Three Approaches to Choosing Legal AI: The Pros, Cons and Cost Considerations

The conversation about AI in legal has evolved. For law firms, the question is no longer whether to use AI. It’s about how to choose legal AI solutions that deliver meaningful value.

Not All AI Is Created Equal

New AI products are launching at a rapid pace. But not every tool is designed to work the way you and your team work. Achieving meaningful impact requires a clear strategy, well-defined use cases, and a structured plan for implementation and adoption. In this article, we will cover:

• Defining and aligning your AI strategy with business and client goals.
• Three distinct approaches to AI with their pros, cons and cost considerations.
• Security, privacy and regulatory concerns to raise with legal tech companies.
• Maximizing your AI investment through thoughtful adoption.

Define and Align Your AI Strategy With Real Goals

Before diving into AI tools, start with strategy. Define why AI can deliver value and what outcomes you expect it to drive. Start by identifying the firmwide priorities, whether that’s winning more work, streamlining specific workflows, improving collaboration, or delivering more value to clients. Then, connect those priorities to practical AI use cases.  

AI adoption is most successful when it supports both business strategy and your client experience goals. For example, a litigation team might focus on using AI to accelerate chronology creation or document summarization, while a transactional team might target contract analysis and risk assessment. In each case, the goal is to apply AI where it can meaningfully and measurably enhance the outcome. 

Once you’ve identified your target use cases, define your expectations around performance and value. Be sure to benchmark pre-AI performance metrics like drafting time, administrative workload and user satisfaction. Over time, this information will help measure progress, calculate ROI, and identify new use cases.

Here are some examples of AI use cases:

• Summarization and analysis. AI that creates document and transcript summaries, surfaces key information, and automates issue classification and coding.
• Transcript management. AI designed to analyze individual or multiple transcripts to identify inconsistencies, speaker sentiment and gaps in the narrative. This information accelerates deposition designations, enhances witness strategy, and supports case strategy efforts.
• Trial preparation. AI with natural language query capabilities enables trial teams to track the issues that must be proven in court, witness strategy and more.

Three Approaches to Legal AI: The Pros and Cons

After you’ve identified AI use cases that align with law firm and client goals, the next step is choosing legal AI tools. Law firms typically take one of three main approaches, each with distinct advantages and trade-offs depending on your needs, resources and risk appetite. Choose the option that best fits your workflows, timeline and client demands.

1. Consumer-Grade AI tools

Tools like ChatGPT, Gemini and Copilot can be appealing because they’re easy to access, often free or low-cost, and require no technical setup. However, consumer-grade AI platforms come with significant limitations and risks, especially for legal professionals. Without strong controls around data use, confidentiality and security, these tools can expose sensitive information, which is why some firms expressly prohibit their use.

2. Standalone Legal-Specific AI Tools

Solutions like Harvey are designed for legal work and can be used for a variety of legal tasks across the firm. That said, standalone solutions aren’t designed specifically for any one purpose, so users must know what to ask and how to ask it in order to see value. They also require users to invest in training, learn a new system, and take extra steps to use it within workflows. These factors, in addition to data limits, can lead to low adoption rates. Considering the substantial financial and operational investment required, achieving positive ROI will take time to realize.

3. AI Integrated Into Existing Legal Tech Platforms 

This approach takes advantage of AI capabilities embedded into platforms your teams already use. Established legal tech providers are increasingly developing AI of their own and integrating it into their products. For example, AI is now available in many e-discovery, case management and litigation solutions. Built into familiar systems, integrated AI adoption is usually faster, less disruptive and more widespread. These solutions are often significantly more affordable than standalone tools and can accelerate high-value, time-consuming tasks without requiring teams to switch between tools. However, for simple, day-to-day tasks like email drafting and general information summarization, these solutions may be overkill.

Ultimately, there’s no one-size-fits-all strategy for choosing legal AI. Many firms combine approaches to meet different needs, such as using general AI for low-risk productivity tasks, standalone tools to accelerate general legal tasks, and integrated AI for efficiency gains in everyday workflows. The right mix depends on your goals, resources and the types of matters you handle. Regardless of your approach, it is important to consider the total cost of ownership, including license fees, implementation, user onboarding, and ongoing training and support.

Security, Privacy and Compliance Are Non-negotiables

In legal AI, trust is everything. Efficiency gains won’t mean much if your data or client confidentiality is at risk. When evaluating AI solutions, keep these considerations top of mind:

• Data governance. Does your vendor keep data isolated by case and client to eliminate cross-client leakage? Do they give clear and robust commitments on safeguarding your data?
• Transparency and auditability. Assess whether the tool supports adherence to the ethics rules that govern your practice, such as the ABA Model Rules of Professional Conduct or other jurisdiction-specific requirements. For example, do the outputs include the citations and links to underlying case data and documents in answers and results?
• Bias and accuracy. Ask the vendor to explain the known limitations of the AI models they use, including potential risks of bias or inaccuracy.
• Regulatory compliance. Ask whether the tool is designed to comply with applicable privacy and data protection laws and what measures are in place to protect data, such as encryption in transit and at rest.

Prioritize vendors that treat these concerns as fundamental design principles, not as add-ons or afterthoughts.

Maximize Your AI Investment Through Thoughtful Adoption

AI technology delivers real value only when people use it effectively. To get the most from your investment — and prove that your effort was worthwhile — focus on embedding AI into daily workflows and supporting your users. Key steps include:

• Integrate AI into daily tasks. Start with intuitive tools integrated into existing platforms to build comfort and confidence using AI.
• Train and enable users. Create a culture of creativity and innovation. Demonstrate practical use cases, like drafting summaries or querying transcripts. Then encourage users to explore and experiment.
• Measure and optimize. Track key metrics over time, such as speed, accuracy and user satisfaction. Use this data to demonstrate progress and identify where to focus future investments.

AI isn’t a magic bullet, but used correctly, it can be a powerful strategic tool.

When choosing legal AI tech, remember that ease of use and early user adoption are more important than promises of endless features and possibilities. In a crowded landscape of legal AI options, value comes from business alignment, outcomes and adoption.

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