Ruby Agarwal, VP of Engineering at Avaya, shares the framework she uses to decide when AI belongs in a workflow, when regular automation is enough, and how to keep costs, reliability, and governance under control.If you’ve ever been told to “just throw AI at it,” this conversation offers a practical reality check. Ruby explains why AI decisions should start with business outcomes, how much intelligence a task truly needs, and how much authority an AI system should have before it reaches production.You’ll discover:

• The three questions to ask before using AI
• Why AI is best suited to the intelligence layer—not always routine execution
• How caps, budgets, alerts, and model defaults help control AI spend
• Why context length, retry limits, and routing rules matter
• The production guardrails every team should consider, including observability, audit logging, access boundaries, and a kill switch
  

Ruby also shares practical examples from engineering workflows, including when AI makes sense for test generation and troubleshooting—and when it becomes an expensive overreach.This episode is especially useful for CTOs, engineering leaders, platform teams, and anyone responsible for moving AI from demo to dependable production.

AI is already reshaping how CTOs build software, but the biggest wins are not where most teams expect. Rajiv Jain and AJ reveal where productivity jumps by 30% to 150%, why new builds are getting faster while legacy code still creates friction, and how the real bottleneck is shifting from writing code to judging its quality.

Rajiv Jain, founder of Thinksys, has spent 14 years scaling a 500-person engineering company that supports software development, QA, DevOps, and security. AJ, founder of Pulso People and a software engineer with 20+ years of experience, brings the operator's view from the staffing and delivery side. Together they break down how AI is changing the software development lifecycle from the inside out.

You'll discover:

• Why AI is delivering the strongest gains on greenfield development, QA automation, and maintenance of test scripts
• How teams are using story points, Jira tickets, and token budgets to measure real ROI instead of AI hype
• Why guardrails matter more in 2026 as unrestricted AI spend gives way to disciplined usage and proven return
• How agentic AI is starting to handle manual QA by reading Jira tickets, running tests, and logging bugs automatically
• Why the hardest new challenge is testing non-deterministic, LLM-powered applications
• The everyday tools both leaders actually use, from Claude workflows and Chrome extensions to Granola for meeting notes

The episode also exposes a new management problem: when AI makes it easy to add code, bloated applications and unreviewed outputs become real risks. Rajiv and AJ explain why engineering teams now need to get better at saying no, enforcing quality gates, and training people to verify AI-generated work instead of blindly trusting it.

If you're leading engineers, managing AI budgets, or trying to figure out where AI creates measurable leverage versus hidden cost, this conversation gives you the practical lens you need. Essential listening for CTOs, founders, engineering leaders, and anyone trying to turn AI from a buzzword into a durable productivity advantage.