In today’s boardrooms, decisions are increasingly influenced by digital transformation and the ability to translate data into strategy. Yet many enterprises still struggle to bridge the gap between business needs and practical AI implementations.

In this demo-driven session, I will showcase how Python can be used to rapidly prototype custom AI workflows tailored to enterprise contexts. Using a social media sentiment analysis prototype as the case study, we will walk through framing the business problem, building the proof-of-concept with Python libraries, and delivering outputs that matter to leadership—such as daily sentiment breakdowns, risk theme detection, and exportable executive summaries.

Attendees will learn a practical approach to moving from idea → prototype → business impact using open-source tools, with examples they can adapt for their own organizations.

Format: Talk + Live Demo

Audience Takeaways:

1. How to translate business pain points into Python AI prototypes

2. A practical workflow for experimenting with sentiment analysis in an enterprise setting

3. Lessons on communicating AI insights to non-technical leadership

Behind the scenes, this demo runs on Python as the main programming language. It connects to a "database" or uses a file as the data source. It uses Google’s Gemini and PaLM AI models to analyse and generate text, and links with Gmail to automatically send out results and reports.

Preview of the slides: https://docs.google.com/presentation/d/1TufvOnWPHHKoB9tUXSNcj1CV_nnB680krLcBNwUNoF4/edit?usp=sharing