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OpenAI has released a new framework for tracking, investigating, and disclosing misalignment in its own models. The OpenAI team announced it on X alongside 6 detailed incident reports. The framework sets criteria and deadlines for public disclosure. It applies even when OpenAI has not fully explained or mitigated the behavior. Why OpenAI Built It OpenAI’s […]
Open-source technologies have become a standard part of modern analytics, data science and AI. Organizations across regulated industries are adopting tools like Python to accelerate innovation while building more flexible analytics environments. But adopting open-source technologies introduces a new challenge. As organizations modernize, they must also maintain the governance, transparency […] The post What the
What the CMS HCC transition means for the future of open analytics Read More »
Why it matters: AI data readiness assessment framework: score your data across 5 dimensions, fix gaps, and stop AI projects from failing on bad data.
The regulatory submission process in life sciences is becoming less about assembling documents and more about proving trust. For years, submission readiness was largely treated as an end-stage operational milestone: finalize the analysis, validate the outputs and package everything for regulators. But that model is beginning to break down under […] The post Steps to
Steps to building regulatory readiness for the next wave of clinical submissions Read More »
The failure mode for enterprise AI in 2026 is not what most people expected. It is not that the models are wrong, or that agents cannot reason, or that the technology is overhyped. The failure mode is that the data feeding those systems is fragmented, inconsistently labelled, and spread across dozens of applications that were
Boomi calls it “data activation” and says it’s the missing step in every AI deployment Read More »
Much of the current focus on AI safety has centred on models – how they are trained and monitored. But as systems become more autonomous, attention is changing toward the data those systems depend on. If the data feeding an AI system is fragmented, outdated, or lacks oversight, the system’s behaviour can become more unpredictable.
Heavy industry relies on people to inspect hazardous, dirty facilities. It’s expensive, and putting humans in these zones carries obvious safety risks. Swiss robot maker ANYbotics and software company SAP are trying to change that. ANYbotics’ four-legged autonomous robots will be connected straight into SAP’s backend enterprise resource planning software. Instead of treating a robot
SAP and ANYbotics drive industrial adoption of physical AI Read More »
Responsible innovation is more than a framework. It’s the discipline of building technology that earns trust. As AI becomes more capable and deeply embedded in business workflows, organizations must ensure that every decision, design choice, and workflow supports accountability, transparency and human-centered outcomes. The Responsible Innovator Spotlight is a new […] The post Responsible innovator
Responsible innovator spotlight: Sierra Shell on designing responsibly Read More »
En un mundo hiperconectado, donde los pagos digitales y las transacciones online crecen a un ritmo vertiginoso, el fraude financiero y el incumplimiento normativo se han convertido en desafíos críticos para las organizaciones. Según la ACFE, las empresas pierden en promedio el 5% de sus ingresos anuales por fraude, lo […] The post Decidir para
Decidir para vencer: cómo transformar la analítica en decisiones que protegen tu negocio Read More »
