Choosing a Generative AI for Your Law Firm

Compare the business service, not just the brand

A personal subscription and an organization-managed workspace are different purchasing decisions. Paying for an individual account does not establish that it meets your firm's confidentiality requirements.

• ChatGPT: OpenAI says business data from ChatGPT Business, Enterprise and its API is not used for model training by default. Its enterprise privacy documentation also describes access controls and data handling. [1]
• Claude: Anthropic says inputs and outputs from commercial products are not used for training by default. Its policy includes exceptions for feedback and voluntary data sharing. [2]
• Gemini: Google says Workspace customer data is not used to train its generative AI models without the customer's prior permission or instruction. Those protections apply within the documented Workspace service scope; third-party apps have different terms. [3]

The providers make these commitments for their business offerings:

These commitments help inform a security review. They do not answer every question about storage, access, deletion or disclosure. Your firm should review the exact plan, contract, enabled features and connected services it intends to use.

Where each platform belongs in your evaluation

The following are suggested pilot starting points, not rankings of legal accuracy. Actual results depend on the task, model, input quality and configuration.

Gemini for Google-centered workflows

Google provides Gemini features within applications including Gmail, Docs and Sheets. [4] If those tools are already central to your firm, an integrated pilot can help you assess whether AI reduces copying, switching applications and repetitive drafting.

Begin with a fictional email thread or an approved internal procedure. Ask Gemini to summarize the discussion, identify open questions and prepare a draft response. Check whether it omits a qualification or draws on material that should be outside the task.

Integration is useful only when access is appropriate. Review shared folders, broad permissions and matter restrictions before connecting client repositories.

Claude for writing and synthesis

Anthropic describes Claude's business chat offering as supporting writing, research and analysis. Its Enterprise offering includes controls such as SSO, provisioning, audit capabilities and retention controls. [5]

A useful pilot is to give Claude an approved document set and ask for a summary that separates stated facts, conflicting accounts and unanswered questions. Evaluate whether the response remains faithful to the sources. A readable summary can still omit an exception that changes its meaning.

Assess the controls available in the plan you are purchasing. Do not assume Team and Enterprise provide identical administration.

ChatGPT for varied tasks and data analysis

ChatGPT supports analysis of uploaded spreadsheets and other files, including tables, charts and code-backed calculations. OpenAI also notes that file support varies and that scanned or complex documents may not be analyzed completely. [6]

Consider a pilot using synthetic operational data: summarize trends, compare categories and explain the calculation method. Separately test a writing task so you can evaluate both analytical and editorial usefulness.

Check the underlying calculations and source coverage. A successful upload does not establish that every page or row was considered.

Establish security requirements before scoring productivity

Treat confidentiality requirements as an eligibility gate. A platform that fails them should not win because it produces a stronger draft.

1. Which information classes and use cases are approved?
2. Who can access prompts, files, outputs and shared workspaces?
3. What retention and deletion settings apply, including exceptions?
4. Which apps, search tools and external actions may receive information?
5. Can the firm manage identities, remove departing users and restrict sharing?
6. What contractual, client, court or regulatory requirements affect each matter?

Ask your technology and legal leadership to resolve these questions:

For health information, assess whether HIPAA applies to the firm's role and the intended processing. Where applicable, confirm an appropriate business associate agreement and covered service scope; a general enterprise label does not establish permission to process PHI. [7]

ABA guidance identifies competence, confidentiality, communication and supervision among the obligations implicated by generative AI. The State Bar of California's 2026 guidance also addresses agentic systems and their access to confidential information. [8, 9]

Run a pilot that measures the finished work

A practical starting point is a four-week pilot using fictional, public or specifically approved material. Pick three recurring tasks and give each candidate comparable inputs and instructions. Record which product configuration you tested.

Have attorneys and staff assess accuracy, omissions, source traceability, usability and the time required to reach an acceptable result. Measure review and correction time alongside generation time.

For example, a summary that takes seconds to create but requires substantial correction may offer less value than a slower draft that reliably preserves the relevant details. Check every legal citation against authoritative sources and require attorney review of substantive legal work.

Compare total costs as well: licensing, configuration, training, support and review. Expand only after the pilot demonstrates useful results within the firm's approved boundaries.

Choose a platform your firm can govern

For a Google Workspace firm, Gemini is a sensible integrated pilot candidate. For writing and document synthesis, include Claude. For varied drafting and data tasks, include ChatGPT. These are starting points for evaluation, not universal winners.

One primary platform can simplify training and administration. Add another when a defined workflow justifies the extra cost and oversight.

Allnext Legal helps law firms evaluate AI platforms, plan adoption and manage implementation with security and everyday productivity in mind.