How do you integrate ChatGPT into your automation workflows?

I see a tremendous opportunity in employing generative AI to elevate marketing automation experiences.

Here are some ways I'm currently utilizing it:

1. Creating Custom Code Actions:
Writing code is in my skill set, but it's undeniably time-consuming. With ChatGPT, I've streamlined the process by generating Node.js and Python scripts. This allows me to seamlessly incorporate them into my HubSpot workflow, saving both time and effort.

2. Email Sentiment Analysis:
We executed an email campaign, reaching out to a massive audience of 1 million people. Managing the influx of email replies manually was impractical. Leveraging ChatGPT, we automated the process by summarizing responses, extracting sentiments, and categorizing emails. The consolidated data was then pushed through Segment into Redshift for further management and analysis.

I'm curious – how have you harnessed the power of ChatGPT to supercharge your automation? Share your experiences!

I wrote that in early 2024. It feels like a lifetime ago.

Back then, the big unlock was getting ChatGPT to write a script you could paste into a workflow. That was genuinely exciting. You described what you needed, it gave you working code, and you saved a few hours. But that was still you as the bottleneck. You were the one copying, pasting, testing, and deploying.

The question has changed completely. It is no longer about integrating ChatGPT into your workflows. It is about letting autonomous agents own the workflows entirely.

I run an AI agent now that monitors our systems, drafts my daily briefings, investigates production errors, and generates reports across multiple data sources. It does not ask me for permission on each step. It just runs. The shift from AI-as-tool to AI-as-operator happened faster than anyone predicted.

If you are still manually wiring up AI into individual workflow steps, you are solving last year's problem. The real leverage is designing systems where agents handle the entire loop. You define the intent, set the guardrails, and let them execute. That is the new automation.

Here is what that looks like in practice. I have a production engineer agent that runs on a cron schedule. It checks deployment health, scans for failed builds, reviews open pull requests, and triages alerts from Sentry. When it finds something actionable, it either fixes it directly or files a detailed report with root cause analysis. No human touched it. I wake up, read the summary, and move on to higher-leverage work. That is not a parlor trick. That is a fundamentally different operating model for engineering teams.

The mistake most people make is treating AI agents like smarter scripts. They are not. Scripts execute a fixed sequence. Agents reason about what to do next based on what they observe. That distinction matters because it means you stop designing workflows step by step and start designing outcomes. You tell the agent what good looks like, give it access to the tools it needs, and let it figure out the path. The result is systems that adapt when something unexpected happens instead of failing silently at step four of a twelve-step Zap.

If you want to start making this shift, pick one workflow that currently requires you to glue things together manually. Something where you are the middleware between two systems. Replace yourself with an agent that has clear inputs, defined tools, and a success criteria. Run it for a week and measure what happens. You will not go back. The gap between AI-assisted and AI-operated is the gap between saving an hour a day and reclaiming entire categories of work.

Related: I Built a Brain with Claude Code and Leveraging AI for Problem-Solving.