The 2010s saw the first great advances in contract AI. Natural language processing (NLP) and latent semantic indexing (LSI) were applied to identify key contract fields and enable search and data mining across thousands of agreements. Two providers in particular, Seal Software and Kira, gained notable market share among legal departments and law firms, distinguished by the depth and breadth of their capabilities. 

For teams prepared to invest heavily in implementation and professional-services-led customization, early contract AI solutions offered substantial benefits. However, that became a central theme in the story: deep investment of time, effort, and (often) budget were needed to achieve and maintain a strong return. This dynamic held as a company’s contract analysis needs changed over time: new regulations, fluctuating market conditions, and disruptive events (like pandemics and international conflict) demonstrated how valuable contract data can be for strategic business initiatives — if you can extend your AI models quickly enough to tap into it. 

Today, many of the leading contract AI players of the 2010’s have been acquired by larger entities, often with the stated goal of integrating them into other solutions. Those plans may require legacy systems to re-architect their AI and underlying platforms, which can mean long term roadmap planning and investment for their parent companies. For their customers, that can mean patience and (eventually) high migration or re-implementation costs. 

Meanwhile, those same early adopters are seeing a time of rapid advancement in AI technology, coinciding with growing demands of their in-house legal teams. Counsel are now routinely expected both to know what’s in their company’s contracts and to systematically use that knowledge to make their contracting processes more efficient with less risk. 

This juxtaposition has led many early adopters of contract analytics to make a change to later-generation solutions in search of better, faster ROI. As others consider making the same move, they are looking first at the advantages available to them.

So, what exactly has changed in the 2020s with contract AI? Quite a lot: 

Ease of deployment: Unlike with many legacy tools, it is now possible to onboard an enterprise’s entire corpus of contracts in mere days – without experiencing processing delays or extensive professional services-led migration, changing existing folder structures, or disrupting access to contracts in CRM, ERP, and other existing systems.

Scalability: Secure, economical, multi-tenant hosting is now readily available for contract AI data, along with high-capacity OCR and AI processing to ingest large volumes of legacy contracts quickly and accurately.

Self-service: Enterprises no longer need to retain third parties or build a center of excellence with specialists to fine-tune contract AI to track the terms they’re interested in. Training custom AI models has been simplified to a code-free, drag-and-drop experience anyone can perform without limitations – and with a high degree of accuracy.

Generative AI: Large language models (LLMs) have joined NLP and LSI, offering new, rapidly-evolving use cases. Capabilities like automated redlining and clause creation can streamline contract preparation and negotiation, while simultaneously improving consistency and control.

Full CLM Integration: Far from the fragmented tech stack of days past, an AI-native contract lifecycle management system delivers unified results across all contracts at all stages, enabling advances like automated clause library creation and allowing pre-signature workflows to be continuously informed by lessons learned from post-signature analysis. 

Connectivity: AI-extracted contract data can be delivered to hundreds of enterprise systems spanning numerous departments, whether via direct integrations, intervening tools like Workato, Tray, and Zapier, or (where needed) custom development around RESTful APIs.

With so many advances available, early adopters now have to choose how best to optimize and future-proof their investment in contract AI. This requires evaluating their current providers’ commitment to and capacity for future innovation, and weighing that against both the benefits and costs of changing to a later generation system. 

Fortunately, as the time and cost required to implement contract AI have become lower and lower, so have the barriers to changing systems. With that trend well established, the path ahead for adopters of contract analytics becomes clearer and clearer.

Want to see what Evisort’s AI can do for your agreement processes? Let Evisort set up a proof of concept with your contracts — all of them, if you’d like — so you can experience true contract intelligence firsthand. Request a proof of concept today.

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