Trust in AI: Navigating the Emotional Side of Generative AI Contract Management

Artificial intelligence (AI) is a proverbial double-edged sword for many industries, particularly in the nuanced territory of contract management.

On one edge of the blade, AI promises efficiencies unattainable by manual processes alone. On the other, there remains skepticism and distrust among stakeholders who fear the technology's potential pitfall of inaccuracies and biases. The challenge at the heart of AI integration in contract management is not just about implementing the tech — it's about fostering an environment where its capabilities are both maximized and deeply trusted.

In this blog post, we’ll explore the promise of AI in contract management, the skepticism that often accompanies it, and how to overcome these doubts.

First, Let’s Compare Traditional vs. AI-Enabled CLM Software:

Before we tackle the complexities of generative AI and trust, let's take a look at where contract management stands today. 

The traditional CLM system has long been the workhorse of legal and corporate affairs departments; however, even the most well-oiled legacy system has its limitations. These systems often require manual data entry, are prone to human error, and their inability to adapt in real-time to the nuances of the legal landscape can lead to inefficiencies and missed opportunities, as seen in the chart below:

On the other hand, AI-based CLM systems — especially those using generative AI — offer a promising solution to the limitations of traditional models by automating contract drafting, risk analysis, and clause suggestion. In fact, two-thirds of surveyed individuals have cited shorter contracting lifecycles as a notable impact and key benefit of adopting AI. Yet, the question of trust remains the biggest challenge: How do we ensure their reliability? 

The Heart of AI: Trust in Contract Analysis Starts with Understanding

Trust in AI is influenced by various factors, such as the clarity of the AI's operations, the validity of training data, and the "explainability" of its decisions. That's why trust starts with understanding the fundamentals; the more you know about how generative AI works, the more confidently you can use it and rely on it for tasks that require precision and accuracy. 

Here's a breakdown of the essentials of generative AI and the critical role of training data:

• Generative AI 101: At its heart, generative AI consists of complex algorithms and LLMs, like GPT-3. These AI systems sift through enormous amounts of text data to learn the nuances of language — how words and sentences are structured to convey meaning. This learning process allows them to generate new, contextually appropriate text when given specific prompts. This ensures that contracts are not only drafted with precision but also adapt to the specific needs of every agreement — for example, drafting a confidentiality agreement that perfectly matches the requirements of a new business partnership by understanding thousands of similar agreements. 
• Influence of Training Data on AI Outputs: The effectiveness and trustworthiness of an AI-based CLM system depend significantly on its training data. Essentially, the training data — including texts, documents, and other inputs — molds the AI's understanding and output.  The old adage 'garbage in, garbage out' holds particularly true here: if the training data is flawed or biased, the AI's output will be too, which could lead to inaccuracies or even legal issues. However, if the training data is diverse, comprehensive, and well-curated, the AI is more likely to produce outputs that are accurate, relevant, and legally sound.
• Need for Tailored Training Data: For AI to be truly effective in contract management, the training data must be:
  • Relevant: The data should pertain to the specific field or industry the contracts are meant for, ensuring that the language and terms used are appropriate and up-to-date.
  • Unbiased: To prevent unfair or prejudiced outcomes, the training data must be checked for and cleared of biases.
  • Comprehensive: A wide range of examples, including various contract scenarios and clauses, should be included to prepare the AI for as many real-world situations as possible. 

The Trust Divide: Native CLM Integration vs. Add-On Contract AI
When you incorporate AI into your CLM processes, how you do it matters. The two primary methods of AI integration —native integration and add-on AI — offer contrasting experiences, especially regarding trust and reliability. Here's how:

Native Integration

Native integration means incorporating artificial intelligence (AI) directly into your system's core, so it feels like a natural part of the whole operation. This approach ensures that AI enhances your technology seamlessly, making every task easier and more intuitive for you. Key advantages include:

• Seamless Integration: By designing AI as an integral component of your system from the ground up, it works smoothly alongside other components, leading to enhanced functionality without the friction that can come from trying to merge disparate systems.
  • AI becomes part of the system’s DNA, leading to smoother overall functionality.
  • There are fewer compatibility issues, as the AI is built to operate within the specific ecosystem of your technology.
• Deep expertise: AI-based CLM software providers, such as IntelAgree, gain an edge through their teams' substantial expertise in AI. Their knowledge in integrating AI seamlessly into systems means they can create more deeply integrated, useful features that anticipate and cater to user needs effectively.
  • Teams have a deep understanding of how to incorporate AI, drawing on past successes and learnings.
  • This leads to AI features that are not just added on but are an integral, well-thought-out part of the user experience.

Add-On AI

Conversely, AI added retroactively involves integrating third-party AI solutions into an existing CLM setup. This approach might seem appealing for its apparent immediacy in "AI-enabling" a platform, but it comes with drawbacks:

• Patchy User Experience: CLM systems with AI added as an afterthought are, by their nature, often less integrated into the core system and face interoperability challenges. 
  • Since the AI wasn't part of the original design, its addition can feel clunky and disjointed. Users might find themselves navigating a maze of functionalities that don't mesh well.
  • This disconnect between the AI solution and the core system can frustrate users, eroding their trust over time and causing doubts about the system's AI accuracy.

• Security Concerns: Integrating a new, add-on AI system with your existing infrastructure without the requisite expertise can be fraught with risks. 
  • Integrating bolt-on AI solutions often involves navigating through a tangle of code or meshing together different security structures. 
  • For inexperienced teams, this could increase the potential for errors or oversights that compromise system integrity. 

Whether you opt for a fully integrated solution or add-on, your decision shapes how much you can rely on your system. With this choice comes a bigger question: How do we retain trust in AI while staying true to the human expertise that has always guided us?

What’s Causing Resistance to Generative AI Adoption in Legal Tech?

Skepticism towards generative AI in contract management stems from a profound duty to safeguard the interests represented in each contract. This cautious attitude is clear when you look at the numbers: 61% of legal experts have yet to bring AI into their contract processes, indicating a widespread wariness to fully commit to a technology that's still evolving. 

And their fears are understandable; they worry about missing out on the human touch — the rich understanding of laws, precedents, and intentions that has always been essential in reviewing and drafting contracts. Plus, many question whether AI can accurately grasp the subtleties of legal language or foresee the complexities that might arise in future disputes.

Their primary concerns include:

• Biases in AI algorithms that could skew decision-making.
• Errors in contract generation that could lead to legal vulnerabilities.
• Loss of human insight and expertise, fueling concerns about job security within the legal profession.

Furthermore, there's an underlying anxiety about the opacity of AI processes. How does the AI decide what's crucial in a contract? If something goes awry, can we trace back through the AI's 'thought process'? Understanding these concerns is the first step toward reconciling them, and in the next section, we'll share practical tips for fostering trust through transparency, ethical AI practices, and human oversight.

Six Ways to Build Trust in AI-Based CLM Software

Building trust in generative AI among CLM stakeholders doesn't happen overnight. It requires a concerted effort to address fears and demonstrate value in tangible, concrete terms. Here's how:

• Ensure Transparency: When users understand how and why an AI system reaches its conclusions, they are more likely to trust and rely on it. Choose an AI-based CLM platform that explains how it decides on specific clauses or terms based on the input data.
• Start Small: Pilot the AI system in non-critical processes, such as drafting standard non-disclosure agreements, before applying it to more complex contracts. Share detailed reports and analyses of these pilot projects, highlighting both successes and areas for improvement. This strategy allows stakeholders to witness firsthand the practical benefits and reliability of AI, gradually building their trust through positive, low-stakes experiences.
• Vet AI Vendors Thoroughly: Before selecting an AI-based CLM platform, ensure the vendor meets rigorous standards for ethical AI practices and data security. Ask questions like:
  • How do they ensure their training data is diverse and unbiased?
  • What measures do they take for data encryption?
  • How robust are their backup and recovery systems?

To ensure you’re making an informed decision, download our whitepaper, "Contract Lifecycle Management & The Generative AI Impact" for a free, comprehensive list of questions to vet AI-based CLM software vendors.

• Implement a Human-in-the-Loop System: This "trust but verify" approach keeps human expertise at the center of AI's application. It means that even as the AI generates contract drafts, a skilled legal professional reviews these outputs, ensuring they meet the high standards expected in legal document preparation. 
• Customize Your Legal Playbook: Tailor the AI system to your specific needs by keeping your organization's legal playbook — including contract templates, clauses, and terminology — up to date. Training your AI tool to recognize and apply these elements accurately ensures that the generated contracts are not only legally sound but also aligned with your organization's standards and expectations.
• Educate Your Team: Knowledge is power. Organize regular training sessions and workshops to keep your team up-to-date on how the AI works, its capabilities, and its limitations. Provide resources like manuals, FAQs, and access to expert support for ongoing learning. When team members understand how AI enhances their work, they're more likely to adopt and advocate for its use driving broader acceptance and integration into business processes. 

Wrapping Up

We get it — trusting AI with your contract management feels like a huge leap. It's more than just adding new tech; it's about finding a dependable ally that boosts both efficiency and accuracy, making sure your investment pays off. 

That's why we've crafted a whitepaper filled with tactics and tips to help generative AI become a trustworthy partner in managing contracts. Ready to turn skepticism into confidence? Download your copy of "Contract Lifecycle Management & The Generative AI Impact" now.