An automatically written earnings report and a chatbot improvising a news story may both be called “AI journalism,” but technically they can be very different systems.
That distinction matters because the failure modes are different.
The Associated Press began automating large numbers of corporate earnings briefs more than a decade ago using structured data from Zacks Investment Research and software from Automated Insights. AP described a system in which company data flowed into a story structure created with its editors. The organization said automation let it expand from roughly 300 manually written earnings stories per quarter to thousands of short reports while journalists spent more time on analysis and original reporting. AP also labeled the automated stories and described the source data behind them. See AP’s explanation of its automated earnings reporting.
The important part is not that a machine typed the sentences. It is that the range of possible sentences was constrained by structured inputs and a known reporting template.
A template transforms data
A structured system might receive revenue, earnings per share, analyst expectations, and year-over-year changes. Rules decide which facts matter and how they are expressed. If revenue rises 12 percent, the software does not need to invent a reason. It can simply report the number.
That still requires quality control. Bad source data produces bad stories. A broken rule can mislabel a gain as a loss. A missing field can produce awkward output. But the relationship between source and sentence is relatively inspectable.
Open-ended generative systems add another layer of uncertainty. They can summarize supplied material, but they can also produce details not explicitly present in the source, blend background knowledge into the answer, or invent connective explanations that sound reasonable.
That is a different problem from templating.
Automation is not one category
A box score turned into a five-paragraph game recap is mostly a transformation problem. A system asked, “Why did the team lose?” is being asked to interpret. A financial template stating that revenue fell is different from a model explaining why revenue fell without reporting, interviews, or an earnings-call transcript.
The word “automated” hides those distinctions.
Readers do not need every publication to expose its software stack, but they do benefit from knowing what kind of process produced the article. Was it generated directly from structured data? Was a human editor involved? Were claims sourced from documents? Did the system infer explanations that nobody reported?
Routine automation can be extremely useful precisely because it is narrow. The problem begins when the authority of a data-driven report is carried over to prose that has much more freedom to invent.
A machine filling a template and a machine making up the next sentence are not the same thing.