As enterprises increasingly adopt artificial intelligence (AI) to streamline operations, enhance decision-making, and improve customer experiences, the role of human oversight remains crucial. You’ve probably heard of "Human in the Loop" approaches where human intervention gets integrated into the AI workflow, improving accuracy, fairness, and ethical decision-making.

This iterative process improves model performance over time and helps AI adapt to new or unexpected scenarios where automated systems alone might struggle.

While this concept makes sense, in reality, at Real Story Group we’re seeing a new paradigm emerge: the "Lawyer in the Loop."

[Insert your own “difference between lawyers and humans” joke here…]

GenAI Pushing the Limits

The rise of generative AI (GenAI) has increased and/or sped up content flows in many enterprises. Not surprisingly, this makes enterprise legal teams nervous – especially those in highly regulated industries such as healthcare, pharmaceutical, and financial services.

In order to mitigate risks, the human in the loop is increasingly becoming a lawyer.

The "lawyer in the loop" model involves not just editorial quality control or subject matter oversight, but also the application of specialized legal knowledge, context, and ethical judgment.

When a lawyer joins the loop, they’ll check for things like:

1. Copyright & IP Protection: Safeguarding intellectual property rights by ensuring the proper use, attribution, and licensing of creative works and inventions
2. Compliance & Regulatory Checks: Verifying adherence to legal, industry, and organizational standards to mitigate risks and ensure lawful operations
3. Bias & Ethical Risk: Identifying and mitigating potential biases and ethical concerns in data, algorithms, or decision-making processes
4. Contractual Obligations: Analyzing business documents to ensure accuracy, compliance, and alignment with organizational goals
5. Fact-Checking & Hallucinations: Validating the accuracy of information and identifying fabricated or misleading content in generated text (technically you don’t need a lawyer for this, but they care a lot about this and are generally good at it)
6. Explainability & Transparency: Ensuring that decisions and outputs from systems, particularly AI, are understandable, interpretable, and accountable

The Problem of Scale and Efficiency

While the “Lawyer in the Loop" model helps to ensure accuracy and ethical oversight, it also presents cost and scalability challenges. Legal processes can be slow and expensive. Some key issues include:

• Bottlenecks in Workflow: AI can process vast amounts of data in seconds, but waiting for human lawyers to review and approve every decision can slow down processes significantly
• Increased Costs: Legal services are already expensive, and requiring lawyers to be in the loop for every AI-driven task can negate the cost-saving benefits AI offers
• Limited Availability of Expertise: Not every enterprise has the resources to provide specialized legal oversight for AI systems, which may hinder widespread AI adoption in smaller legal practices

In some enterprises – including among highly regulated industries – we are seeing legal teams employing AI of their own to sift through the volumes of AI generated content.

AI platforms checking AI-driven outputs brings several challenges, including bias in evaluation, lack of transparency, and limited scope in assessing a system’s performance. Since AI systems can be "black boxes," it’s hard to understand why certain decisions are made, which complicates accountability. You’ll want to proceed cautiously here and repeatedly check to make sure you are getting your desired results.

Finding the Right Balance

Ultimately, the "Lawyer in the Loop" model highlights the ongoing tension between AI-driven efficiency and the necessity of specialized oversight. While AI can enhance productivity and automate routine tasks, legal professionals remain essential for ensuring ethical responsibility, regulatory compliance, and nuanced decision-making. Moving forward, enterprises must strike a balance—leveraging AI to handle scalable, low-risk tasks while (at least for now) reserving human expertise for complex, high-stakes matters.

As AI continues to evolve, so too must the frameworks that govern its use, ensuring that innovation does not come at the cost of legal integrity or ethical accountability.

Brainstorming necessary frameworks is always a topic of discussion during our quarterly meetups with our MarTech Stack Leadership Council members.  The Council is an exclusive, confidential peer group, composed of primarily Global 2000 stack leaders. If you fit this profile and you’d like to join the discussion, please feel free to apply!