Here’s a concise guide to contract lifecycle management (CLM) systems that include meaningful AI features, what those AI features do, and how to pick one that fits your needs.

Top AI-enabled CLM vendors (good starting shortlist)

• Workday / Evisort (Evisort’s AI integrated into Workday Contract Intelligence / CLM): AI-native contract extraction, clause identification, obligation tracking, AI Q&A and automated redlines. Good if you want an enterprise-grade, AI-first engine combined with HR/finance integrations. (investor.workday.com)
• Ironclad: AI suggestions and extraction across repositories, custom AI properties you can train, automated redlines and clause tagging to speed negotiation and reporting. Well-regarded for legal/operations collaboration. (support.ironcladapp.com)
• Icertis (Icertis Contract Intelligence): focuses on “contract intelligence” — converting clauses to structured data, risk scoring, clause libraries and pre-signature/ post-signature automation and analytics at scale. Often used by very large enterprises. (icertis.com)
• Conga (Conga Contract AI + Conga CLM): AI extraction, playbook-based redlines, suggested language, compliance checks and custom model creation; positions itself for revenue/commerce integrations (CPQ, billing). Good for organizations that want CLM tied to revenue ops. (conga.com)
• DocuSign (Intelligent Agreement Management / Lexion tech): e‑signature leader expanding into AI contract understanding, Q&A, faster review/automation after acquiring AI agreement tech. Strong if you already use DocuSign for signatures and want tighter agreement analytics/workflow. (docusign.com)

What AI in modern CLM typically does

• Extracts structured data (parties, dates, amounts, notice periods, auto-renewals) from legacy and scanned contracts so obligations and milestones are tracked automatically. (investor.workday.com)
• Summarizes key terms and creates natural‑language answers to “what does this contract say about X?” (AI Q&A). (investor.workday.com)
• Suggests redlines and alternative clause language based on your playbook or historical wins, speeding negotiations and standardization. (support.ironcladapp.com)
• Flags risk and compliance issues, scores clauses, and surfaces revenue/ cost opportunities hidden across the portfolio. (investor.workday.com)

How to choose (practical checklist)

• Use case fit: prioritize extraction/obligation tracking, drafting/redlining, analytics, or a mixed need. Different vendors excel at different stages. (investor.workday.com)
• Data & model control: confirm whether AI runs on vendor-hosted LLMs, your private models, or on-prem; ask about the ability to opt out of external LLMs and to train/customize on your data. (Critical for IP/privacy.) (conga.com)
• Integrations: ensure connectors to Salesforce/ERP/HR systems, e-signature tools, and finance systems you use. (investor.workday.com)
• Accuracy & tuning: validate extraction accuracy on a sample of your contracts (POC). Check whether the vendor provides custom labeling/training or human-in-the-loop correction. (support.ironcladapp.com)
• Security & compliance: request SOC2/ISO27001, encryption-at-rest/in-transit, data residency guarantees, and contract-model governance docs. (conga.com)
• Implementation footprint & ROI: estimate time to ingest legacy contracts, set up templates/playbooks, and user training. Many vendors offer pilots or staged rollouts to show ROI quickly. (investor.workday.com)

Common deployment/pricing notes

• Pricing models vary (per user, per contract processed, tiered CLM suites). Enterprise AI features often cost extra or are included only in higher tiers—ask for total cost of ownership (data migration, training, integrations). (investor.workday.com)

Implementation best practices (to get useful AI results fast)

• Start with a focused POC: 500–2,000 representative contracts, define 8–12 extraction fields and review accuracy. (support.ironcladapp.com)
• Establish a clause/playbook library and governance (who approves AI suggested redlines). (support.ironcladapp.com)
• Use human-in-the-loop to correct the model early — that improves accuracy quickly. (support.ironcladapp.com)

Risks and mitigations

• Incorrect extractions/overreliance on AI: always keep human review for high‑risk clauses. (icertis.com)
• Data privacy/regulatory issues: insist on contractual protections and the ability to opt-out of model reuse/sharing. (conga.com)

Short, practical next steps you can take now (no vendor contact required)

• Pick 2–3 vendors above that match your scale (Workday/Evisort, Icertis, Ironclad, Conga, DocuSign) and run side-by-side POCs on the same contract sample (same fields, same evaluation metric). (investor.workday.com)
• During POC measure: extraction precision/recall, SLA for new-contract turnaround time, and the number of manual reviews saved per month. (support.ironcladapp.com)

If you want a tighter recommendation, tell me which matters most (enterprise vs. SMB, volume of legacy contracts, must-have integrations like Salesforce/Workday, required data residency) and I’ll map the best 1–2 choices and the exact POC fields to test.