AI product recommendations work when they solve real problems | Week 12 to 18 Oct 26

This week shows two sides of how AI gets deployed in real businesses—one that nailed a specific outcome, and one where the vendor's legal safety mattered more than the customer's operational trust. Both offer direct lessons for how you think about buying and building AI systems.

The win this week

DoorDash faced a straightforward problem: grocery shoppers were ordering too little per transaction. The company deployed AI to optimize product recommendations and the ordering experience for grocery purchases, and the results speak for themselves—50% larger baskets per order.

What makes this win concrete is that DoorDash measured something real: basket size. Not engagement metrics, not app opens, not "customer satisfaction." The company identified a friction point (shoppers leaving money on the table), applied AI to smooth it out, and watched the behavior change in proportion to the effort. That's not luck; it's specificity.

(Source: Supermarket News, 23 Sept 2026)

The lesson this week

Elgin Community College installed six Flock Safety cameras on campus in April 2025 for security. By September, the school's administration had identified serious problems: Flock was sharing camera data with Customs and Border Patrol without clear consent, the feeds were not properly secured, and both law enforcement and Flock employees were abusing access to the footage. In October, the college requested that Flock remove the cameras.

Flock refused. The company stated it had not violated its contract.

The school's concerns—uncontrolled data-sharing, weak access controls, documented misuse by staff and law enforcement—stayed unresolved. Flock's legal position (contract compliance) and Elgin's operational safety (who can see what, and who is using it) were not the same thing.

(Source: The Verge, 11 Oct 2026)

What to do about it

DoorDash's AI win worked because it solved a specific, measurable problem rather than chasing a vague metric. The recommendation engine matched product suggestions to what shoppers actually wanted to buy next. This scales down immediately: audit whether your product recommendations are solving for customer intent or for business convenience. If it's the latter, the numbers might look good until customer trust erodes.

Flock's rejection teaches something harder: once AI or surveillance infrastructure is deployed, exit clauses matter as much as entry terms. A vendor's legal position is not the same as your operational safety. "We didn't break the contract" is not a valid response to "we can no longer trust your system." Before you buy into any vendor's AI—whether it's a camera network, a data-sharing platform, or a recommendation engine—assume that you will eventually need to leave. Read the removal clause as carefully as you read the service terms. If the vendor can keep their infrastructure on your premises against your will, you don't actually own the decision to use it.

Disclaimer

This article is based on LinkFeed Issue 47 (12 Oct to 18 Oct 2026) — two verified case studies (one AI win, one AI failure). Sources: AI Win: Supermarket News, 23 Sept 2026 · Lesson: The Verge, 11 Oct 2026. For informational purposes only; verify critical claims at the source.

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