Join Siddhant Wadhwani as he traces the evolution of AI from Generative to Agentic to Quantum, showing how AI has transformed from a tool into an autonomous collaborator and co-creator.
Explore real-world applications, case studies, and high-impact use cases, from smart testing agents to self-improving assistants, and learn how to leverage AI for productivity, innovation, and enterprise-scale transformation.
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Can you provide a real-world example where an AI agent automated QA pipelines or workflow orchestration?
Which shift do you think will impact us more in the near future, agentic AI or quantum AI?
Which underlying innovation really unlocks the shift from generative models to agentic AI, extended memory, external tool use, or some more fundamental mechanism?
If generative AI is about producing answers and agentic AI is about taking action, what’s the key technical leap that enables that transition, memory, tool use, or something beyond both?
As AI agents become more autonomous, what is the single most critical safety ‘guardrail’ or ‘off-switch’ a company should implement to prevent an agent from causing unintended, large-scale problems?
What are the potential long-term societal implications, regarding areas like human decision-making, the nature of work, and the potential for a widening gap between those who benefit from AI>agentic>quantum advancements and those who may not?
What is the key technical or architectural leap that allows an AI to transition from being ‘Generative’ to truly ‘Agentic’? Is it long-term memory, the ability to use external tools, or something else entirely?
What are the specific technical hurdles and required breakthroughs in quantum hardware and software to achieve real-time molecular sims in HCI, particularly concerning qubit stability and error correction?
How can businesses best manage the cultural and organizational changes required to integrate these evolving AI workflows, especially when facing potential employee concerns about job displacement and the need for new skill sets?
Considering the need for new skill sets in an AI-powered world, how can educational institutions and workforce development programs adapt to prepare individuals for a future increasingly shaped by generative, agentic, and quantum AI?
How has AI evolved from rule-based systems to generative models, and now towards agentic and quantum approaches?
As AI agents become more autonomous, what is the single most critical safety ‘guardrail’ or ‘off-switch’ a company should implement to prevent an agent from causing unintended, large-scale problems?
What architectural shifts are needed in data pipelines to support quantum-enhanced AI, especially for optimization and cryptography?
When we move into the quantum era of AI, what kinds of problems will suddenly become solvable that today feel impossible?
Will agentic AI make “prompting” obsolete, just as the GUI killed the command line for most users?
Are we using AI to make better testing decisions or just to automate more noise?
How should organizations future-proof their data pipelines to continuously feed and retrain generative models?
What architectural patterns ensure safe orchestration of multi-agent systems in critical domains (finance, healthcare, operations)?
How can businesses best manage the cultural and organizational changes required to integrate these evolving AI workflows, especially when facing potential employee concerns about job displacement and the need for new skill sets?
