Global HCM leader migrates 700K contracts in six months with 65% reduction in manual effort using AI

UKG, a global multi-billion dollar Human Capital Management company, faced a mandate to migrate 700,000 legacy contracts into a modern Contract Lifecycle Management system within six months — a timeline that left no room for multi-pass processing or iterative cleanup. In-house legal operations teams in large enterprises routinely inherit contract repositories built over decades with no consistent governance: inconsistent naming conventions including DBA and FKA variations, duplicate documents with subtle redlines, near-zero metadata, and incomplete contract families spanning parent-child relationships that were never formally tracked. Off-the-shelf automation tools failed under the scale and structural disorder, leaving the legal team exposed to missed obligations, unenforceable terms, and delayed CLM go-live.

UKG engaged Elevate to deploy its AI-powered Enterprise Legal Management platform combined with the Contracts Insights solution — blending natural language processing automation with expert-led contextual review in a single quality-controlled workflow. NLP drove metadata enrichment and parent-child hierarchy mapping in one pass, eliminating the multi-stage processing bottlenecks typical of large-scale migrations. The system used semantic document clustering to group contracts for batch review and applied built-in OCR to handle scanned files seamlessly. AI pre-screening automatically isolated duplicates, unsigned drafts, incomplete contract families, and out-of-scope documents, retaining only those meeting defined accuracy thresholds for human review. Real-time dashboards merged daily processing logs into a unified project view, enabling data-driven decisions on pace and quality throughout the engagement.

All 700,000 contracts were fully migrated within the six-month deadline. Key outcomes:

• >99% accuracy across all extracted metadata fields
• ~65% reduction in manual effort compared to a traditional review-and-tag workflow
• 100% completeness validation achieved across all contract families, including parent-child relationships
• Real-time dashboard provided continuous visibility into processing volume, error rates, and review queue status
• Predictable, AI-optimised pricing enabled cost control across the full migration scope

The combination of automated pre-screening and semantic clustering dramatically narrowed the manual review surface, making a six-month timeline operationally viable at this scale.

• Combining metadata enrichment and hierarchy mapping in a single processing pass is essential at 700K+ contract volumes — multi-stage pipelines introduce compounding errors and timeline risk.
• AI pre-screening that filters out duplicates, drafts, and out-of-scope documents before human review is the primary lever for reducing manual effort at scale.
• Accurate parent-child hierarchy reconstruction requires NLP analysis of party names, dates, and naming conventions — not just file structure or folder paths.
• A real-time unified dashboard merging daily processing logs is operationally necessary, not optional, when migration pace and quality must be managed simultaneously.
• Legacy contract repositories in large enterprises are structurally messier than anticipated — build classification and deduplication into the earliest pipeline stage.