Migrating from paper and ad-hoc processes into a modern, AI-enhanced Contract Lifecycle Management platform is a big undertaking. This article provides a step-by-step migration pattern, practical tips, and a sample roadmap to make that transition predictable and valuable.
Typical starting challenges
- Scattered documents across email, file shares, and local drives.
- Lack of standard templates and inconsistent clause language.
- No central metadata or actionable audit trails.
High-level migration roadmap
- Discovery: Inventory contract repositories, stakeholders, and common contract types.
- Prioritization: Identify high-value contract classes (e.g., NDAs, vendor contracts) to migrate first.
- Extraction: Digitize paper documents and extract metadata using OCR + AI document understanding.
- Normalization: Standardize parties, clause names, and metadata taxonomy.
- Platform selection & pilot: Choose CLM, run a pilot with 1–2 contract classes and refine templates/workflows.
- Scale & govern: Expand to additional contract types, enforce clause libraries, and train users.
How AI assists the migration
- Document classification: Automatically categorize contract types during ingestion.
- Clause extraction: Identify termination, indemnity, and payment clauses with high accuracy for metadata tagging.
- Risk scoring: Flag high-risk clauses to prioritize legal review.
Change management essentials
- Start with stakeholders who will gain the most (procurement, finance).
- Provide short training modules focused on common tasks (search, create, route).
- Keep legal in the loop for playbook and clause governance to prevent reversion to old habits.
Sample 6-month pilot plan
- Weeks 1–4: Discovery and tooling proof of concept.
- Weeks 5–8: Pilot ingestion and classification for top contract type.
- Weeks 9–16: Template and workflow configuration; integrate with eSignature.
- Weeks 17–24: Expand to second contract class, measure ROI (time saved, risk reduced).
KPIs to track
- Time-to-execute (request → fully signed).
- Manual interventions avoided per month.
- Percent of contracts with structured metadata.
- Number of high-risk clauses flagged vs resolved.
Conclusion
Moving from paper to AI-enhanced CLM is a staged journey. Start small, use AI for the heavy lifting of extraction and tagging, and expand once processes and governance prove their value.