Jaime DeLanghe: How I run a product org with AI — a CPO's playbook

Jaime DeLanghe is the Chief Product Officer at Slack, which Salesforce has been repositioning as the operational core of AI-native enterprise work. At ProductCon New York 2026 on May 20, she presented not a framework but a walkthrough of her actual daily workflow—the specific places where AI tools change what she does and how she manages a large product organization.

The talk opens with an honest description of what a CPO’s calendar looks like: back-to-back meetings, customer calls, board preparation, partner conversations, and constant context-switching between decisions at very different levels of abstraction. DeLanghe’s argument is that AI’s value in this context is not that it reduces the volume of work but that it lowers the cost of context-switching, allowing her to get up to speed on a project or a conversation faster than manual review would allow.

Who it is for

This talk is most useful for PMs and product leaders who are trying to figure out where AI fits in their own workflow—particularly those managing multiple teams or working at the director, VP, or CPO level. Engineers and founders building tools for knowledge workers managing complex portfolios of work will also find it relevant.

Key takeaways

1. The “Peek at my day” Slackbot feature DeLanghe demonstrates pulls from calendars, connected systems, and conversation history to synthesize a priority briefing each morning. The point is not the specific feature but the underlying pattern: rather than a PM manually scanning everything before the day begins, a configured AI agent does the aggregation. DeLanghe starts her day with a synthesis of what matters rather than catching up reactively.

2. She has configured project status skills in Slackbot that surface roadblocks across her portfolio automatically. Rather than relying on people to escalate problems, the system flags when a project shows signs of slippage—delays, missing owner responses, stalled decisions—without requiring anyone to report it. This shifts her role from reactive problem-solver to someone who can intervene earlier.

3. For ad hoc data analysis, DeLanghe drops a raw CSV file and uses Claude to build a dashboard or summary in minutes. The relevant point here is that she doesn’t wait for a data analyst to form a view before a meeting. At the CPO level, this changes how well-prepared she can be for a conversation where she’s the decision-maker.

4. The practices DeLanghe describes are configured systems, not one-off prompts. The value compounds precisely because these agents run on her behalf regularly. The upfront configuration cost is real, but it’s a different category from asking an AI to summarize one document.

Worth watching if

You are a product leader who suspects AI could help you manage the information load of your role but have not moved past using AI as a writing or editing assistant. DeLanghe shows what it looks like to systematically wire AI into the operations of a product organization—not just individual tasks. The talk is specific enough to take notes on and apply the same week.