Solving Problems with AI: Contract Management

Posted on March 2, 2020 by Brad Blickstein

Perhaps the fastest-growing category in AI-powered legal technology is contract management, which really breaks down to two areas: pre-execution and post-execution. Pre-execution tools help companies through the contract negotiation process, for example comparing documents to playbooks devised by the legal team. Post-execution tools, which are sometimes referred to as contract life cycle management, work on a company’s stockpile of contracts, typically helping extract and organize the information that’s in them.

While both functions are related to contracts, which are the lifeblood of business, the process and technology are quite different, as is how the tools get used.

Contract Management: Pre-Execution

Many companies struggle to review incoming contracts, be they sales contracts, NDAs, procurement contracts or something else. These incoming agreements can often create a backlog for the legal function, resulting in frustration by the business units and often even a delay in revenue recognition for the corporation. It’s how so many law departments have become “the revenue delay team” or “the department of no.”

AI-powered contract pre-execution tools can solve this problem. First administrators and professionals set up what is effectively a playbook of approved contract language. Some of these tools do this by helping attorneys actually write a playbook; some do it by having attorneys review a sample set of incoming contracts and learning from it; and some do it by analyzing a large group of already approved contracts and learning that way. Others actually come “pre-built” based on industry best practices.

Then incoming contracts are ingested and analyzed, typically using natural language processing, or NLP, and compared to the playbook by the tool. Problematic clauses are marked, sometimes with approved language suggested. These tools allow law departments to review many more incoming contracts in much less time with the assistance of law firms or ALSPs. Some companies are starting to deploy these tools as a self-service model, allowing businesspeople outside legal to agree to contracts with no legal review if the text comes back through the system clean.

The pre-execution contract management tools covered in the Legal AI Efficacy Report include BlackBoiler, LawGeex, LegalSifter, Litera Contract Companion and ThoughtRiver.

Post-Execution Contract Management

While pre-execution tools tend to be deployed by the law department itself, post-execution contract management tools tend to be the domain of law firms and alternative legal service providers.

These tools review large amounts of already existing legacy contracts and structure the data culled. Typical use cases are:

  • Large-scale review for macroeconomic reasons, such as CCPA or the end of the LIBOR protocol.
  • Large-scale review for business reasons, such as identifying contracts with change-in-control provisions as part of a due diligence project for a merger or acquisition.
  • Ongoing business reasons, such as organizing when real estate leases end or automatically renew.

Once analyzed, these systems organize the contracts in a single repository and structure the data so that users can easily find the ones with the clauses on which they have been tested. Some tools create extremely useful visualizations that help users understand their corpus of existing contracts.

The post-execution contract management tools covered in the Legal AI Efficacy Report include eBrevia, HighQ, iManage RAVN, Kira Systems and Seal Software.

Remember, AI is just technology, and so all the existing strategies used to develop or evaluate technology solutions still apply. Your goal should be to cut through the cool features the vendors want to talk about and focus on where exactly AI adds value to solving your legal and business problems.


Brad Blickstein is principal at Blickstein Group and partner and co-head of NewLaw at Baretz+Brunelle. More information about his Legal AI Efficacy Report can be found at


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