The Future of AI Operations: Insights from PwC AI Managed Services

The Future of AI Operations: Insights from PwC AI Managed Services

Author: Demetrios November 14, 2025 Duration: 41:27
MLOps.community
Technology

Rani Radhakrishnan is a Principal at PwC US, leading work on AI-managed services, autonomous agents, and data-driven transformation for enterprises.


The Future of AI Operations: Insights from PwC AI Managed Services // MLOps Podcast #345 with Rani Radhakrishnan, Principal, Technology Managed Services - AI, Data Analytics and Insights at PwC US.


Huge thanks to PwC for supporting this episode!


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// Abstract

In today’s data-driven IT landscape, managing ML lifecycles and operations is converging.

On this podcast, we’ll explore how end-to-end ML lifecycle practices extend to proactive, automation-driven IT operations.

We'll discuss key MLOps concepts—CI/CD pipelines, feature stores, model monitoring—and how they power anomaly detection, event correlation, and automated remediation.


// Bio

Rani Radhakrishnan, a Principal at PwC, currently leads the AI Managed Services and Data & Insight teams in PwC US Technology Managed Services.

Rani excels at transforming data into strategic insights, driving informed decision-making, and delivering innovative solutions. Her leadership is marked by a deep understanding of emerging technologies and a commitment to leveraging them for business growth.

Rani’s ability to align and deliver AI solutions with organizational outcomes has established her as a thought leader in the industry.

Her passion for applying technology to solve tough business challenges and dedication to excellence continue to inspire her teams and help drive success for her clients in the rapidly evolving AI landscape.


// Related Links

Website: https://www.pwc.com/us/managedservices

https://www.pwc.com/us/en/tech-effect.html


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Connect with Demetrios on LinkedIn: /dpbrinkm

Connect with Rani on LinkedIn: /rani-radhakrishnan-163615


Timestamps:

[00:00] Getting to Know Rani

[01:54] Managed services

[03:50] AI usage reflection

[06:21] IT operations and MLOps

[11:23] MLOps and agent deployment

[14:35] Startup challenges in managed services

[16:50] Lift vs practicality in ML

[23:45] Scaling in production

[27:13] Data labeling effectiveness

[29:40] Sustainability considerations

[37:00] Product engineer roles

[40:21] Wrap up


Hosted by Demetrios, MLOps.community is a space for honest, meandering talks about the real work of making artificial intelligence systems actually work. This isn't about hype or theoretical papers; it's about the messy, practical, and often surprising journey of taking models from a notebook into a live environment. You'll hear from engineers and practitioners who are in the trenches, discussing the tools, the frustrations, and the occasional breakthroughs that define the day-to-day. The conversations are deliberately relaxed, covering everything from traditional machine learning pipelines to the new world of large language models and even the intangible "vibes" of team culture and process. Each episode peels back a layer on what "production" really means, whether that involves deploying a predictive service, managing an agentic system, or maintaining reliability as everything scales. Tuning into this podcast feels like grabbing a coffee with colleagues who aren't afraid to dig into the technical nitty-gritty while keeping the tone conversational and accessible. It's for anyone who builds, manages, or is just curious about the operational backbone that allows AI to deliver value, offering a grounded perspective often missing from the broader conversation.
Author: Language: en-us Episodes: 100

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