Jayesh

Jayesh Govindarajan runs the 700-person org behind Salesforce AI and Agentforce. He breaks down what an AI native company looks like layer by layer, from the tools his teams live in to how the product trio is converging. He also defines the ADLC, the agent development life cycle that replaces the SDLC once your software is an agent. Full Writeup: Transcript: Timestamps 02:19 - Inside Salesforce's AI org and how agentic coding changed it 07:33 - Ads 09:30 - How to pick and roll out AI tools 12:56 - Headless Salesforce inside Claude 15:14 - Getting ground truth from probabilistic AI 19:22 - Why Slack is the AI native work hub 21:25 - Ads 24:51 - Prototyping with customers inside Slack 25:45 - The future of the product trio 34:15 - The two AI PM skills and how to hire for them 38:00 - ADLC vs SDLC 46:36 - Roadmapping with forward deployed teams 52:29 - Biggest mistakes in AI transformations 🏆 Thanks to our sponsors 1. Fingerprint: Identify good and bad visitors with industry-leading accuracy - https://fingerprint.com/?utm_source=aakash-gupta&utm_medium=youtube 2. Ariso: Ship AI agents and features faster, with fewer regressions - https://ariso.ai/aakash 3. Customer.io: Send smarter messages using your product data - http://customer.io/productgrowth 4. Land your next PM role: Coached live by PM leaders who've hired at Meta, Uber, Microsoft, etc - https://www.landpmjob.com/?utm_source=podcast&utm_medium=podcast&utm_campaign 5. Product Faculty: Get $150 off their #1 AI PM Certification via code AAKASH150 - https://www.productfaculty.com/?code=AAKASH150 Key Takeaways 1. Manage agents instead of doing the work - The biggest change in Jayesh's org is mental. People stopped asking how to do the work by hand. They now ask how to manage a team of agents that does it. 2. Keep tool choice open - Salesforce started on Cursor, then engineers moved to Claude Code. Clear every tool through security first, then let teams pick. Real usage data tells you what to back next. 3. The value was never the UI - Going headless means Salesforce's business logic lives in the API. Customers pull decades of tuned workflows into Claude through skills and build whatever interface they need on top. 4. Let deterministic tools hold the ground truth - An LLM can hand you two different pipeline numbers. Agent Script turns a plain English spec into tool calls and context lookups, so the model orchestrates tested APIs instead of writing fresh code. Hallucination drops sharply. 5. Go eval-first - Jayesh's data science teams were eval native from day one. Now his engineering teams are catching up with custom scorers tied to outcomes and built for each specific agent. 6. Make Slack the context layer - Slack is where work happens asynchronously, and that history is gold dust for agents. A PM can mock up a prototype in a Slack thread with a customer and get feedback before a PRD exists. 7. The product trio lines are blurring - Engineers now put prototypes in front of customers directly. PMs no longer wait on engineering. Team ratios have not shifted yet, but work is bleeding across roles. 8. AI PMs need prototyping and evals - Jayesh agreed these are the two must-have skills. When he hires, he looks first for agency, then for craftsmanship that refuses AI slop and turns skepticism into failing test cases. 9. Adopt the ADLC - The agent development life cycle covers how you build, test, evaluate, deploy and observe an agent. The model underneath keeps changing, so without test cases and evals you are too afraid of breaking things to ship. Salesforce's optimization agent now watches every live agent for drift. 10. Start simple, then go wall to wall - Define value and an ROI metric before you build. Minimize variables on the first implementation and get one win. One of Europe's largest recruiting firms started with text agents in 17 languages and is now moving to voice. 👨‍💻 Where to find Jayesh Govindarajan LinkedIn: https://www.linkedin.com/in/jayeshg/ 👨‍💻 Where to find Aakash Twitter: https://x.com/aakashgupta LinkedIn: https://www.linkedin.com/in/aagupta/ Newsletter: https://www.news.aakashg.com Apple Podcasts: https://podcasts.apple.com/us/podcast/the-growth-podcast/id1763555775 Spotify: https://open.spotify.com/show/7vVEMqCSKb7I7xPk8xZtg5 🧠 About Product Growth The world's largest podcast focused solely on product + growth, with over 200K+ listeners. 🔔 Subscribe and turn on notifications.

Aakash GuptahostJayesh Govindarajanguest

Oct 29, 202657mWatch on YouTube ↗

EPISODE INFO

Released: October 29, 2026
Duration: 57m
Channel: Aakash Gupta
Watch on YouTube: ▶ Open ↗

EPISODE DESCRIPTION

Jayesh Govindarajan runs the 700-person org behind Salesforce AI and Agentforce. He breaks down what an AI native company looks like layer by layer, from the tools his teams live in to how the product trio is converging. He also defines the ADLC, the agent development life cycle that replaces the SDLC once your software is an agent. Full Writeup: Transcript: Timestamps 02:19 - Inside Salesforce's AI org and how agentic coding changed it 07:33 - Ads 09:30 - How to pick and roll out AI tools 12:56 - Headless Salesforce inside Claude 15:14 - Getting ground truth from probabilistic AI 19:22 - Why Slack is the AI native work hub 21:25 - Ads 24:51 - Prototyping with customers inside Slack 25:45 - The future of the product trio 34:15 - The two AI PM skills and how to hire for them 38:00 - ADLC vs SDLC 46:36 - Roadmapping with forward deployed teams 52:29 - Biggest mistakes in AI transformations 🏆 Thanks to our sponsors
Fingerprint: Identify good and bad visitors with industry-leading accuracy - https://fingerprint.com/?utm_source=aakash-gupta&utm_medium=youtube
Ariso: Ship AI agents and features faster, with fewer regressions - https://ariso.ai/aakash
Customer.io: Send smarter messages using your product data - http://customer.io/productgrowth
Land your next PM role: Coached live by PM leaders who've hired at Meta, Uber, Microsoft, etc - https://www.landpmjob.com/?utm_source=podcast&utm_medium=podcast&utm_campaign
Product Faculty: Get $150 off their #1 AI PM Certification via code AAKASH150 - https://www.productfaculty.com/?code=AAKASH150
Key Takeaways
1. Manage agents instead of doing the work - The biggest change in Jayesh's org is mental. People stopped asking how to do the work by hand. They now ask how to manage a team of agents that does it.
1. Keep tool choice open - Salesforce started on Cursor, then engineers moved to Claude Code. Clear every tool through security first, then let teams pick. Real usage data tells you what to back next.
1. The value was never the UI - Going headless means Salesforce's business logic lives in the API. Customers pull decades of tuned workflows into Claude through skills and build whatever interface they need on top.
1. Let deterministic tools hold the ground truth - An LLM can hand you two different pipeline numbers. Agent Script turns a plain English spec into tool calls and context lookups, so the model orchestrates tested APIs instead of writing fresh code. Hallucination drops sharply.
1. Go eval-first - Jayesh's data science teams were eval native from day one. Now his engineering teams are catching up with custom scorers tied to outcomes and built for each specific agent.
1. Make Slack the context layer - Slack is where work happens asynchronously, and that history is gold dust for agents. A PM can mock up a prototype in a Slack thread with a customer and get feedback before a PRD exists.
1. The product trio lines are blurring - Engineers now put prototypes in front of customers directly. PMs no longer wait on engineering. Team ratios have not shifted yet, but work is bleeding across roles.
1. AI PMs need prototyping and evals - Jayesh agreed these are the two must-have skills. When he hires, he looks first for agency, then for craftsmanship that refuses AI slop and turns skepticism into failing test cases.
1. Adopt the ADLC - The agent development life cycle covers how you build, test, evaluate, deploy and observe an agent. The model underneath keeps changing, so without test cases and evals you are too afraid of breaking things to ship. Salesforce's optimization agent now watches every live agent for drift.
1. Start simple, then go wall to wall - Define value and an ROI metric before you build. Minimize variables on the first implementation and get one win. One of Europe's largest recruiting firms started with text agents in 17 languages and is now moving to voice. 👨‍💻 Where to find Jayesh Govindarajan LinkedIn: https://www.linkedin.com/in/jayeshg/ 👨‍💻 Where to find Aakash Twitter: https://x.com/aakashgupta LinkedIn: https://www.linkedin.com/in/aagupta/ Newsletter: https://www.news.aakashg.com Apple Podcasts: https://podcasts.apple.com/us/podcast/the-growth-podcast/id1763555775 Spotify: https://open.spotify.com/show/7vVEMqCSKb7I7xPk8xZtg5 🧠 About Product Growth The world's largest podcast focused solely on product + growth, with over 200K+ listeners. 🔔 Subscribe and turn on notifications.

SPEAKERS

Aakash Gupta
host
Host of “Product Growth with Aakash Gupta,” sharing product growth/AI PM insights and running related courses and newsletters.
Jayesh Govindarajan
guest
Leads the broader AI engineering organization at Salesforce, covering predictive, generative, and agentic AI (including Agentforce).

EPISODE SUMMARY

In this episode of Aakash Gupta, featuring Aakash Gupta and Jayesh Govindarajan, Jayesh explores how Salesforce builds AI-native teams with ADLC, evals, and Slack Jayesh Govindarajan describes how Salesforce’s AI engineering org builds and operates predictive, generative, and agentic AI across the Salesforce stack, with Agentforce as a foundation customers use to build their own agents.

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