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Ray Beharry on Muck Rack

Ray Beharry

As seen in: Entrepreneur, Business 2 Community, MarketingProfs, CMSWire, Memeburn, Total Retail, Luxury Daily, Martech Zone, MC Press Online

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Articles

Retrieval is not reasoning: What agents actually need from your data

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By Ray Beharry
| ibm.com
A global manufacturer deploys an AI agent to optimize inventory allocation across its distribution network. In testing, the system performs well. It analyzes demand forecasts, inventory levels and shipment data, identifying opportunities to rebalance stock and reduce delays. The results are strong enough to justify a broader rollout. But once the system is connected to live operations, limitations begin to surface.
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The context gap: Why AI systems fail in the real world

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By Ray Beharry
| ibm.com
A global retailer deploys an AI agent to optimize pricing. In testing, the system performs well, analyzing historical sales, inventory and market signals and generating pricing recommendations in almost real time. The results are strong enough to justify a broader rollout. However, once the system is connected to live operations, problems begin to surface. Pricing is applied inconsistently across regions. Contractual constraints are overlooked.
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Why most enterprise AI projects stall before they scale

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By Ray Beharry
| MC Press Online
A global bank deploys an AI agent to support regulatory reporting. It retrieves financial data, generates reports and surfaces insights faster than any manual process. The results are promising enough that leadership begins planning a broader rollout. By Ray Beharry But the system never scales. It still depends on curated datasets maintained by a small team. Outputs require manual validation before they can be used.
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