Artificial Intelligence (AI) is revolutionizing contract lifecycle management (CLM), streamlining contract creation, negotiation, and analysis.
AI-powered contract management solutions address the limitations of traditional methods by automating tasks, reducing human errors and providing AI-driven intelligence.
Take, for example, a study by researchers from Princeton University University of Pennsylvania and New York University which concluded that the industry most exposed to new AI technology was "legal services". At the same time, another research report by economists at Goldman Sachs estimated that 44% of legal work could be automated. Contracts are clearly ripe for AI-enabled automation. But every technological revolution comes with its set of challenges. For AI and contracts, the main challenges are data quality and security, required human oversight and intervention, and AI ethics.
Yet, the organizations that overcome these challenges will unlock streamlined processes, reduced costs, and improved compliance – and this gives them a competitive edge.
Awareness Of AI Contract Risk on the Enterprise
Contracts are central to business operations, impacting areas from risk management to finance. Yet, traditional contract management can hide critical information, exposing companies to risks. AI is improving this, but it also brings new challenges.
Primary among these is data security. AI contract systems store sensitive information, requiring robust security policies for protection and compliance. As AI becomes integral in contract management, legal and procurement teams must ensure its ethical application and security.
When vetting AI solutions, organizations should ask vendors about their AI ethics, data retention and privacy practices, and how they protect their source code against security threats. But these are just the starting points.
This article dives deeper into the complexities, risks, and strategies to harness AI's potential in contract management effectively.
The Contract AI Paradox - Automation vs. Human Expertise
According to Deloitte, organizations can lower contracting costs by 60% using intelligent CLM. But there is nonetheless a balance to be struck between automation and human expertise. As AI becomes more integrated into contract management processes, there is a risk that critical human skills may erode due to over-reliance on AI tools.
This over-dependence can also reduce human oversight, leading to potential errors, biases, and misinterpretations. To address this paradox, organizations must recognize the importance of human oversight in AI contract solutions. While AI can automate various tasks and provide valuable insights, it is crucial for humans to review AI-generated outputs, verify accuracy, and address potential discrepancies or errors.
Ethical Concerns, Bias, and the 'Black Box' Dilemma in AI Contract Negotiation
Furthermore, the lack of transparency in AI-driven processes can make it challenging for teams to identify inefficiencies and improve processes. It’s part of the “black box” dilemma, the often-mystifying nature of AI algorithms, making it difficult to understand the rationale behind AI-driven decisions in the legal context.
Not knowing or understanding where your data is coming from or how AI is using it poses implementation challenges, as it can lead to hard-to-detect biases, errors, and misinterpretations.
To address this issue, legal teams must be able to filter out sensitive vendor and internal information, secure data in a private cloud, and ideally leverage an LLM that offers a way to trace data back to its primary source. By doing so, you increase transparency and trust in the AI powering your negotiations.
The Future of Contract AI with LLMs
Contracts commonly feature high word counts, standard phrases, critical key terms, and specific dates, making legal contracts a great use case for Large Language Models (LLMs) in AI. These models already help with specific tasks adjacent to contract management, such as reviewing lengthy agreements, pinpointing language on a topic, and identifying the presence or absence of key clauses or concepts.
However, a challenge surfaces with the innate nature of LLMs—they generate content based on patterns from their training data. This can lead to inaccuracies, especially when solely relying on a single LLM that is not specifically trained for contracts. Such reliance can inadvertently cause the model to produce fictional details (hallucinations), leading to potential misrepresentations in the contract.
The solution? Diversification in LLM usage. By leveraging multiple LLMs, organizations can ensure a more balanced and accurate output. While one model might miss a nuance, another can catch and correct it. Off-the-shelf LLMs provide a generalist approach, but proprietary LLMs can be tailored to specific contractual nuances.
This blended approach, combining the strengths of different LLMs under human oversight, is what will drive the future of AI in contract management, ensuring accuracy, fairness, and agility.
Generative AI Privacy And Data Accuracy
The swift uptake of AI technology has elicited caution among security leaders –the World Economic Forum highlights CISOs' concerns over enhanced adversarial capabilities, potential data leaks, the elusive 'black box effect' of AI models, and the associated costs of implementation and security.
While these generative AI tools process data online, their exact data storage locations remain undisclosed. Service-specific terms and conditions require scrutiny to understand data usage.
Further questions emerge around the training data of these AI models. LLMs utilize public data for training, and organizations lack control or insight into that training data. The use of AI, therefore, prompts compliance concerns. When integrating customer data with generative AI, businesses must ensure compliance with GDPR and other regional privacy regulations.
An effective way to approach this is centralizing all contracts on a single platform and training the AI on the data from the business’ contract repository rather than external sources. Organizations must also establish specific compliance policies internally, conduct regular audits, and ensure the AI is continuously trained on updated, accurate data company data.
Navigating The Future of Contracting Technology: The Road Ahead
The introduction of LLMs in CLM marks a significant shift, with potential cost savings, improved efficiency, and enhanced accuracy all potential benefits. But, concerns about data privacy, algorithmic transparency, potential biases, and the balance between human expertise and automation introduce complexities inherent in this evolution.
Prudence is essential, and that includes ample human oversight. The onus is on companies to prioritize transparency and ethical considerations, address potential biases, and ensure data security and compliance.
Nonetheless, the organizations that apply these points of wisdom can make the most out of AI-driven CLM while minimizing the risks associated with deploying cutting-edge technology.
Want to find out more about what AI can deliver in contract management and how to deal with the potential challenges? Read: Ink to innovation: How AI Is Transforming Enterprise Contracting
About the Author
A highly experienced chief technology officer, professor in advanced technologies, and a global strategic advisor on digital transformation, Sally Eaves specialises in the application of emergent technologies, notably AI, 5G, cloud, security, and IoT disciplines, for business and IT transformation, alongside social impact at scale, especially from sustainability and DEI perspectives.
An international keynote speaker and author, Sally was an inaugural recipient of the Frontier Technology and Social Impact award, presented at the United Nations, and has been described as the "torchbearer for ethical tech", founding Aspirational Futures to enhance inclusion, diversity, and belonging in the technology space and beyond. Sally is also the chair for the Global Cyber Trust at GFCYBER.