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Mastering AI for Contracts: How to Sidestep Common Adoption Mistakes

Mastering AI for Contracts: How to Sidestep Common Adoption Mistakes

Published on Jun 6, 2025

From drafting contracts to analyzing clauses, AI tools like DocuSign CLM and Ironclad streamline complex processes. However, adopting AI for contracts comes with challenges that can lead to costly mistakes if not addressed. Choosing the wrong tool, over-relying on automation, or neglecting data security can undermine the benefits of AI. This guide explores common pitfalls in using AI for contracts and provides practical strategies to avoid them, ensuring a smooth and effective adoption for cautious users.

Mistake 1: Choosing the Wrong Tool for Your Needs

Selecting an unsuitable AI tool is a frequent misstep when implementing AI for contracts. With numerous platforms available, each with varying features, businesses may pick a tool that doesn’t align with their needs. For example, a small law firm might choose an enterprise-level solution like Kira Systems, only to find its complexity and cost overwhelming for their modest contract volume.

To avoid this, assess your organization’s specific requirements before selecting a tool. Consider factors like contract volume, team size, and budget. For instance, a startup with 50 contracts monthly might opt for ContractWorks, which offers affordable, user-friendly features, while a corporation handling thousands of agreements might prefer Ironclad’s advanced analytics. Compare tools based on ease of use, integration with existing systems (e.g., Salesforce or Microsoft Office), and scalability. Reading user reviews on platforms like G2 or Capterra can reveal practical insights. Testing a demo or trial version ensures the tool fits your workflow. By aligning your choice with your needs, you ensure AI for contracts enhances efficiency without unnecessary costs.

Mistake 2: Over-Relying on AI Without Human Oversight

AI for contracts excels at automating repetitive tasks like clause extraction or compliance checks, but over-reliance without human oversight can lead to errors. AI may misinterpret nuanced legal language or miss context-specific details. For instance, an AI tool might flag a non-compete clause as standard but fail to notice it violates local labor laws, leading to legal risks.

To mitigate this, maintain human oversight in the contract process. Train staff to review AI outputs, especially for high-stakes agreements like mergers or partnerships. For example, a legal team using LawGeex should verify AI-generated summaries against original contracts to catch discrepancies. Establish a hybrid workflow where AI handles initial reviews, and lawyers finalize critical decisions. Regular training ensures staff understand AI’s limitations, such as struggles with ambiguous terms. A 2023 study from Gartner noted that 65% of AI-driven contract errors stemmed from lack of human validation. By combining AI’s speed with human expertise, you maximize the reliability of AI for contracts while minimizing risks.

Mistake 3: Ignoring Data Privacy and Security Risks

Data privacy and security are critical when using AI for contracts, as these tools process sensitive information like financial terms or personal data. Ignoring these risks can lead to breaches or non-compliance with regulations. For example, a company using an AI platform without robust encryption might expose client data, resulting in fines or reputational damage.

To avoid this, prioritize tools with strong security features. Look for platforms compliant with ISO 27001 or SOC 2 standards, which ensure data protection. Verify that the tool uses end-to-end encryption and secure cloud storage. For instance, DocuSign CLM emphasizes compliance with global standards, making it a safer choice. Conduct due diligence by reviewing the vendor’s privacy policy and data handling practices. Additionally, limit data sharing to only what’s necessary for contract processing. Regular security audits and employee training on data handling reduce vulnerabilities. By addressing these risks, you ensure AI for contracts operates within a secure framework, protecting your organization and clients.

Compliance with GDPR and CCPA

Compliance with regulations like GDPR (General Data Protection Regulation) and CCPA (California Consumer Privacy Act) is crucial when adopting AI for contracts, especially for businesses handling personal data. Non-compliance can result in penalties, such as GDPR fines up to €20 million or 4% of annual revenue. For example, a European firm using AI to process contracts with customer data must ensure the tool adheres to GDPR’s data minimization and consent rules.

To stay compliant, choose AI tools with built-in regulatory features. Platforms like OneNDA offer GDPR-compliant templates, while Ironclad provides audit trails for transparency. Train your team on GDPR/CCPA requirements, such as obtaining explicit consent for data processing. Implement data retention policies to delete contract data after its purpose is fulfilled. For instance, a retail company might configure its AI tool to anonymize customer data post-contract execution. Regular compliance audits, ideally quarterly, ensure ongoing adherence. By prioritizing GDPR/CCPA compliance, you safeguard your use of AI for contracts against legal risks.

Best Practices: Combining AI with Legal Expertise and Regular Audits

To maximize the benefits of AI for contracts, adopt best practices that balance technology with human expertise. First, integrate AI with legal oversight. Use AI for initial tasks like clause analysis or risk flagging, but have lawyers review outputs for accuracy. For example, a legal team might use Conga Contracts to identify risky clauses, then verify them manually to ensure context is preserved.

Second, conduct regular audits of AI processes. Quarterly reviews of AI outputs, data storage, and compliance can catch issues early. For instance, audit logs from an AI tool might reveal unauthorized data access, prompting security updates. Third, train staff regularly on AI tool usage and legal updates to maintain proficiency. Finally, start with pilot projects—test AI on low-risk contracts like NDAs before scaling to complex agreements. These practices ensure AI for contracts enhances efficiency while maintaining accuracy and compliance.

Case Study: A Company’s Success After Avoiding AI Pitfalls

TechTrend, a mid-sized tech firm, successfully adopted AI for contracts by avoiding common pitfalls. Initially, they chose a mismatched tool, leading to workflow disruptions. After reassessing, they selected ContractWorks for its simplicity and affordability, aligning with their 200-contract monthly volume.

To prevent over-reliance, they trained their legal team to review AI-drafted contracts, catching a misclassified clause that saved them from a $50,000 dispute. By prioritizing GDPR-compliant tools and conducting quarterly audits, they avoided data breaches. TechTrend’s strategic approach reduced contract processing time by 40% and cut legal costs by 25%, proving the value of cautious adoption.

Conclusion

Adopting AI for contracts offers immense potential to streamline legal processes, but pitfalls like choosing the wrong tool, over-relying on automation, and ignoring data privacy can derail success. By carefully selecting tools, maintaining human oversight, prioritizing security, and adhering to regulations like GDPR/CCPA, businesses can avoid these traps. TechTrend’s success shows how strategic adoption pays off. With best practices like regular audits and pilot projects, organizations can confidently leverage AI for contracts, ensuring efficiency, compliance, and cost savings while minimizing risks.