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Generative artificial intelligence (GenAI) applications—such as ChatGPT, Gemini, and similar platforms—are reshaping how individuals access legal information, offering conversational, responsive, and highly personalized interactions. These tools can improve general legal awareness and streamline aspects of document generation. However, their ability to produce tailored, context-specific responses introduces meaningful risks—particularly when used without sufficient subject-matter context or experience with the specific legal issue at hand.

Unlike traditional legal research tools, GenAI systems do not merely retrieve information; they generate outputs based on how a user frames a question. In practice, this creates a structural risk: the quality and reliability of AI-generated output are closely tied to the user’s underlying assumptions and level of domain knowledge. Where that foundation is incomplete, the prompts themselves may be incomplete—resulting in responses that are misdirected or fail to address the full scope of the legal issue.

Equally important, those same limitations may prevent users from recognizing what is missing or incorrect in the resulting work product. Because the output is often well-structured and responsive, it can create a false sense of competence—even where critical elements have been omitted or misunderstood.

This dynamic can amplify confirmation bias in legal decision-making. Rather than surfacing objective legal standards or fully developed risk considerations, GenAI may reinforce a user’s preexisting beliefs about their rights, obligations, or desired outcomes. The result is not merely misinformation, but a more subtle—and more consequential—outcome: the production of legal documents or strategies that appear complete and credible, yet fail to account for material risks, contingencies, or jurisdiction-specific requirements.

In estate planning and transactional work, these risks are often realized far downstream, where errors may remain latent until a triggering event—such as incapacity, death, dispute, or enforcement—exposes structural deficiencies. Due to the nature of estate planning, when failures occur, they are rarely incremental; they tend to be consequential, difficult to unwind, and, in many cases, irreversible.

While GenAI may assist in drafting baseline documents, effective legal planning requires more than document generation—it requires issue spotting, structural alignment with a client’s assets and objectives, and the integration of multiple legal considerations into a coherent and enforceable plan. These functions depend on professional judgment and experience, not merely language generation.

These confirmation bias dynamics reflect longstanding patterns in how individuals seek legal information online, where question framing, selective engagement, and confirmation-seeking behavior shape outcomes. GenAI systems compress this process—collapsing multiple sources, perspectives, and decisions into a single, authoritative-seeming response shaped by the user’s input. In this environment, three areas where bias and error consistently emerge can be identified:

• How the question is framed: The way a legal question is posed—and the completeness of that question—shapes the scope and direction of AI-generated responses. Where key facts or concepts are missing, the output may be correspondingly incomplete or misdirected.

• Gravitation toward confirming outcomes: Users tend to favor outputs that align with their expectations or desired outcomes—while AI systems tend to produce agreeable, responsive answers—further perpetuating confirmation bias.

• Failure to recognize gaps and conflicting information: Users may fail to identify gaps or avoid engaging with responses that introduce complexity or contradict their initial assumptions, allowing incomplete or inaccurate conclusions to persist.

Estate planning is not one-size-fits-all, and state-specific rules play a critical role in whether a plan functions as intended. In Texas, considerations such as homestead protections, community property laws, and tools like Lady Bird deeds can significantly impact how assets are structured and transferred.

AI-generated documents often lack the context needed to account for these nuances. A plan that appears complete in form may fail in application if it does not align with Texas-specific legal frameworks or the client’s actual asset structure.

Effective planning requires more than assembling documents—it requires ensuring that each component works together within the governing legal landscape. This is where professional judgment and jurisdiction-specific experience become essential.

Accordingly, while GenAI represents a valuable tool within the legal ecosystem, its effective use depends on informed supervision and critical evaluation. The role of the attorney is not displaced by these technologies, but rather becomes more clearly defined: to guide the process, apply professional judgment, identify risks, ensure completeness, and translate generated content into a legally sound and strategically appropriate outcome.

AI can be a helpful starting point, but estate planning requires more than generating documents—it requires getting the structure right.

If you’re considering using AI—or already have documents in place—we can assess your situation, identify structural gaps or risks, and recommend an appropriate path forward.