Your in-store cameras are already running. The question is whether they're just recording or actually working. #RetailTech #ComputerVision

The AI decisioning engine is the easy part to swap. The structured content layer underneath it is not. Most teams optimize in the wrong order. #AgenticMarketing #AIProductEngineering #ComposableShelf

Before your next AI tool purchase, read this. Alive Labs lays out the three foundation layers that determine whether AI performs or just automates your existing errors faster: alivelabs.io/insights/fix-yo… #AIMarketing #MartechStack #DataFoundation

Your inventory system says you're stocked. Your shelf says otherwise. Computer vision in retail stops being about counting boxes and starts being about execution. A product marked available in your system but missing from the shelf isn't a data problem, it's an operational one.

Two things happened this week that most people filed in separate folders. Daimaru Matsuzakaya started selling its in-store behavioral data as a product to tenant brands. #RetailIntelligence #PhysicalRetail

Convenience stores aren't using conversational AI to chat with customers. They're using it to know what's actually happening on the floor. There's a gap that exists in every retail operation, no matter the size. The manager thinks inventory is at X. #retail #operations

When an AI agent personalizes a journey on fragmented customer data, it gives you a confident answer built on incomplete inputs. It will not tell you fragmentation is the problem. #MarketingOps #AIreadiness

AI does not close the gap between a great demo and your actual stack. That gap is almost always a data foundation problem. New post from Alive Labs breaks down what to fix first: alivelabs.io/insights/fix-yo… #AIstrategy #MartechData

AI does not fix weak data foundations. It amplifies their consequences at speed and scale. The correct sequence: foundation first, then automation. #AIstrategy #MartechData #MarketingOps

Aramark just deployed 39 camera-based checkouts across six concession stands at Williams-Brice Stadium. Not a pilot. Not a proof of concept. A rollout. This matters because stadium concessions are the hardest test case for checkout automation. Thirty seconds between plays. Fifty

AI handles discovery. Humans handle belonging. Target's opening 600 beauty studios. Foot Locker's building sneaker community hubs. Retailers understand what's happening, they're investing in the relationship layer. #retail #experientialcommerce

Three separate acquisitions and product launches this week all arrived at the same architectural decision: keep video processed on-site, discard the person, retain only the aggregate signal. GreyOrange buying Site Bionics. #RetailAI #EdgeComputing

Inventory robots aren't about replacing shelf-stockers. They're about giving your replenishment engine ground truth. Tally rolls through a Fred Meyer aisle, camera array scanning shelf tags and product placement in real time. #retail #inventory

Most AI marketing agent deployments fail because the buyer skipped the spec work, not because the tech underperformed. Named task. Named owner. Written success metric. All three before you sign. #AIagents #MarketingOps #B2BAI

If your product data has to be manually re-entered for every system that touches a launch, you do not have a speed problem. You have an architecture problem. We wrote up what the fix looks like: alivelabs.io/insights/how-do… #AgenticAI #ProductEnrichment

Your storefront probably can't complete an AI agent purchase end-to-end. InMobi scanned 3 million US e-commerce sites. 64% failed because inventory systems don't connect to payment systems, payment systems don't connect to fulfillment, and nothing connects to the agent. This is

Your competitor's field team just became a data collection unit. Coca-Cola, AB InBev, Kraft Heinz, and their distributors are putting AI cameras in the hands of merchandisers. These aren't scanning for compliance anymore. #retail #supplychain

A typical NPI production cycle stretches 4 to 6 weeks of calendar time. The actual decision-making inside that window: a few days. The rest is waiting, re-explaining, and fixing drift between systems. #ProductOps #NPILaunch #AIAgents

Certification is how AI gets into 55,000 stores without anyone writing a press release about it. #retail #AIinfrastructure

Slow NPI launches are not a capacity problem. They are a context problem. The same product data gets re-entered for every downstream system, compounding drift with each handoff. There is a better architecture. Read how it works: alivelabs.io/insights/how-do… #NPILaunch #AgenticAI