This guide is designed for contract managers, procurement teams, legal professionals, compliance officers, sourcing leaders, and business executives evaluating the next generation of contract management technology. This article explains the practical differences between traditional CLM platforms and CLM AI agents, and how the latter can modernize existing contract processes and reduce contract risk and administrative burden.
There is no denying it: even traditional CLM technology fundamentally changed contract management for the better. Centralized repositories have made searching for and retrieving contracts infinitely easier. Workflow automation has resulted in quicker contract lifecycles and better contract outcomes. Reporting features have increased contract visibility and analytics.
However, today's organizations need more.
Higher contract volume and increased contract complexity make it so tedious or repetitive processes can quickly add up and become overwhelming. More compliance pressure results in less room for error. Increasing operational complexity makes it harder to solve nuanced problems fast.
Enter CLM AI agents.
Instead of simply storing contracts, AI agents help users work with contracts more efficiently by assisting with analysis, extraction, review, and decision support. Let's explore these agents further.
Again, it is hard to understate how traditional, non-AI-based CLM has reinvigorated the way professionals manage contracts. Contract storage, workflow routing, alerts and notifications, reporting, and electronic signatures cut contract cycle times, decrease risk, increase revenue, and promote accountability.
However, there are several blindspots that come with traditional CLM - in that manual work still exists in:
We now inhabit an era in which contract AI can assist contract teams to such a meaningful degree that manual tasks essentially become a thing of the past. Instead of "What does this contract say?" the AI agents help answer "What should I pay attention to in this contract?" Users can analyze the "meat" of contracts, identify clauses, spotlight risk, extract obligations, and easily search through and about contracts beyond extraction - including with an AI chatbot with nearly limitless potential for drilling down into contracts and their implications.
For a deeper look at how AI agents are evolving from simple automation tools into intelligent contract assistants, explore the whitepaper: "How Agentic AI Transforms Contract Management: From Automation to Autonomous Action":
Traditional CLM, despite all of its revolutionary enhancements to all things "contracts," still requires some level of manual legal review cycles.
Conversely, an approach with CLM AI agents can eliminate the need for performing myriad aspects of contract review unassisted.
Clause detection and playbook comparison allow contract managers to quickly spot clauses that either are or are not aligned with pre-approved language standards and strategic playbooks. Risk-flagging brings risky language and aspects of a contract to the front of contract managers' attention. Users can also leverage suggested improvements to have contracts generally more prepared for execution.
With obligation management using traditional CLM, users still need to manually manage, to some extent, deliverables, milestones, and reporting requirements.
However, this is not the case with CLM AI agents.
AI goes far beyond just extracting obligations (which it does). It also engages in subsequent task creation based upon those extracted obligations. It helps surface deadlines, supports compliance monitoring, and deploys workflow agents to fulfill obligations.
To better understand where obligation management fits within the broader contract lifecycle, download the whitepaper: "The 8 Critical Stages of Contract Management."
In traditional CLM, users search for contract numbers, vendors, keywords, and more - which can take up a lot of time that would be better spent strategically utilizing legal experise.
With contract intelligence, meanwhile, users can engage an AI chatbot - asking questions such as:
This marriage of helpful (pre-existing) reporting functionality and complex querying about the enterprise-wide contract database can markedly increase visibility, actionable insights, and more.
Looking to unlock greater value from contract data? Explore the whitepaper: "6 Contract Data Analytics Tools to Boost Contract Oversight" to learn how AI-powered analytics can help organizations identify trends, risks, and opportunities faster.
Even traditional CLM requires periodic, somewhat manual audits and assessments. These can bog down resources and expose organizations to undue risk.
AI-assisted risk analysis, meanwhile, helps users to proactively clock non-standard clauses, missing provisions, and high-risk language while providing contract sentiment analysis from the organization, their counterparty, and from a neutral perspective.
While CLM AI agents are undoubtedly extremely powerful and remove the tedious work of contract management - and even some more strategic aspects of CLM - human expertise still matters. AI assists, but human legal resources validate, approve, and govern.
Traditional CLM systems helped organizations digitize contract processes. CLM AI agents represent the next evolution by helping users analyze contracts, identify risks, enforce standards, and better manage obligations.
It is not fully automomous contract management.
Rather, it is human-guided contract management enhanced by intelligent AI agents.
Book a free demo of CobbleStone to experience CLM AI agents and more today. It's free, and risk-free.
*Legal Disclaimer: This article is not legal advice. The content of this article is for general informational and educational purposes only. The information on this website may not present the most up-to-date legal information. Readers should contact their attorney for legal advice regarding any particular legal matter.