Navigating the AI-Tool Frontier in Law: Top Concerns (Beyond Confidentiality)
Looking past privacy issues for now, here are some forefront frustrations with AI
The legal tech landscape is buzzing with the promise of Artificial Intelligence. AI tools are poised to revolutionize how legal professionals conduct research, draft documents, and strategize. There is cautious optimism, with a keen interest in leveraging any technology that can enhance efficiency and efficacy.
However, beyond the critical concern of client confidentiality–that’s certainly discussed quite often around these parts–significant reservations exist about the current state and trajectory of law-specific AI tools.
These are not dealbreakers, but critical points that vendors need to address and practitioners must carefully consider.
1. The "Black Box" Conundrum: What Exactly is Being Used, and Will It Still Work Tomorrow?
A major frustration with many legal AI tools is the opacity surrounding the underlying Large Language Models (LLMs) they employ. Is the tool running on a proprietary LLM, trained on a dataset without insight? Or is it a shiny API-calling wrapper around a well-known model like a version of OpenAI's GPT or Google's Gemini? This lack of transparency raises several practical and strategic concerns:
Performance Benchmarking: If the base model is unknown, it is incredibly difficult to gauge its capabilities against the rapidly evolving state-of-the-art. Is the "AI-powered" legal research tool actually leveraging the latest advancements, or is it several iterations behind the curve compared to what’s directly available from major AI labs? This makes it challenging to assess true value and ensure the most powerful resources are being used.
Version Stability: The AI field is moving at breakneck speed. Models are updated, deprecated, and replaced. If a legal tech provider heavily customizes a specific version of an LLM, what happens when that LLM provider issues a major upgrade or sunsets that version? Will the legal AI tool break? Will its performance characteristics change overnight, potentially impacting established workflows?
Upgrade Timing: A major concern involves an upgrade rolling out the day before a crucial deadline, forcing a re-learning of a tool or, worse, finding that its output quality has degraded for specific use cases. Consistency and reliability are paramount in legal practice, and the current "trust us, it works" approach from some vendors is not reassuring.
The "Proprietary" Premium vs. Open Innovation: While some firms are developing their own private LLMs, promising greater control over training data, this route comes with substantial initial and ongoing costs. For most, the question is whether a proprietary LLM can truly keep pace with the innovation and massive research and development budgets of giants like Google or OpenAI.
Cutting Edge: Tools like Custom GPTs and Google's NotebookLM offer a glimpse into what's possible with cutting-edge models. The question then becomes: is the specialized legal AI tool truly offering something more sophisticated than these rapidly advancing generalist-adaptable powerhouses, or is a curated interface being paid for on a potentially lagging or overly niche model?
Without clarity on the foundational technology, investments and workflow adjustments are being made based on faith rather than a clear understanding of the engine under the hood.
2. The Prompt Predicament: Give Guardrails, But Also the Steering Wheel
A major issue lies in how these AI tools handle user instructions – "prompt engineering." Currently, many tools seem to fall into one of two less-than-ideal camps:
Overly Prescriptive Pre-Configuration: Some tools offer a suite of "canned" functions. While these can be helpful starting points, particularly for common tasks, the underlying prompts used to generate these outputs are often hidden. This is problematic. A "summarize" button might provide a surface-level overview when a deep dive is needed. Without being able to see or modify the underlying prompt, the AI's focus cannot be tailored to the nuanced requirements of a specific task.
The Blank Slate Intimidation: Conversely, other tools offer a completely blank canvas, placing the entire burden of prompt engineering on the user. While this offers maximum flexibility, it also requires a significant investment in learning how to "talk" to the AI effectively. Lawyers are experts in law, not necessarily in LLM interaction–yet.
What is desired is a hybrid approach. A button for common tasks with well-designed pre-configured prompts is needed. But it is also necessary to see that prompt, understand its structure, and have the ability to tweak, customize, or even completely rewrite it.
This level of nuanced control, combined with sensible, expert-designed starting points, would make these tools significantly more powerful and adaptable.
3. The Silo vs. Swiss Army Knife Dilemma: The Challenge of Comprehensive, Specialized Legal AI
A major concern revolves around the breadth versus depth of AI capabilities, particularly in complex, multi-stage areas like patent law.
The "Jack of All Trades, Master of None" Risk: Many AI tools are marketed as comprehensive solutions for legal practice. However, it's a monumental task for programmers to build a single AI system that excels equally at patent drafting, prior art searching, patent analytics, and infringement analysis. The risk is that a tool performs many tasks adequately but none of them exceptionally. In specialized fields like patent law, “adequate” often is not good enough.
The Workflow Labyrinth with Multiple Applications: The alternative to a single, potentially mediocre, all-in-one tool is to subscribe to multiple specialized AI applications. While this allows access to best-in-class solutions for each specific task, it introduces significant workflow challenges. Developing an efficient and integrated workflow across multiple platforms can be complex.
The Difficulty of True Specialization: Truly specialized AI needs to be trained on relevant, domain-specific data and embody specific analytical methodologies. It’s a tough ask for developers to cover all these bases deeply.
This highlights a fundamental tension in the current legal AI market. Users need to be aware of the limitations of generalist tools when applied to specialized tasks and evaluate whether a suite of niche tools genuinely offers a more effective solution.
Bonus Frustration: The IP Portfolio Scalability Problem
A significant practical hurdle emerges when dealing with intellectual property at scale: the inefficiency of current AI tools in handling patent families or entire portfolios.
One-by-One Tedium: Many AI tools seem designed for single-document or single-task instances. This is often by design as, e.g., API calls can be taxing to a system and expensive.
Ignoring Interconnections: A tool that analyzes a US patent in isolation is missing a crucial layer of understanding. Family members abroad can be vital. Continuations likely have similar-but-different claims. The file histories will need to be reviewed.
Inability to Batch and Compare: A common need is to apply a consistent analytical framework across multiple assets and then compare the results. The ability to check a box and add some documents for analysis and comparison would be valuable.
The ideal AI tool for patent practitioners would offer robust batch processing capabilities. Something that functions like Google’s Notebook LM would be a great start, but sometimes the analysis calls for looking at batches or groups of documents at each instance.
The Path Forward
These concerns are rooted in a desire to see these technologies mature into indispensable assets for the legal profession. It’s your money and these tools are rarely inexpensive!
Greater transparency from vendors about their underlying models, more sophisticated and flexible prompt engineering capabilities, a clearer understanding of the trade-offs between generalist and specialized tools, and a genuine commitment to handling tasks at scale would go a long way.
As legal professionals, critical questions must be asked, better functionality demanded, and time invested to understand the capabilities and limitations of the AI tools integrated into practices.
The promise is undeniable, but realizing that promise requires a partnership between developers who understand the nuances of legal work and practitioners who are informed, discerning, and vocal about their needs. Only then can the AI frontier be confidently navigated and its full potential responsibly and effectively harnessed.
Disclaimer: This is provided for informational purposes only and does not constitute legal or financial advice. To the extent there are any opinions in this article, they are the author’s alone and do not represent the beliefs of his firm or clients. The strategies expressed are purely speculation based on publicly available information. The information expressed is subject to change at any time and should be checked for completeness, accuracy and current applicability. For advice, consult a suitably licensed attorney and/or patent professional.