Why does template chaos erupt after a merger, and what risks does it create?

Each company brings its own template libraries, clause variants, and approval paths built over years. Conflicts across liability, payment terms, and governance create compliance gaps, value leakage, and missed obligations. The pressure to show progress in about 100 days amplifies risk if templates are not harmonized quickly.

How fast can AI standardize templates compared with manual methods?

AI-driven CLM ingests thousands of legacy contracts, classifies clauses, and surfaces deviations in days or weeks, not months. Sirion resources indicate drafting time reductions of up to 80% when teams author from a governed clause and template library.

What are the core steps to harmonize templates post merger?

Ingest and classify legacy contracts to map template families and clause variants. Build a unified clause and template library with approved language and fallbacks, then layer governance and version control to prevent drift. Establish an Integration Management Office to own standards, track deviations, and sustain adoption beyond Day 100.

How should we evaluate CLM platforms for post-merger harmonization?

Prioritize scalability, AI sophistication, analytics, and deep governance features over basic storage. Use analyst evaluations such as the Forrester Wave, Gartner Magic Quadrant, and IDC MarketScape to benchmark capabilities, and verify the platform can handle cross-business and cross-region complexity.

Is there proof that AI CLM can scale to very large portfolios?

IBM announced plans to deploy Sirion CLM with IBM watsonx to manage complex, global contracting at scale. The solution is cited as managing over 5 million contracts worth more than $450 billion across 70+ countries, demonstrating the need for automation at enterprise scale.