AI is shaking up enterprise contract management across all business functions. From procurement and legal to HR and finance, AI improves outcomes… when used properly. But there are still unknowns at play when it comes to AI’s risks and benefits.
This panel discussion—featuring leaders from Sirion, Spend Matters, and IBM—offers a behind-the-scenes look at the good, the bad, and the ugly of AI for CLM.
AI is everywhere. But, we need to master the data behind AI to create value in procurement and contracting. This requires outside intelligence to scale and augment with AI, all while monitoring risk and compliance.
Most of what is on the market today is traditional AI. Enter generative AI, which is creating so many more opportunities. When properly used, gen AI creates better outcomes based on automated intelligence around how to negotiate, spend planning, renewal planning, and deeper insights on the front end to get more value early on.
In this webinar, we dove deep into the innovative world of Contract Lifecycle Management (CLM) and the revolution brought about by generative AI. Our insightful chat with Pierre Mitchell (Spend Matters), Madison Gooch (IBM), Sujay Rao (Sirion), and Kanti Prabha (Sirion) covered what this new age of CLM looks like and best practices for implementing AI for CLM in your organization.
Here are the key highlights:
The CLM space is undergoing yet another transformation with the advent of generative AI. It’s important to understand how this differs from traditional AI, which predominantly focuses on data extraction. The new generative AI offers a broader range of potential applications, leveraging vast datasets.
A notable feature driving this change? Language. As language serves as a key driver, we need to understand LLMs (large language models) and the best ways to use them.
Businesses and enterprises require different AI capabilities and data sets than what the general consumer needs. There are four core principles to tailor generative AI for the enterprise.
1. By making contracting conversational – Chatbots are at the forefront of this change and allow users to do things like draft a standard NDA or ask direct questions about the contents of a contract – summarizing, understanding context, identifying risks (clause favorability or ambiguity), generating reports on the fly to get insights.
2. By making contracting zero-touch – Redlining a contract is a manual, time-consuming process involving multiple people. While human involvement and strategy will still exist in drafting contracts, you can use AI for contracts to set up auto-redlining processes to give you a head start. AI will also help automate workflows by predicting and recommending the next steps, prompting users to take action, and auto-generating drafts for related documents. For example: AI monitors and senses an upcoming renewal date, generates a first draft of a new contract, and then automatically sends the draft to the first reviewer in your workflow.
3. By requiring a multi-model and multi-modal approach – A single LLM won’t cut it. Different LLMs are good at different things. So, depending on your use case, you may need one or more specific LLMs supported in your system to handle various tasks.
With great power comes great responsibility.
This was only a glimpse into the power of generative AI for CLM. As we move forward, it’s evident that integrating AI into contract management is not just a passing trend but a necessity for getting the most business value out of your contracts. Connect with us 1-1 to learn more about how an AI CLM works for your organization.
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