Alive Labs

Not another agency talking about innovation. We build digital experiences, AI tools, and interactive tech that actually do something. #AI #Experential

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

#RetailTech #ComputerVision

[Alive Labs @Alive]

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

[Alive Labs @Alive]

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: https://t.co/LA5C5k9Amx #AIMarketing #MartechStack #DataFoundation

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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.

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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

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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

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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

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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: https://t.co/LA5C5k9Amx #AIstrategy #MartechData

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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

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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

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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

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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

[Alive Labs @Alive]

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

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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

[Alive Labs @Alive]

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: https://t.co/5ye64V6EYh #AgenticAI #ProductEnrichment

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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

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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

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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

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

#retail #AIinfrastructure

[Alive Labs @Alive]

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: https://t.co/5ye64V6EYh #NPILaunch #AgenticAI