How I AIHow AI got me 3 promotions: the ultimate guide for EAs (w/ Zapier’s EA)
CHAPTERS
- 0:00 – 1:41
Why EAs can teach AI to “think like me” (and why that matters)
Cortney and Claire open with the core mindset shift: AI can mirror an EA’s judgment if you can explain the underlying system behind your decisions. They frame the episode as highly practical—focused on reducing meeting overhead and scaling EA leverage across an organization.
- •AI can approximate an EA’s decision-making when the process is articulated clearly
- •EAs already operate like “second brains”; AI can externalize that knowledge beyond memory
- •This episode’s focus: meeting prep, meeting quality, culture reinforcement, and strategic alignment
- •Theme: automate repetitive work to reclaim time for higher-leverage, human work
- 1:41 – 2:42
Sponsor: WorkOS and the enterprise-readiness layer for AI apps
Claire explains why AI products face immediate enterprise scrutiny: deep system access requires strong security and compliance controls. WorkOS is presented as a fast path to add authentication, access controls, and audit logs without building them from scratch.
- •AI tools need deep access to internal systems to be useful
- •Enterprise buyers require strong security: auth, access control, audit logs
- •Building enterprise features in-house is costly and slow
- •WorkOS provides drop-in APIs ("Stripe for enterprise features")
- 2:42 – 4:34
Cortney’s motivation: automate the boring parts, get ahead of the curve
Cortney shares why she adopted AI early: to eliminate repetitive admin work and reshape the EA role into more interesting, higher-impact work. She emphasizes it’s not “if” but “when” EAs will need to adopt AI tools.
- •Working at Zapier helped, but the main driver is personal leverage and curiosity
- •Goal: remove manual/repetitive tasks to focus on higher-value work
- •Approach: learn by doing; iterate over time
- •Belief: AI adoption in EA work is inevitable
- 4:34 – 6:05
Weekly meeting prep agent: turning Friday planning into an automated workflow
Cortney introduces her flagship automation: a scheduled “weekly meeting prep” agent in Zapier Agents. It scans the upcoming calendar, identifies which meetings truly require prep, and assembles the context she’d normally gather manually.
- •Agent triggers on a schedule (e.g., Fridays at 8am)
- •Pulls next week’s calendar and filters to meetings needing prep
- •Focuses especially on external meetings vs routine internal ones
- •Designed to replace 1–2 hours of manual weekly prep
- 6:05 – 7:05
How the meeting prep agent does research across web, CRM, email, and Slack
Cortney breaks down the agent’s data-gathering: web research for external attendees, CRM lookups for relationship status, and internal history via email and Slack. The goal is one consolidated view instead of hunting across tools.
- •Web research for non-company emails: role, background, notable details
- •CRM enrichment (HubSpot example): deal status, sales notes, relationship history
- •Searches Gmail and Slack for prior interactions and internal references
- •Combines sources into a single prep context for faster recall
- 7:05 – 10:07
Outputs that actually change behavior: Todoist tasks + a structured Slack digest
The agent produces two concrete deliverables: meeting-specific prep tasks and a weekly Slack digest summarizing what matters. Cortney highlights the “second brain” effect—reliable context without cognitive load.
- •Creates prep tasks in Todoist and schedules them relative to meeting time
- •Posts a structured weekly digest to Slack for easy scanning
- •Includes error handling (missing matches, manual follow-ups)
- •Adds agent-generated insights and prep recommendations, not just raw data
- 10:07 – 12:16
Iteration mindset: debug the agent’s reasoning and improve it over time
Cortney shows how she inspects the agent’s step-by-step thinking, then refines it with Copilot in natural language. The key operating principle: start simple, ship, then continuously enhance.
- •Agent logs reveal exactly what it did (calendar pull, CRM lookups, failures)
- •Natural-language tweaks (e.g., add LinkedIn links) via Copilot
- •“Progress over perfection” when building automations
- •Agents improve as tools improve—keep updating workflows with new capabilities
- 12:16 – 18:13
Design principle for EAs: narrate your workflow like you’re training an intern
Claire and Cortney explain why agents are approachable: you can literally describe your steps as if teaching a new hire. They connect this to EA leverage—templates can be shared org-wide and customized.
- •Narrate the process: check calendar → email → Slack/CRM → organize → send digest → create to-dos
- •Agents as “interns”: need training and tools, then become autonomous
- •Sharing templates scales EA best practices across teams
- •EAs are uniquely positioned to systematize organizational leverage
- 18:13 – 23:13
Culture reinforcement via automated meeting feedback and coaching
Cortney introduces an “after-meeting coaching” system driven by meeting transcripts (Fathom initially, later Fellow). The automation normalizes feedback, reinforces values, and reduces the interpersonal friction of delivering coaching directly.
- •Started as manual prompting for meeting-performance feedback beyond action items
- •Automation sends participant-level coaching: what went well + growth opportunities
- •Tuned tone to be both “demanding and supportive” (company norm)
- •Includes safeguards: exclude short meetings, ensure enough context, only employees
- 23:13 – 27:54
Sponsor: Brex and autonomous agents for finance operations
Claire presents Brex as an AI-enabled finance platform where agents automate key workflows like card issuance, expense filing, and fraud prevention. The pitch emphasizes speed, control, and founder-friendly scale.
- •Brex positions itself as an intelligent finance platform for founders
- •Autonomous agents handle routine finance operations in the background
- •Claims: real-time fraud prevention, simplified expenses, scalable finance stack
- •Call to action: learn more via the show’s link
- 27:54 – 33:34
‘Exec-thinking’ document review: a GPT that stress-tests strategy memos before the meeting
Cortney shares a ChatGPT-based “exec prep GPT” used to review strategic docs posted for executive review. It helps employees tighten arguments, surface trade-offs, and align to company norms—reducing bottlenecks and improving meeting efficiency.
- •Built to avoid Cortney becoming the gatekeeper for strategy doc feedback
- •GPT gives direct feedback, suggested rewrites, and “bullpen-ready” improvements
- •Grounded in internal materials: values, strategy memos, norms, examples, leader preferences
- •Impact: scales EA and exec leverage; encourages asynchronous iteration before synchronous time
- 33:34 – 37:27
NotebookLM strategy companion: a centralized, queryable “living strategy” hub
Cortney describes a NotebookLM-based strategy knowledge base that unifies docs, all-hands content, and transcripts into an interactive system. Employees can ask questions in chat and even generate an AI “podcast” summary to stay aligned.
- •Aggregates many sources: top-level strategy, all-hands, transcripts, org action plans
- •Enables Q&A: “How does strategy affect my role?” prompts get source-grounded answers
- •Makes strategy interactive vs static; supports continuous updates over time
- •Multimodal consumption: auto-generated audio/podcast-style summaries
- 37:27 – 43:21
Lightning round: AI won’t replace great EAs—plus prompting and “AI exec clones”
Cortney addresses fears about AI replacing EAs, arguing AI primarily removes low-value busywork and unlocks more creative, relational work. She also discusses limits of “AI exec replicas,” and shares her direct, rambly prompting style for better outputs.
- •EA role expands with AI: more depth across the org, more time for high-leverage projects
- •Cortney cites real impact: three promotions by automating and scaling systems
- •Exec replication is partial: stable patterns (writing/comms) replicate better than fast-evolving judgment
- •Prompting technique: dictate/ramble, be direct, ask for reasoning, give clear corrective feedback
- 43:21 – 44:32
Where to find Cortney + the EA exec ops AI playbook
Cortney closes with ways to connect and learn from her work, including LinkedIn and an EA/exec-ops AI playbook. Claire wraps the episode with standard subscribe and review requests.
- •Connect with Cortney on LinkedIn for additional use cases and workshops
- •EA exec ops AI playbook: categories of automations and replicable ideas
- •Invitation for community feedback: what’s missing, what to build next
- •Show close: like/subscribe, podcast platforms, and howiai pod.com
