“AI should help you notice trouble before your customers have to prove it. A small warning deserves attention when the cost of missing it is large.”

A customer says a charging cable smells strange. Another reports that its connector became too hot to touch. A third returns one with the comment, “Stopped working after melting the edge of my desk.” Consider these as hypothetical reports arriving through different channels. Each might receive a refund and disappear into a service record. Together, they warrant a product-safety investigation.

For the October 24, 2026 edition of Navigating the AI Revolution, this is a timely executive concern. Holiday promotions are approaching, and a product defect can reach more households as sales accelerate. The opportunity is not to have AI declare products safe or unsafe. It is to help your business recognize scattered warning signs early enough for qualified people to investigate.

Many organizations summarize complaints by frequency: late delivery, wrong color, damaged packaging. That is useful for service improvement, but safety operates on a different logic. A rare report of injury or overheating may deserve more attention than hundreds of complaints about presentation. An AI system instructed to summarize the most common issues could faithfully deliver the wrong management picture. Your executive decision is whether safety detection receives its own purpose, resources, and review process rather than remaining a footnote in customer sentiment.

Start with one product family where a missed defect could cause meaningful harm. Ask your product-safety or quality leader to define the warning signs worth examining. For a small electrical appliance, these might include smoke, scorching, electric shock, or unexpected activation. Include ordinary customer language: someone may write “it bit me” rather than “electric shock.” AI can help recognize related descriptions, but specialists must establish what those descriptions might mean.

Build an evidence packet, not just an alert. Every flagged pattern should link to the original reports and preserve available model numbers, purchase dates, batch identifiers, and descriptions of use. Distinguish a customer’s words from the system’s interpretation. Remove unnecessary personal information from the analysis. If identifying details are missing, the next step may be a careful customer follow-up, not a confident conclusion about an entire product line.

The charging-cable example also illustrates why counting mentions is insufficient. Three reports might describe three separate incidents, or one incident copied across a retailer review, a support ticket, and social media. A surge might follow increased sales rather than a higher failure rate. Conversely, a small number of reports from a newly shipped batch could be important. Have investigators check duplication, sales exposure, product identity, and severity before treating a pattern as evidence of a shared defect.

Before launch, test the process against past complaints that your specialists have already assessed. Mix serious incidents with harmless descriptions and ambiguous cases. Can it find a dangerous report buried among routine returns? Does it overlook unfamiliar wording? How much investigator time do false alarms consume? Do not accept a single overall accuracy percentage: a system can look impressive by correctly dismissing thousands of routine complaints while missing the few that matter most.

For the remainder of October, run a retrospective exercise on that product family. In early November, introduce daily review of new flags alongside—not instead of—existing safety reporting. Name a qualified reviewer and a backup, and establish response deadlines based on potential severity. Give that team access to engineering, suppliers, and legal counsel. Existing duties to investigate, report, warn, or withdraw products remain in force; an AI assessment must never become a reason to delay them.

Track time from the first available warning to specialist review, serious cases missed in testing, and the investigation burden created by weak alerts. These measures reveal whether the process improves your ability to protect customers. As I emphasize in The AI Revolution: Leveraging AI for Business Success, implementation must serve a business purpose. Here, success is not a more polished complaint summary. It is a better chance of noticing a preventable harm before holiday volume makes it larger.