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Welcome to the Impact of AI: Explored podcast. In this episode
James and Gerjon have a awesome guest: Bhaskar Barat Sawant - Lead Engineer | Solutions Architect | AI/ML & Cybersecurity | Azure Cloud | SaaS Platforms | Microsoft Certified (https://www.linkedin.com/in/bhaskar-bharat-sawant-533218122/)
In this episode, we discuss the complexities and challenges of AI adoption in enterprises, emphasizing the importance of responsible AI deployment. We explore the disconnect between vendor promises and real-world experiences, the risks associated with AI agents, and the emerging concept of federated learning. The discussion highlights the need for strong architecture, governance, and observability in AI systems to ensure security and effectiveness.
Takeaways
* Organizations prioritize speed over security in AI adoption.
* AI introduces new risks that traditional systems didn't have.
* Deploying AI requires proper architecture and governance.
* Vendors often oversimplify AI as a plug-and-play solution.
* AI is not a magic solution for every problem.
* Successful AI adoption requires careful planning and management.
* Start with a clear business problem when considering AI.
* Responsible AI involves continuous monitoring and transparency.
* AI agents need defined boundaries to operate safely.
* Federated learning allows for privacy-preserving AI training.
Support the show
By James O'Regan and Gerjon KunstWelcome to the Impact of AI: Explored podcast. In this episode
James and Gerjon have a awesome guest: Bhaskar Barat Sawant - Lead Engineer | Solutions Architect | AI/ML & Cybersecurity | Azure Cloud | SaaS Platforms | Microsoft Certified (https://www.linkedin.com/in/bhaskar-bharat-sawant-533218122/)
In this episode, we discuss the complexities and challenges of AI adoption in enterprises, emphasizing the importance of responsible AI deployment. We explore the disconnect between vendor promises and real-world experiences, the risks associated with AI agents, and the emerging concept of federated learning. The discussion highlights the need for strong architecture, governance, and observability in AI systems to ensure security and effectiveness.
Takeaways
* Organizations prioritize speed over security in AI adoption.
* AI introduces new risks that traditional systems didn't have.
* Deploying AI requires proper architecture and governance.
* Vendors often oversimplify AI as a plug-and-play solution.
* AI is not a magic solution for every problem.
* Successful AI adoption requires careful planning and management.
* Start with a clear business problem when considering AI.
* Responsible AI involves continuous monitoring and transparency.
* AI agents need defined boundaries to operate safely.
* Federated learning allows for privacy-preserving AI training.
Support the show