AI Leadership Insights: Challenges of Operationalizing GenAI

In this Techstrong AI interview, Mike Vizard speaks with Christian Lau, chief product officer at Dynamo AI, about the evolving challenges organizations face as they move from AI experimentation to large-scale deployment. Lau explains that in the early days, companies were mainly concerned with understanding AI’s risks, particularly in regulated sectors like finance and defense. Now, the focus has shifted to proving that AI systems are secure, compliant, and capable of managing risk to meet stakeholder expectations. Effective governance and risk management are seen as essential first steps to unlocking AI’s value at the foundation model and application layers.

Lau emphasizes the complexities of managing AI models over time, noting that AI systems can drift in performance due to model updates, dynamic behavior changes, or external factors. To address this, organizations must implement two layers of oversight: regular, tailored offline evaluations specific to their use cases, and real-time monitoring to catch unexpected behaviors. He stresses that AI should be treated like a product, requiring analytics on usage patterns, output failures, and user queries to maintain consistency and quality, rather than relying on generic benchmarks that may not align with business-specific needs.

Finally, the conversation turns to how companies can better match generative AI models to appropriate tasks, balancing AI’s probabilistic nature against the deterministic expectations of traditional business processes. Lau explains that while AI’s creativity is powerful for ideation and problem reframing, when consistency is critical, companies must carefully tune model parameters, enforce strict guardrails, and automate monitoring at scale. By defining clear success criteria and rigorously testing against them, businesses can ensure AI augments their workflows instead of introducing chaos.