From Calls to Workflows: How a Content Engineer Uses AI to Automate GTM Work Without Writing Code

AI promises to transform how GTM teams work, yet for many people it still feels like something other teams have figured out first; while valuable insights from calls and conversations continue slipping into the void. This article shares how one content engineer, Ryan McCready, went from feeling behind to building powerful AI workflows in just six months using tools he already knew, and how you can start doing the same; without code, without overwhelm, and without waiting for someone else to bring AI into your processes.

Why GTM teams feel behind on AI, and how Ryan caught up fast

If you work in GTM, you’ve likely felt the quiet panic that you’re already behind when it comes to leveraging AI. New AI tools launch every week, peers casually mention automations they’ve built, and meanwhile much of your work still involves manual copying, updating, and stitching information together across spreadsheets and docs. That same feeling hit Ryan McCready hard after a layoff, when multiple hiring managers told him the same thing: his content and growth experience were strong, but he was missing the AI piece. So he bet on himself. 

Over six months, Ryan went from an AI skeptic to what he now calls a content engineer; not by learning to code, but by using durable platforms he already trusted. Fellow became his source of truth for AI meeting notes and meeting context. Zapier became the orchestration layer that structured and transformed information. Notion became the searchable memory where everything lived. Instead of chasing shiny tools, he built a scalable system on platforms built to last.

A major unlock came when Ryan returned to Zapier and realized it had quietly evolved from simple automation glue into a true AI workflow console. Today, Zapier can incorporate models like OpenAI and Claude directly into workflows, letting AI extract meaning, restructure data, and make decisions as information flows through the system. Copilot eliminates the blank-canvas problem by turning plain-language ideas into structured workflows. Tables and Interfaces give non-technical teams lightweight data storage and intake forms that trigger deeper automation; no engineering required.

That shift from thinking of AI as a separate tool to treating it as a reasoning layer inside tools you already use is what helped Ryan close the gap so quickly. And it’s the same mindset that turns the fear of falling behind into the confidence of building workflows that compound over time.

Where meeting insights become workflows

Ryan’s automations all ladder up to one core philosophy: conversations are full of valuable insights, but unless you intentionally capture and structure that information, it becomes fragmented and forgotten. By using Fellow as the meeting source of truth and Zapier as the AI orchestration layer, he has created repeatable workflow templates that help GTM teams turn everyday calls, messages, and ideas into searchable knowledge, seamless meeting prep, and ongoing content fuel.

Workflow #1: Turn every meeting into a searchable call database

Ryan’s foundational workflow takes calls already captured in Fellow and turns them into a structured intelligence layer in Notion. 

When a tagged call hits a specific Fellow channel, Zapier cleans the transcript, extracts summaries, questions, pain points, action items, and standout quotes using AI, and then stores that information in a Notion database.

Instead of digging through transcript archives, Ryan can search patterns, analyze themes, and trigger additional workflows from this knowledge base. It allows him to reuse meeting insights everywhere without changing how he works, just by structuring what was already happening.

Workflow #2: Capture 1:1 talking points straight from Slack

This workflow solves a daily irritation: remembering what to talk about in your next meeting.

Ryan created a dedicated Slack channel where he quickly drops thoughts and tags the relevant person. Zapier sees each message, identifies who it’s about, and automatically adds it to the correct agenda in Fellow, whether for a manager or a client.

By turning Slack into the intake surface and letting Zapier handle routing, nothing gets lost and meeting prep becomes a natural byproduct of everyday communication.

Workflow #3: Build an AI-powered content engine from your calls

For content-heavy roles, Ryan built a workflow that transforms “content-rich” conversations into pitch-ready assets. 

Calls sent to a Thought Leadership channel trigger a series of AI steps that extract golden quotes, frameworks, demo walkthroughs, and audience questions, then synthesize them into structured pitch documents stored in Notion. It accelerates creative work by turning long transcripts into actionable outlines, ensuring that the best thinking from conversations becomes fuel for future stories, social content, and educational assets.

Quick-hit workflow ideas for every GTM function

Not every automation needs to be a full content engine or a complex branching zap. Once you see the underlying pattern of cleaning the inputs with Fellow, structuring and routing the information with Zapier, you start noticing opportunities everywhere. Across Sales, CS, Finance, and Ops, the most valuable wins are often the simplest ones: repetitive tasks, text-heavy outputs, and insights that currently vanish into the void.

Sales & Customer Success workflow idea: Turn discovery calls into a shared intelligence layer

Fellow captures the full context from discovery and customer conversations (transcripts, AI notes, action items) and Zapier turns those insights into structured data. Pain points, objections, feature requests, and success stories automatically populate a shared Notion or CRM database. When the same objection starts appearing in multiple calls, the pattern surfaces quickly; fueling tighter messaging, sharper enablement, and better roadmap prioritization.

Finance workflow idea: Automate expense intake and compliance nudges

Fellow keeps track of conversations involving budget approvals or procurement updates, while Zapier handles the operational workflows that follow. Employees can upload receipts through a Zapier Interface, where an AI step extracts vendor, amount, category, and date before sending clean data into a finance system. Zapier can even cross-reference Fellow meeting notes and send Slack nudges when receipts are missing for something that was approved, removing friction without adding workload.

Operations & Leadership workflow idea: Get a daily summary of what’s happening

Fellow captures AI notes from the most important recurring meetings across teams (product reviews, pipeline syncs, standups) and Zapier compiles those notes into a leadership digest. AI highlights key decisions, risks, escalations, and blockers, then delivers a concise update into Slack or email. Leaders stay aligned without needing to sit in every room or chase updates across channels.

How to start building (even if you’re new to automation)

The biggest myth about automation is that you need to be technical to do it well. Ryan learned early on that starting with something complex like the sprawling, dozens-of-steps webinar flywheel he built as one of his first automations, only leads to frustration. 

The better path is to begin with tiny, practical workflows that follow a simple pattern: something happens, something is transformed, and something is sent. Maybe a new Fellow recap triggers a short summary into Slack, or a discovery call automatically adds a highlight to a Notion page. With these smaller, reliable wins, you learn the platform while creating visible value fast. Just as importantly, you learn to prioritize clean inputs over fancy prompts. If the transcript is structured and the task is scoped, the AI step almost always performs better.

Testing and cost efficiency also make this more accessible than most people think. Each AI step in Zapier includes a preview mode, so you can refine outputs one step at a time instead of guessing where something broke. And because Zapier includes smaller, affordable models like GPT-4.1 nano and mini, you can run these automations for pennies while you experiment. 

The takeaway: you don’t need engineering resources or a grand automation plan to get started. With Fellow as your source of truth for meeting data and Zapier orchestrating the flow, you can automate one real task right now, and that small start is how teams go from feeling behind on AI to suddenly seeing it everywhere they work.