MLOps for Program Managers: A Non-Technical Field Guide As artificial intelligence (AI) initiatives move from experimentation into production, many program managers find themselves repeatedly hearing the term “MLOps” without receiving a clear or practical explanation of what it means. In meetings, MLOps is often discussed as a technical capability owned by data science or engineering…
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EU AI Act, NIST AI RMF and ISO/IEC 42001: A Plain English Comparison Security practitioners, governance/risk/compliance leaders, internal auditors, risk managers, and executives need to navigate a thicket of emerging artificial intelligence (AI) regulations and standards. The European Union’s Artificial Intelligence Act (EU AI Act), the U.S. National Institute of Standards and Technology’s AI Risk Management…
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Boards are being asked to oversee artificial intelligence (AI) without the signals they need to do it well. Most AI reporting still focuses on performance factors, including accuracy, adoption, and cost savings. These metrics matter operationally, but they do not answer the questions boards are responsible for answering. That includes who owns the risk, who…
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Most enterprise artificial intelligence (AI) initiatives do not fail because the technology is flawed. They fail because teams do not always align on what the program is. AI work often begins with pilots. A team experiments. Early results look promising. Momentum builds. Funding increases. At no point does anyone pause to define a shared operating…
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