How commas, semicolons, subjects, objects, and verb tense quietly determine whether a claim is covered — and how AI can help you see what the drafter buried in the sentence structure
Missouri Injury & Insurance Law | missouriinjuryandinsurancelaw.com
Introduction
Insurance policies are contracts, and contracts are made of sentences. The outcome of a coverage dispute often turns not on some grand question of public policy but on a far more granular one: what does this sentence actually say? Who is the subject? What is the verb? What modifies what? Does that comma change the meaning? Does the semicolon create a separate condition or merely continue the same one?
These are questions of English grammar and syntax, and they are questions that AI handles remarkably well. Large language models are, at their core, language-processing engines. They were trained on the structural rules of English. They can diagram a sentence, identify its grammatical components, and explain how changes in punctuation or word order alter meaning. For the insurance coverage practitioner, this capability transforms AI into something more specific and more useful than a general research assistant: it becomes a syntax interpreter for policy language.
This post demonstrates how to use AI to break down the grammatical structure of policy provisions and uncover ambiguities that traditional reading may overlook.
I. Subject, Verb, Object: Who Does What to Whom
Every coverage grant and every exclusion is built on a subject-verb-object framework. The subject identifies who acts or who is affected. The verb defines the action or state. The object identifies what is acted upon. When policy language is clear, these components are easy to identify. When it is not — and it frequently is not — the ambiguity in the sentence structure becomes the ambiguity in the coverage.
Consider a common exclusion: “This policy does not apply to bodily injury or property damage arising out of the ownership, maintenance, or use of any aircraft, auto, or watercraft owned or operated by any insured.” The sentence has a clear subject (“this policy”), a clear verb (“does not apply”), and a clear object (“bodily injury or property damage”). But the modifier chain — “arising out of the ownership, maintenance, or use of any aircraft, auto, or watercraft owned or operated by any insured” — is where the real work happens. Does “owned or operated by any insured” modify only “watercraft,” the last item in the series? Or does it modify all three — aircraft, auto, and watercraft? The answer determines whether the exclusion applies, and the answer depends on grammar.
Ask the AI: “In the following sentence, identify the subject, verb, and object. Then identify all modifying phrases and explain what each one modifies. State whether the final modifier applies only to the last antecedent or to the entire series, and explain the grammatical basis for your conclusion.” The AI will walk through the sentence structure and apply the last-antecedent rule or the series-qualifier canon as appropriate, giving you a grammatical foundation for your coverage argument.
Practice Tip: When the AI identifies a modifier as grammatically ambiguous, that ambiguity is your argument for coverage. Under Missouri law, ambiguous policy language is construed against the insurer and in favor of coverage. AI’s grammatical analysis gives you the technical basis for establishing the ambiguity.
II. The Comma That Changes Everything
Punctuation in policy language is not decorative. A single comma can alter the scope of an exclusion, the breadth of a definition, or the conditions under which coverage applies. The most frequent punctuation disputes involve the serial comma (or its absence) in a list of conditions, and the placement of commas around modifying clauses that may be restrictive or nonrestrictive.
A restrictive clause limits or defines the noun it modifies and is not set off by commas. A nonrestrictive clause adds information but does not limit the noun, and it is set off by commas. The distinction matters enormously in policy language. Consider: “We cover employees who are acting within the scope of their employment” versus “We cover employees, who are acting within the scope of their employment.” The first sentence restricts coverage to employees acting within scope. The second sentence covers all employees and merely adds a descriptive aside about scope. One comma. Opposite results.
AI can identify whether a clause is functioning as restrictive or nonrestrictive, explain the grammatical rule, and flag instances where the punctuation is inconsistent with the apparent intent of the provision. Prompt the AI: “In the following policy provision, identify all dependent clauses. For each one, state whether it is restrictive or nonrestrictive based on the punctuation and sentence structure. If the punctuation is ambiguous or inconsistent with the grammatical function of the clause, explain how each interpretation changes the meaning of the provision.”
III. Semicolons, Conjunctions, and List Structure
Semicolons in policy language typically signal that the drafter intended to separate independent conditions or distinct categories. A semicolon between two clauses usually means each clause stands alone as a separate requirement or a separate exclusionary category. A comma in the same position might suggest the clauses are part of a single, continuous condition.
The conjunctions “and” and “or” are equally consequential. “And” means all conditions must be met; “or” means any one is sufficient. When a policy exclusion lists three conditions joined by “and,” the insurer must prove all three to invoke the exclusion. When the same conditions are joined by “or,” proving any single condition is enough. Insurers and their drafters occasionally use these conjunctions inconsistently within the same policy, and the inconsistency itself can create an ambiguity.
Ask the AI to map the logical structure of a complex policy provision: “Break down the following provision into its component conditions. Identify each condition, state whether conditions are joined by ‘and’ or ‘or,’ and explain whether all conditions must be satisfied or only one. If the provision uses semicolons, explain how they affect the grouping of conditions. If the conjunctions are used inconsistently, flag the inconsistency.”
Practice Tip: When you find an “and/or” inconsistency within a policy, document it immediately. If the same provision uses “and” in one clause and “or” in an adjacent clause addressing parallel conditions, the inconsistency is strong evidence that the drafter intended a meaningful distinction — or, alternatively, that the provision is ambiguous and should be construed in favor of coverage.
IV. Verb Tense and Voice: When and Who
Policy drafters use verb tense and voice to define the temporal scope of coverage and to assign or obscure responsibility. Present tense (“the insured maintains”) implies a contemporaneous, ongoing condition. Past tense (“the insured maintained”) implies a condition that was satisfied before the event. Future tense (“the insured shall maintain”) creates a prospective obligation. Each tense produces a different coverage trigger and a different evidentiary burden.
Passive voice is the drafter’s favorite tool for obscuring agency. “Damage caused by the insured” is active and assigns causation to the insured. “Damage that is caused” is passive and leaves the actor unidentified. In an exclusion, the difference between active and passive construction can determine whether the exclusion requires the insurer to prove the insured caused the damage or merely that the damage occurred.
AI can identify every instance of passive construction in a policy provision and rewrite each one in active voice so you can see who the drafter chose not to name. Prompt: “Identify every passive-voice construction in the following provision. For each one, rewrite the clause in active voice and identify the implied agent. Explain how the shift from passive to active changes the evidentiary burden or the scope of the provision.”
V. Putting It Into Practice
The workflow is straightforward. Upload the policy provision you are analyzing. Tell the AI to break the sentence into its grammatical components: subject, verb, object, modifiers, dependent clauses, conjunctions, and punctuation. Ask it to identify every ambiguity — every instance where the grammar permits more than one reading. Then ask it to explain each alternative reading and its effect on coverage.
The AI’s output is not a legal opinion. It is a grammatical analysis that you, the attorney, will then apply within the legal framework of contract interpretation. But it gives you something that is surprisingly difficult to produce on your own under time pressure: a disciplined, sentence-level breakdown of language that was deliberately drafted to be dense and, in many cases, deliberately drafted to be ambiguous.
The insurers have teams of drafters who construct this language carefully. AI gives the practitioner a tool to deconstruct it with equal precision.
For more information on how AI systems work and professional obligations in using AI in your law practice see: AI Systems for Missouri Lawyers: How They Work, What They Risk, and How to Use Them Responsibly
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