Ten of the fifteen books recommended in a Chicago Sun-Times supplement from May 18, 2025 were fake, along with their descriptions. The paper had licensed the content from King Features, a division of the Hearst media group. Its journalists neither produced nor reviewed it before it went to print. Its content operations lacked a review step before distribution.
Melissa Bell, CEO of Chicago Public Media, the paper’s parent organization, explained the failures on May 29, with a correction published on June 2. She acknowledged successive human errors and her own approval to continue the supplements. The case shows a chain of decisions; it wasn’t a system that published on its own.
Content operations brings together the people, processes and tools that plan, produce, review and publish content. Automation can prepare those tasks and execute defined steps, while someone in charge retains the decisions that affect people, business commitments or trust.
We propose five checkpoints for marketing teams and small studios. The marketing applications that follow are editorial recommendations, not business outcomes measured in the cases cited.
Choosing what deserves to be published, and with what stance
You can automate topic detection, cluster questions and pull performance data. Prioritization, business fit and editorial stance need someone in charge.
That person approves a brief with an audience, a question and expected evidence. For example, in a hypothetical scenario, a spike in searches about a tool might trigger research. Recommending it requires a basis for evaluation that the search trend alone doesn’t provide.
If you’re investing in branded media, a brand’s own media outlets, that same selection also defines which editorial promise you’ll uphold once producing another piece becomes cheap.
Deciding which claims and recommendations to stand behind
Content automation can locate possible sources, extract figures and flag contradictions. Whoever verifies the content must open the original and confirm it backs up the sentence, including its date and conditions. A working link can lead to a document that doesn’t say what the draft attributes to it.
Log the relevant claims with source, date and the name of the verifier. For a recommendation, add the basis for comparison and distinguish firsthand testing from third-party information.
After the supplement, the Sun-Times changed its policy to identify the provenance of outside content and submit it to its Standards team, responsible for editorial rules. It also pulled the section from the digital edition. A vendor delivery needs an identifiable approval before distribution.
Privacy, consent and commercial conflicts
Detectors can flag names, sensitive information and matches against an approved list of business relationships. The person in charge decides what can be disclosed, what consent is missing and what disclosure is required. The risk may justify legal review; a detector doesn’t guarantee compliance.
Hypothetical example: transcribing a call with a client makes it easy to find a quote, but that doesn’t authorize publishing it as a testimonial. The control starts before loading sensitive material into an external tool and continues before publishing.
Approving the content the audience will see
In January 2025, Apple paused news and entertainment notification summaries in developer preview versions of its operating systems. According to Aisha Malik, a TechCrunch reporter, the BBC, the British public broadcaster, had protested a summary that introduced a false claim about one of its stories.
The episode dates to January 2025. We did not verify the current status of that feature for this piece. Nor was it a decision by the BBC to automate its headlines: the summary was generated by Apple Intelligence, Apple’s AI system.
The application to the editorial workflow is concrete: also review the subject line, headline, excerpt, image and CTA. An adaptation can change a correct claim. Automate formatting, metadata and technical checks; keep an approval step over promises, nuance and context whenever there’s risk.
Low-risk tasks with stable rules can run automatically under an explicit policy, with an auditable log and the ability to stop them. There’s no need to manually review every mechanical operation.
Taking responsibility for errors and deciding on corrections
Automate alerts, version tracking and correction drafts. Assign one person to assess severity, pull content when warranted, notify those affected and approve the public explanation.
Decide in advance who can pause the workflow. If a GTM engineer connects your marketing and sales tools, they can implement the technical block; editorial responsibility still needs to be clearly assigned.
A checklist for any automation
This checklist translates the decisions into testable controls.
| Field | What to put in writing |
|---|---|
| Authorized input | What materials the workflow can use and what’s off-limits |
| Permitted task | What it can execute without expanding scope |
| Owner | Who answers for the outcome and can block it |
| Approval | What must be verified and who authorizes it |
| Pause signal | What error or uncertainty stops the next step |
| Recovery | How to restore the version, correct it and notify |
Human review fails too. Its usefulness depends on what it checks and what it can stop. If no one has the authority to block a publication, the workflow still has a decision with no owner.
Frequently asked questions
What are content operations?
Content operations bring together people, processes and tools to plan, produce, review and publish. In the workflow proposed here, automation gathers inputs and executes defined tasks. Identifiable people in charge retain decisions about stance, evidence, privacy, approval and corrections, especially when an error could affect people, business commitments or audience trust.
What shouldn’t you automate in content marketing?
In content marketing, it’s worth keeping human owners for choosing the stance, backing up claims, resolving consent and disclosures, approving final versions that carry risk, and deciding on corrections. That’s the editorial recommendation of this guide. Preparation and mechanical execution can be automated, with defined scope, a log of what was executed and a condition that lets you stop the workflow.
What happened with the Chicago Sun-Times book list?
The Chicago Sun-Times supplement from May 18, 2025 recommended fifteen books, of which ten titles and descriptions were fake. According to Melissa Bell, CEO of Chicago Public Media, it was content licensed from King Features, not produced or previously reviewed by the newsroom. The paper changed its controls over third-party material and pulled the section from its digital edition.
What does the Apple Intelligence case show about content operations?
The Apple Intelligence case shows that summarizing for distribution can alter a claim. TechCrunch reported on January 16, 2025 that Apple paused news and entertainment summaries in preview versions after false alerts. The recommendation for content operations is to also review the version the audience receives, not just the original. This piece did not verify the current status of the feature.
Does AI-assisted editorial review eliminate errors?
AI-assisted editorial review doesn’t eliminate errors. In the workflow proposed here, someone in charge verifies claims against originals, reviews the final version and resolves specific approvals. The Sun-Times case involved successive human failures, by its own account. Generative AI, rule-based automation and licensed content each require different controls depending on the task and the risk.