Part 4/12:
2. Reliability: Building a Trustworthy Foundation
Reliability centers around data quality, model robustness, and platform stability. High-quality data is fundamental; without it, AI systems risk hallucinations, inaccuracies, or biases. Grounding models with the right data pipelines, ensuring clean and representative data flows, and selecting suitable models tailored to specific use cases are essential steps.
Organizations must also adopt flexible and open architectures to adapt to rapidly changing AI landscapes—embracing advancements like multi-agent models and regional-specific offerings—while maintaining a reliable backbone that prevents obsolescence or bias.