Challenges of Using Generative AI in Contract Management

Generative AI holds immense promise but also introduces significant challenges, particularly in the realm of Contract Lifecycle Management (CLM). Legal teams, being naturally risk-averse, approach these tools cautiously due to the critical nature of contract handling, where errors can result in compliance failures, disputes, or financial losses. The nuanced and complex language of contracts and the lack of AI transparency exacerbate these concerns.

Key Issues to Address When Adopting Generative AI in CLM

1. Opaque Decision-Making Lacks Accountability 

   Generative AI often operates as a “black box,” offering insights or outputs without providing a clear rationale. This lack of explainability undermines trust, especially in critical areas like regulatory compliance and legal dispute resolution. Without a transparent decision trail, the defensibility of AI-generated results becomes questionable, leading to user skepticism.

2. Overreliance on a Single AI Model Reduces Flexibility

   With many new AI models emerging, each with unique strengths, depending on one model limits your options. Relying on a single-model CLM system could restrict adaptability and lock you into a vendor’s ecosystem, making it harder to pivot as technology advances.

3. Generic LLMs Are Ill-Equipped for Contract-Specific Needs

   Most commercially available LLMs lack the specialized training required to handle the intricacies of legal language, contract structures, pricing strategies, and negotiation nuances. Without deep pretraining tailored for contracts, these models are prone to generating inaccurate or misleading outputs—commonly referred to as “hallucinations.”

4. LLMs Alone Are Insufficient — Small Data Models Add Precision

   While LLMs can provide broad contextual understanding, certain contract management tasks—like identifying specific clauses, pricing terms, or compliance requirements—are better handled by small, task-specific models. These models, trained on contract-centric data, deliver more precise and reliable outputs for critical use cases. Relying solely on LLMs can reduce accuracy in contract workflows where precision is paramount.

5. AI Training and Maintenance Come with High Costs

   Building and maintaining an AI model is expensive. Training even a basic model can cost over $1 million, and you’ll also need experts to keep it running smoothly. This can significantly increase operational costs, making AI adoption a substantial financial commitment.

6. Data Security Risks with Third-Party LLMs

   CLM solutions leveraging external LLM providers may expose proprietary data to risks. When data flows between a CLM vendor’s platform and the LLM provider’s servers, there’s limited visibility or control over how the data is stored or used. Customers face the potential risk of sensitive information inadvertently being used to train models for other organizations.

Addressing these challenges is essential for businesses aiming to integrate generative AI into CLM workflows effectively and securely. Only by overcoming these hurdles can organizations fully realize the transformative potential of AI in contract management.