SUPERWISE has released V1.24.0 and announced the upcoming Starter Edition Early Access for September 15th. The blog post argues that for any organization, from startups to enterprises, AI governance is the key to sustainable growth and avoiding catastrophic failure. The platform-first approach of SUPERWISE provides governance as infrastructure, with features like runtime policy enforcement and compliance by configuration.

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Projects often fail when AI agents degrade in real-world scenarios. The core problem isn’t the model itself, but its runtime operation. This post explores how a platform-first approach operationalizes governance by embedding observability and policies directly at the runtime, ensuring that your AI deployments are reliable, auditable, and adaptable under scrutiny.

Predictive analytics promised transformation. Businesses invested in systems to forecast demand, detect fraud, and optimize logistics. But in many organizations, those deployments are sitting idle — technically “live,” but forgotten. This isn’t a failure of modeling; it’s a failure of operationalization. For technical teams, the challenge isn’t creating models, it’s keeping them healthy, relevant, and accountable over time with a platform-first approach to runtime observability.

In industrial environments, delayed, incomplete, or inaccurate data can lead to costly inefficiencies, safety risks, and operational blind spots. Whether in construction, manufacturing, or logistics, frontline teams rely on real-time, high-quality data to make informed decisions that drive productivity and safety. Yet, traditional data collection methods—paper-based forms, siloed spreadsheets, and outdated digital tools—fail to meet the demands of modern industrial operations.

In industrial environments, delayed, incomplete, or inaccurate data can lead to costly inefficiencies, safety risks, and operational blind spots. Whether in construction, manufacturing, or logistics, frontline teams rely on real-time, high-quality data to make informed decisions that drive productivity and safety. Yet, traditional data collection methods—paper-based forms, siloed spreadsheets, and outdated digital tools—fail to meet the demands of modern industrial operations.