ModelOp CTO Jim Olsen was recently quoted in VKTR’s article, “The AI Black Box Problem Is Getting Worse, Not Better,” which explores how increasingly complex AI systems are making transparency, explainability, and enterprise control more difficult.
As AI agents and automated workflows become more deeply embedded in business operations, the risks are no longer limited to a single model output. Errors can move across systems, decisions, tools, and downstream actions — often before teams have a clear view of what happened.
As Jim explained in the article:
“Cascading errors, where each system is even 95% accurate, means you encounter a 5% error rate for each decision made. Clearly, this error rate then compounds at each step.”
The article highlights a growing challenge for enterprise AI leaders: performance at the individual model or task level is not enough. As AI systems become more agentic, interconnected, and autonomous, organizations need stronger visibility into how decisions are made, where risks emerge, and how issues can be detected, audited, and controlled across the full AI lifecycle.
Enterprise AI scale requires more than experimentation. It requires a system of record that gives organizations the visibility, accountability, and control needed to manage AI as a disciplined portfolio of business systems.
