"When AI systems query inconsistent or ungoverned data, adding more model complexity doesn't contain the problem, it compounds it. Organizations across industries have acted quickly to develop agentic AI, deploying systems that analyze data, generate insights, and trigger automated workflows. In response to this trend, the AI models have adapted to react quickly via larger model parameters, increased computing power, and additional features."
"Recent TDWI research found that nearly half of respondents characterized their AI governance initiatives as either immature or very immature. This may have more to do with data lineage and the business definitions on which these models are based than with the models' capabilities."
"The AI industry tends to operate on an unexamined assumption about what drives better performance: as we build more advanced models, they will somehow self-correct their performance errors. In enterprise analytics, that assumption can fall apart quickly."
Enterprise organizations deploying agentic AI systems face critical governance challenges that cannot be resolved through model size alone. When AI systems query inconsistent or ungoverned data, increasing model parameters compounds rather than solves the problem. Recent research shows nearly half of organizations characterize their AI governance initiatives as immature. The industry operates on a flawed assumption that larger models self-correct performance errors, but this breaks down in enterprise analytics. Success requires addressing underlying data lineage issues and establishing consistent business definitions rather than relying on model complexity to deliver reliable results.
Read at TNW | Artificial-Intelligence
Unable to calculate read time
Collection
[
|
...
]