Blogs

AI use cases in retail have moved from experimentation to actual execution. In 2026, the global AI-in-retail market is valued at roughly $16.5–$20 billion depending on scope and methodology, and is projected to grow several-fold by the early 2030s, according to Fortune Business Insights and Mordor Intelligence.  Retailers are no longer piloting AI in isolated...

Enterprise data is expanding far faster than an organization’s ability to govern its meaning. Without effective metadata management, conflicting data definitions and disconnected pipelines create a chaotic environment where no single number can be fully trusted. The commercial impact of this confusion is measurable. Dataversity’s 2025 survey reveals that 75% of data leaders report a...

Ask five leaders in the same enterprise for last quarter’s customer count and you’ll likely get five different numbers. Multiply that gap across every business unit, region, and system, and it becomes clear why enterprise data governance is no longer optional, and why so many AI and analytics initiatives stall without it. Enterprise data governance...

Every day, your organization generates more data than ever before, from customer interactions and operations to IoT sensors and cloud applications. Yet many technology leaders face a troubling paradox: despite having vast amounts of information, their teams still struggle to make confident decisions, AI initiatives stall due to poor data quality, and regulatory risks continue...

Most AI initiatives fail not because of poor algorithms, but because of broken data foundations. While your organization may already have a data pipeline in place, traditional systems built for business intelligence simply can’t handle what AI demands – real-time streams, unstructured content, vector embeddings, and continuous model retraining.  For technology leaders across Japan, Korea,...

Ask five departments in the same company for “the number of active customers” and you may get five different answers. Not because anyone is lying, but because the data was never built to agree with itself in the first place. Sales pulls from the CRM, finance pulls from the ERP, marketing pulls from a campaign...

Your sales team updates a customer record, but an hour later your support team still sees the old version. This is the core problem cloud data synchronization solves. It keeps data consistent and current across every system your business relies on. As companies expand across regions, they add cloud platforms and layer in AI-driven automation. Consequently,...

Most enterprises have deployed generative AI, but very few have gotten it to finish a job from end to end. Agentic AI systems mark the shift from AI that suggests answers to AI that plans, decides, and executes complex workflows autonomously. An agentic AI system is an orchestrated network of AI agents that pursues complex...

AI has quickly become a strategic priority for businesses pursuing digital transformation. With Odoo 19, AI is now embedded across the ERP platform, helping organizations automate routine work, improve decision-making, and operate more efficiently. But adopting AI features is only the first step. In the projects I’ve worked on, the real value of Odoo AI...

Retail data utilization is becoming a key priority for retailers seeking to improve their entire operations. By combining and analyzing data from multiple sources, retailers can better understand customer behavior, optimize store operations, improve demand forecasting, and build a stronger foundation for retail DX initiatives. Japan’s retail environment makes this urgent. The domestic BtoC e-commerce...