How various industries use real-time analytics to improve the business bottom line. We explore both physical, IoT uses (such as sensor data in manufacturing, transportation, and energy) as well as use of transactional and Web data, such as in the financial services, marketing, and customer-experience management industries.
The industrial sector has always thrived on adaptation. AI, IoT, software-defined systems, and digital twins represent the next stage of that evolution.
Retailers can no longer afford to rely solely on static dashboards. The combination of conversational analytics, Gen AI, and semantic intelligence provides …
Agentic AI represents the fusion of data intelligence and operational autonomy, bringing the long-promised vision of smart, adaptive, and resilient industrial …
AI agents represent an opportunity to go beyond isolated analytics projects and move toward self-improving, autonomous workflows that directly drive …
Scaling AI in industry requires the right technology, a practical understanding of industrial operations, and open interoperability with an organization’s …
Retailers must integrate new data sources, such as real-time social sentiment, search trends, and influencer engagement metrics, into forecasting models that …
Real-time data may be one of the most underutilized tools in the retail industry. With today’s sophisticated AI-powered POS and inventory management systems, …
Manufacturers are entering a phase where AI is less about exploration and more about embedding it into the core fabric of operations.
Guardrails like pre-approved scripts and hard handoffs keep AI agents in check, ensuring outputs stay predictable instead of wandering into
Most industrial organizations now have CEO-driven industrial AI strategies, reflecting a shift from experimentation to enterprise-wide