Contract managers are drowning in data. How can you use machine learning to stay afloat?

In my exploration of the evolving landscape of contract management, machine learning has emerged as a pivotal technology. At its core, machine learning is a facet of artificial intelligence that enables systems to learn from data, identify patterns, and make decisions with minimal human intervention. This technology is revolutionary, not because it seeks to replace human insight, but because it augments our capabilities, allowing us to interpret and manage data with unprecedented efficiency and accuracy.

Machine learning is a branch of artificial intelligence (AI) depends on the use of data and algorithms to imitate the way that humans learn, gradually improving its accuracy. It has streamlined many tasks within contract management, while AI has begun helping contract managers draft and negotiate agreements.

From my perspective, machine learning can be a lifeline for contract managers submerged in data. By automating the extraction, organization, and analysis of contract data, machine learning can significantly reduce the time spent on manual tasks, allowing contract managers to focus on strategic decision-making. Moreover, machine learning algorithms can identify trends and anomalies in contract data, providing insights that can lead to more informed, data-driven decisions. This capability is invaluable in optimizing contract performance, mitigating risks, and identifying opportunities for value creation within the contract lifecycle.

There are a lot of advantages that can be offered by machine learning during the Contract lifecycle management (CLM) such as; speed and effeciency. It has streamlined many tasks within contract management, while AI has begun helping contract managers draft and negotiate agreements. Machine learning is a subfield of AI that can learn from data and repetitive tasks to solve routine and complex problems. Machine learning contract management can create contract drafts by pulling information from another source — a form, spreadsheet, or chatbot — eliminating the need for manual data entry. As a result, a routine or boilerplate agreement might be mostly complete before the parties finalize it.

The market today offers a plethora of machine learning tools designed to streamline various aspects of contract management. Tools like Kira Systems and ThoughtTrace specialize in contract analysis, efficiently extracting and organizing key data from contracts. On the other hand, platforms such as Juro and Outlaw focus on contract generation, leveraging machine learning to automate the creation of customized contracts based on predefined templates and rules. For a comprehensive solution, contract lifecycle management tools like Icertis and Agiloft integrate machine learning across the entire contract lifecycle, enhancing efficiency and insight at every stage.

Embarking on the machine learning journey can be approached methodically to ensure success. Firstly, it's crucial to clearly define the specific challenges and objectives you aim to address with machine learning. Whether it's automating data entry, enhancing contract analysis, or streamlining contract generation, understanding your goals will guide the selection of the appropriate tool. Starting with a pilot project allows for the evaluation of the tool's effectiveness in a controlled environment, providing valuable insights and learning opportunities. As proficiency and confidence grow, machine learning can be progressively integrated into broader contract management processes, continually adapted and refined to meet evolving needs.

Machine learning continues to impress me. Even the most simple software companies have improved their effectiveness by iteratively learning from new data and feedback. I have found the most value in contract lifecycle management. Machine learning optimizes the contract management process by automating workflow tasks, tracking contract milestones, and sending alerts for upcoming deadlines or renewals. From an administrative standpoint, this streamlines routine tasks, freeing up time to address higher-value activities, such as financial analysis, strategic negotiations and performance management.