What role do cloud-native architectures and distributed computing play in building scalable AI systems?
What are the biggest bottlenecks in scaling AI infrastructure—compute, storage, or governance—and how do you see them evolving?
How do organizations balance experimentation with production stability in AI deployments?
How should orgs plan to bridge the skills gap in specialized AI infrastructure management and development within the team?
From your experience, how do you communicate AI reliability trade-offs to non-technical stakeholders for trust-building?
What advice would you give to organizations just beginning their AI scaling journey?
How can bias be detected and mitigated when models are continuously retrained with large-scale real-world data?
What are the main challenges in scaling AI infrastructure while ensuring data integrity and reliability?
What ethical guidelines and frameworks should be established regarding how teams are trained to address potential biases and ensure fair and non-discriminatory outcomes from the deployed AI systems?
What are the biggest infrastructure challenges when scaling AI across an enterprise?
How do you balance speed, cost, and reliability in AI system design?
If AI at scale were a superhero team, who’s the MVP — Infrastructure, Integrity, or Innovation?
How will the organization make sure of the explainability and transparency of large-scale AI models, especially in high-stakes decision-making scenarios where understanding the model’s rationale is critical?
What are your best practices for ensuring data integrity at scale?
How should teams approach security for AI systems at scale, particularly in safeguarding against adversarial attacks and ensuring the robustness of deployed models?
How do you detect and prevent AI model drift over time?
How do you design AI infrastructure that can scale seamlessly without becoming cost-prohibitive?
In agentic AI, what has been your approach to testing decision boundaries where safety and autonomy collide?
How do organizations balance rapid AI innovation with operational reliability?
How do you foster collaboration between ML engineers, data teams, and operations for large-scale AI projects?