AI in Logistics and Manufacturing: Success Stories
Artificial intelligence is transforming logistics and manufacturing, boosting efficiency and cutting costs. Discover five real-world examples of successful implementation.
Read articleFront Gate: AI Innovation to Prevent Ticket Hoarding
Front Gate’s ticketing platform has implemented an AI-based solution with agentic controls to combat ticket hoarding, proving effective and reshaping the cybersecurity landscape in the industry.
Read articleHow OpenAI and Anthropic Are Transforming SMEs with AI
We explore how these leading companies are applying AI to boost productivity and strengthen governance in SMEs, through real-world cases.
Read articleOptimizing RAG in Production: A Measurable Framework
This article presents a measurable framework for optimizing RAG in production, controlling the cost, latency, and service quality.
Read articleGlobal Store Reduces Returns with AI
Global Store implemented an AI system to manage returns, reducing 20% of returns and speeding up approval processes by 30%.
Read articleAI in Corporate Sustainability: Practical Cases
Explore how companies like BBVA and IBM are using AI to improve their sustainability, optimizing inventory and supply chain processes.
Read articleThe Traps of Closed Systems: Lessons in Financial Integration
The Traps of Closed Systems: Lessons in Financial Integration In 2026, industrial artificial intelligence has stopped being a futuristic promise...
Read articleThe Evolution of the Assistant: From Response to Execution
In 2026, artificial intelligence has stopped being a support tool and has become an operational engine. It's no longer about asking a system what to do, but about delegating the execution of complex tasks. According to the new Globant report, presented on the International Day of Virtual Assistants, agentive AI is redefining the functioning of key industries such as healthcare, finance, and retail.
Read articleThe Integration of Predictive Modeling with WMS
In the 2026 logistics landscape, market uncertainty is no longer managed with static spreadsheets, but with dynamic models that learn in real-time.
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