy process. The binding step is reading and understanding the contract — extracting clauses, obligations, and deviations from documents the counterparty often drafts. Get the language understood and tracking, monitoring, and analysis follow; drafting and portfolio analysis are generative, and negotiation stays human.
Contracts carry legal, compliance, and financial exposure — AI drafts and extracts, but redline, signature, and risk calls stay with a person.
The question isn’t only “is there savings” — it’s can I run this better: cheaper, faster, higher quality, better service? Here’s what best-in-class looks like, and how teams get there. (How much of it AI specifically drives — and how proven that is — is Section 04.)
Process discipline first, then automation — AI is one slice of the second column, not the whole answer.
- Clause libraries & standard templates
- Obligation & renewal calendars
- Standardized terms & fallback positions
- Contract playbooks
- GenAI drafting & redline
- NLP clause extraction
- Obligation / renewal alerts
- Portfolio risk & savings analysis
“Who runs the work” is its own question, separate from AI. AI shows up across these options — sometimes heavily, sometimes not at all. Vendor-neutral; the real options mapped to PR03.
Recall the tasks and their nature from Section 01. AI is one lever, not the whole story — the mix below is simply the result of matching the right kind of solution to each kind of work, weighted by where the work concentrates.
Generative AI leads (~40%) because contract work is reading, comparing, and summarizing legal language, extracting clauses, and flagging deviations — a natural-language-understanding task. Document AI captures the executed contract; NLP supports clause identification. (Deloitte State of AI 2025, McKinsey State of AI 2025.)
The right lever fits your volume, variability, control needs, and appetite to operate a system. Start here.
The autonomy question: agent or copilot?
Whichever delivery model you pick, one choice cuts across them — who presses enter.
AI agent
Runs the steps end-to-end, completes the clean cases on its own, and routes only the exceptions to a person.
AI copilot
Sits beside the person and speeds up each step; the human acts on every decision.
What to evaluate — whichever you choose
- Accuracy on your own inputs — vendor benchmarks are on clean data; test your messiest cases.
- Straight-through / touchless rate — the real efficiency number, not “AI-powered.”
- Exception-handling experience — most of your team's time goes here, not the happy path.
- ERP write-back & integration depth — does it post cleanly to your system of record?
- Data residency — does data leave your environment, and is that acceptable to compliance?
- The accountability surface — what happens, and who owns it, when the model is confidently wrong?
See every lever across your processes
Run your portfolio through the assessment — work profile, improvement potential, confidence, and executor options across all your blocks, scored against 127 enterprise subprocesses.
Open the AI Value Assessment →