From Data Quality to Agentic AI: What Works in Practice

Podcasts

5 Mar

This episode explores what organizations need to get right with their data, the difference between automation and generative AI, and how agentic systems can support real workflows. We break down practical steps for readiness, governance, and using tools like MCP servers and multi-agent models effectively.

What You Will Learn:

AI is only as good as the data behind it.
Generative AI does not replace classic data science.
Unstructured data is a major (and risky) part of the AI landscape.
AI should be treated as a copilot, not an autonomous decision-maker.
Good data quality matters more than large data volumes.
AI agents require governance and visibility.
Not every problem needs Generative AI.
Bottom-up ideas drive successful AI adoption.
Multi-agent systems introduce new risks.
Education is the first layer of AI governance.

Guest bio:
With over a decade of experience, Sammy designs and implements data‑driven AI solutions across finance, healthcare, and sustainability. His current focus is on Generative AI, where he builds scalable architectures, optimises data pipelines, and translates complex AI capabilities into actionable business insights.

As a six‑time Microsoft AI MVP, Sammy is committed to ethical AI practices and responsible AI adoption. Beyond his technical work, he is deeply passionate about AI education, sharing knowledge at international conferences, webinars, and workshops to inspire the next generation of innovators.