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Introduction

AI is revolutionizing business operations, and contract management is no exception. Contracts, being the backbone of business relationships, require precision and consistency, often demanding significant manual effort. The integration of AI technologies helps automate, analyze, and optimize these processes, offering efficiency and improved decision-making.

Traditional AI in Contract Management

The main use of AI comes from the fact that it can read documents and understand the meaning with context (a capability not possible with previous traditional workflow-based systems).

In this form, AI primarily focuses on automating repetitive, data-driven tasks. Contract management tools with traditional AI are capable of:

1. Monitoring compliance and key dates or performing risk analysis: AI systems automatically track deadlines, renewal dates, and obligations by analyzing large volumes of contracts to identify key terms and flag risks according to company policies.
2. Document classification and extraction: This has emerged as one of the most sought-after applications of AI in the contracting space. AI extracts critical metadata like parties, dates, payment terms, obligations, and other important clauses, improving contract visibility and searchability. It can also apply rules to determine if some important information is missing from the contracts.

For extraction, there have been two approaches:

• Standard Models: Pre-trained on market data, these can recognize common contract terms but may not handle unique or highly specific clauses effectively.
• Custom AI Models: These are trained using a company’s own historical contracts, providing greater precision but requiring more effort to develop.

Generative AI’s Impact on Contract Management

Generative AI goes beyond understanding data (as with traditional AI) and has a learning on how to create data. This capability enables it to draft content such as creating contracts or individual clauses instead of just analyzing existing ones.

This is where things take an interesting turn and Gen AI is used in following use cases:

• Automated Contract Drafting: Generative AI can create complete contracts or individual clauses from scratch, based on predefined inputs or specific requirements.
• Dynamic Templates: Rather than relying on rigid templates, generative AI can tailor contract language in real time, adjusting to evolving terms or negotiation conditions.
• Negotiation Support and Redlining: Generative AI offers real-time suggestions during negotiations, proposing clause revisions or identifying risks based on opposing party inputs. It can also automate the redlining process, ensuring consistency and tracking every change made during negotiations.
• Summarization and Insights: Generative AI can generate concise summaries, highlighting key terms, obligations, and risks, making it easier for stakeholders to make quick decisions.

Comparing Traditional AI vs. Generative AI in Contract Management

• Traditional AI: Focuses on automating repetitive tasks like metadata extraction and compliance tracking. It is best suited for structured data but may struggle with nuanced language in complex contracts.
• Generative AI: Creates new content and adapts to changing scenarios in real time. It offers flexibility in drafting and negotiation. It also has application in training custom models for Contract data extractions.

How to Adopt AI Technology for Organizations

AI has many use cases that can be applied to contract processes; however, it is still in its nascent stage, and organizations should not give AI complete control over decision-making. Here are some risks or pitfalls of relying entirely on AI:

1. Contextual Understanding Gaps:
AI lacks the nuanced understanding of industry-specific intricacies, ethical considerations, and cultural nuances, which can lead to misinterpretations or inappropriate decisions.
2. Handling High-Stakes Risks:
Fully automating critical decisions, especially in high-value or high-liability scenarios, increases the risk of costly errors, regulatory violations, or reputational harm.
3. Edge Case Challenges:
AI struggles with non-standard or unique situations, requiring human oversight to address outliers and ensure comprehensive handling.
4. Transparency and Trust Issues:
The “black box” nature of AI systems makes it difficult to build trust among stakeholders, particularly in high-risk or compliance-heavy contexts.
5. Data Quality Dependence:
AI’s performance hinges on the quality of input data. Errors, biases, or poor data hygiene can lead to flawed outcomes, necessitating human review.

Hence, for effective and safe AI adoption, companies should use a hybrid approach. This enables organizations to start leveraging AI technology without relinquishing full control. Clear rules must be established to define when AI can operate independently and when human oversight is necessary.

For instance:

• Low-risk contracts: Routine agreements below a certain dollar threshold can be fully managed by AI.
  
• High-risk or complex contracts: Those with significant liabilities or high value should involve human supervision to ensure accuracy and compliance.

Conclusion

AI brings significant advantages to the contracting space, offering the potential to transform how contracts are managed and utilized. While it is important to approach AI adoption with caution, the greater risk lies in ignoring its potential and playing it safe. Thoughtful implementation is key, opening the door too wide could expose your organization to unforeseen challenges, as AI still requires oversight and control to ensure alignment with policies and processes.

Author: Niraj Ittan