What this article is and isn't
This is a practical overview of AI tools legal teams use for contract review, legal research, and drafting support. It is not legal advice, and nothing here should be read as a recommendation about how to handle a specific matter. None of the tools discussed are described as bar-certified, and none should be treated as a substitute for a licensed attorney's judgment or for independent verification of case law and statutory citations. AI legal tools are aids to research and drafting speed β the professional responsibility for accuracy, confidentiality, and the final work product still sits with the lawyer or paralegal using them. With that framing, here's what these tools actually do and where legal teams are using them today.
Legal research and case analysis
Legal research has historically meant hours in a database like Westlaw or Lexis, manually cross-referencing case law, statutes, and secondary sources. AI research tools built specifically for law aim to compress that process without removing the need for a lawyer to verify what comes back.
Casetext CoCounsel is built around legal research and document review workflows β asking research questions in plain language and getting back analysis grounded in case law and statutes, along with deposition preparation and contract analysis features. Harvey is a broader AI platform aimed at law firms, covering research, drafting, and due diligence workflows, often with deeper customization for firm-specific workflows and document sets. The two overlap significantly in the research use case, and the practical difference often comes down to firm size and workflow depth β the Harvey vs. Casetext CoCounsel comparison is a useful reference for teams trying to decide which fits their research and drafting volume better.
The single most important discipline with any AI legal research tool is citation verification. AI models, including ones built specifically for legal use, can produce citations that are subtly wrong or, in rare but well-documented cases, entirely fabricated. Every citation an AI research tool surfaces needs to be checked against the primary source β the actual case, the actual statute β before it goes into a filing or client-facing document. This isn't a hypothetical caution; courts have sanctioned attorneys for submitting filings with AI-generated citations that didn't check out. Treat AI research output as a lead to verify, never as a citation to submit directly.
Contract drafting and review
Contract work β drafting, redlining, and reviewing agreements against a playbook β is one of the areas where AI assistance has become genuinely embedded in day-to-day legal workflows, particularly for high-volume, template-driven agreements like NDAs, vendor contracts, and standard commercial terms.
Spellbook works inside Microsoft Word to suggest redlines, flag missing or unusual clauses, and draft standard contract language based on your firm's or company's own playbook and precedent. It's built for lawyers who are already drafting in Word and want AI assistance layered directly into that existing workflow rather than a separate platform to manage. Ironclad approaches contracts from more of a lifecycle-management angle β handling the workflow around contract creation, negotiation, approval routing, and repository management for an organization's contract portfolio at scale, with AI assistance built into that broader process.
The choice between them usually comes down to what problem you actually have. If the pain point is "our drafting and redlining takes too long and is inconsistent across attorneys," Spellbook's in-Word playbook-driven suggestions are the more direct fit. If the pain point is "we have hundreds of contracts in flight across departments and no visibility into where they are in the approval process," Ironclad's lifecycle management addresses a different and broader problem. The Spellbook vs. Ironclad comparison covers this distinction in more depth β it's less "which is better" and more "which problem are you actually solving."
As with research tools, AI-suggested contract language and flagged risks need attorney review before anything is finalized. These tools are strong at catching missing standard clauses or flagging deviations from a playbook, but they don't understand the specific commercial context, negotiating leverage, or client priorities behind a given deal the way the attorney handling it does.
Documentation and internal knowledge management
Legal teams generate a lot of internal material outside of filings and contracts β matter notes, research memos, precedent libraries, and meeting summaries from client calls and internal strategy discussions. Notion AI is useful here as a general organization layer: summarizing long research memos, keeping a searchable internal knowledge base of past matters and precedent, and cleaning up meeting notes into something the rest of the team can actually use. It is a general productivity tool and not a legal-specific platform, so it's best suited to internal organization rather than anything client-facing or filed with a court.
For capturing spoken content β client intake calls, depositions prep sessions, or internal strategy meetings β Otter.ai provides searchable, timestamped transcription. This can meaningfully speed up the process of turning a recorded conversation into usable notes. Given the sensitivity of legal conversations, especially anything touching privileged communication or client confidences, any transcription tool needs to be vetted against your firm's confidentiality obligations and client agreements before it's used on anything privileged β check the vendor's current data handling and retention terms directly rather than assuming a general-purpose transcription tool automatically meets a law firm's confidentiality requirements.
General-purpose assistants for drafting support
ChatGPT and Claude are widely used by legal professionals for lower-stakes drafting support β structuring a first draft of a non-privileged internal memo, brainstorming how to explain a legal concept to a non-lawyer client, or getting a second pass on the clarity of a document's plain-language summary. Neither is a legal research tool in the way Casetext CoCounsel or Harvey are built to be, and neither should be relied on for case law citations or statutory interpretation without independent verification β general-purpose models carry the same hallucination risk in a legal context as anywhere else, and the stakes of an uncaught fabricated citation in legal work are considerably higher than in most other fields. If you're deciding which general model to standardize on for non-privileged administrative and drafting support, the ChatGPT vs. Claude comparison covers general differences in reasoning and long-document handling between the two.
A further caution specific to legal work: general-purpose consumer AI tools typically are not built with attorney-client privilege or confidentiality obligations in mind by default. Before putting any matter-specific or client-identifying information into any AI tool β general-purpose or legal-specific β confirm the vendor's data handling terms, whether inputs are used for model training, and whether your firm's engagement letters and confidentiality obligations permit that use. This is a question for your firm's own risk and compliance function, not something to infer from a tool's marketing.
Building a stack that matches your actual workload
Not every legal team needs every category above. A solo practitioner or small firm doing a lot of contract-heavy work will get more immediate value from Spellbook than from a full research platform. A firm with heavy litigation research volume will find more value in Casetext CoCounsel or Harvey. A larger organization managing contracts across multiple departments β not just the legal team β is the more natural fit for Ironclad's lifecycle management approach. And general organization tools like Notion AI and Otter.ai are worth adopting regardless of practice area, since the administrative overhead they address β notes, memos, meeting capture β is universal across legal work.
The verification discipline that doesn't go away
The throughline across every tool in this article is that AI acceleration in legal work has to be paired with verification discipline that doesn't get any lighter just because the first draft came from an AI tool. Citations get checked against primary sources. Contract language gets reviewed against the actual deal context. Confidentiality obligations get confirmed with the vendor before anything sensitive goes into a tool. None of these tools change the standard of care a legal professional is held to β they change how much of the mechanical work happens before that professional judgment gets applied. Teams that get the most value from this category are the ones that treat AI output as a fast first draft requiring the same scrutiny as a junior associate's first draft, not as a finished product.
Firm policy and supervision
Most bar associations that have issued guidance on AI use in legal practice converge on a similar set of themes: competence in understanding how the tool works and where it can fail, supervision of any AI-assisted work product before it reaches a client or a court, and confidentiality safeguards around what information goes into any AI system. Individual attorneys experimenting with a tool informally is a very different risk profile from a firm formally adopting a tool across a practice group, and the latter typically warrants a written internal policy β covering which tools are approved, what information can and cannot be input, and who is responsible for reviewing AI-assisted output before it's finalized. If your firm doesn't yet have a policy like this and multiple attorneys are already using AI tools informally, that's usually a sign the policy conversation is overdue, not a sign the tools are fine to keep using without oversight. Junior associates and paralegals in particular should have clear guidance on what level of independent verification is expected before AI-assisted work is passed up the chain.
Client communication about AI use
Depending on your jurisdiction and the nature of the engagement, clients may have a reasonable expectation of knowing when AI tools are involved in work being billed to them, particularly around billing practices β if a task that used to take an associate three billable hours now takes forty-five minutes with AI assistance, the billing approach for that task is worth revisiting proactively rather than waiting for a client to ask. Some engagement letters now include explicit language about AI tool use; even where that's not yet standard practice, being able to answer a client's question about AI involvement in their matter clearly and honestly is worth preparing for in advance rather than improvising in the moment.
Matching tools to matter type
Not every matter calls for the same tool. High-volume, low-complexity contract work β standard NDAs, vendor agreements, routine commercial terms β is where Spellbook's playbook-driven redlining earns back the most time relative to manual drafting. Complex litigation with heavy research demands is where a tool like Casetext CoCounsel or Harvey's broader research and drafting capabilities matter more, provided every citation is independently verified before filing. Organizations juggling contract volume across multiple business units, not just the legal department, are better served by Ironclad's lifecycle and workflow management than by a pure drafting assistant. Matching the tool to the actual shape of the workload, rather than adopting whichever tool is best known, is the difference between a tool that gets used daily and one that gets a trial period and is quietly abandoned.
