Dev Interrupted

Data Science & AI: Getting Buy-In and Demonstrating ROI | PagerDuty’s Sanghamitra Goswami

Jun 18, 2024

Sanghamitra Goswami, Senior Director of Data Science at PagerDuty, discusses leveraging AI in engineering teams, measuring ROI, and practical AI applications. Topics include the importance of LLMs, getting buy-in for AI, and finding use cases for AI in products.

23:28

Episode guests

Sanghamitra Goswami

AI Summary

Highlights

AI Chapters

Episode notes

Podcast summary created with Snipd AI

Quick takeaways

Establish clear goals and plans for AI development to demonstrate long-term ROI.

Prioritize understanding risks and defining ROI to successfully integrate AI within engineering teams.

Deep dives

Importance of Setting Goals and Phased Approach in AI Development

It is essential to establish clear goals and plans at each phase of AI development, even if imperfections are acknowledged. By presenting a roadmap with deliverables at each stage, large foundational work becomes more manageable. This structured approach allows for the demonstration of long-term return on investment (ROI) even if immediate results are not visible, ensuring a convincing strategy for successful AI implementation.

Pillars of Successful AI Implementation

01:28

Empowering Data Scientists for Efficient Code Production

01:29

Empowering Developers with Generative AI in Incident Management

01:23

Intro

4min

Challenges and Importance of Leveraging AI in Products

12min

Leveraging AI Ops and Early Access Programs at PagerDuty

4min

Exploring Use Cases in Data Science & AI to Solve Customer Problems

2min

Exploring AI Applications and Advice for Data Science Leaders

2min

In this episode of Dev Interrupted, Conor Bronsdon is joined by Sanghamitra Goswami, Senior Director of Data Science and Machine Learning at PagerDuty. Sanghamitra shares her expertise in AI and data science, including how engineering teams can effectively leverage both within their organizations. She also explores the history and significance of LLMs, strategies for measuring success and ROI, and the importance of foundational data work. The conversation ends with a discussion about practical applications of AI at PagerDuty, including features designed to reduce noise and improve incident resolution.

Episode Highlights:
00:56 Why are LLMs important for engineering teams to understand?
03:17 How should engineering leaders think about using AI in their products?
07:57 What sort of plan should engineering leaders have to get buy in for AI?
13:22 Are there ways to show ROI on an investment in AI?
15:08 How should we communicate with customers about AI in our products?
18:53 How can companies find a good use case for AI in their product?

Show Notes:

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Dev Interrupted

Data Science & AI: Getting Buy-In and Demonstrating ROI | PagerDuty’s Sanghamitra Goswami

Episode guests

Podcast summary created with Snipd AI

Quick takeaways

Deep dives

Importance of Setting Goals and Phased Approach in AI Development

Leveraging AI Models and Addressing Organizational Challenges

Building Customer Trust and Leveraging AI Features at PagerDuty

Pillars of Successful AI Implementation

Empowering Data Scientists for Efficient Code Production

Empowering Developers with Generative AI in Incident Management

Remember Everything You Learn from Podcasts