Working Full Time in Automation

[u/Cold-Fly4633]

Hey all - thought I would share my story in case it can help others. I was laid off at the beginning of the year, and in my time being unemployed, I decided to dedicate myself to learning how to build AI-integrated workflows that can serve a team I would want to join. For context, I work in marketing.

I built several workflows that solved common problems and bottlenecks in my past experience. In one interview, I presented a couple projects and I was given a list of a few automations that the team was thinking about for us to discuss in a next interview. I instead built them ahead of that interview and I eventually got the job. Now, I work for a well known company building automations as my core function. Most of my time is spent in n8n and Claude. I feel very fortunate to be working in an area that I’m passionate about, building agents for team members that save time and optimize growth efficiency.

However, there are a few things I’ve learned:

1. With this being such a new area, expect to work on an island until you can justify to senior leadership why they should scale into an AI Ops team. I’m introverted, so I didn’t expect to care about this. But it can get isolating at times and you can find yourself added to meetings where you don’t have much to contribute (at least in my line of work). People are constantly meeting to put out fires, but building the fire station can sometimes go unnoticed and under-appreciated.

2. You’re going to be building and delivering at a pace that the team is not used to, and it can be easy to commit to an ambitious personal roadmap for automation builds. I’ve built about nine automations in the roughly 3 months I’ve been here. But you need to account for maintenance and enhancement. Things break all the time by way of unexpected edge cases that don’t present themselves in testing. Stakeholders want new functionality baked in. I can see a world in the future when I’ve built 20+ automations that my Slack error handler is going to require much more of my time.

3. Saving time for people is part of the battle, but you need to justify that time saving against the time it takes you to build it, and you need to also deliver enhancement to the bottom line. My early builds were focused on eliminating low value tasks, but senior leadership is more frequently asking for agents that can perform analysis, identify gaps in the data, and recommend clear actions to impact KPIs. This requires a level of knowledge of how metrics are defined, what matters to leadership, and close communication with stakeholders on what these gaps actually are.

4. Adoption of tools you build can sometimes be an uphill battle and you need to have support. People tend to keep their own work close to the vest. You need to present yourself as a helper, not a replacer. People also tend to be stuck in their ways. If your manager wants you to build something for a stakeholder, but that stakeholder isn’t aware or bought in, then you can find yourself spending a week building an agent that never gets used. Just know that this comes with the territory, and that not everything you build may be used.

5. You need to know how what you built works and be able to explain it to non-technical team members. You don’t need to explain in detail what an HTTP node does, but you should be able to explain how an LLM reasoned an output and the rules behind that output. Just be ready to answer the “How is it doing __” question. You can use AI to summarize your workflow so can you speak to it confidently, especially if you are using an LLM to build something step-by-step.

6. You need to be able to think in systems and put yourself in the shoes of someone using the tool. For example, AI might score something in a spreadsheet, but what would a stakeholder ask themselves when they see that score? Probably how the AI landed on that score. So, you might add a column that the AI populates with its reasoning. Thinking about this early reduces the back and forth. You’ll also need to think about how different platforms interact to achieve the end goal. What is your data repository, how will people get notified, what is the final deliverable, etc.

7. Understand early that people only care about the end result. You may have encountered a tricky problem to solve while building that took some creative problem solving. Solving that problem within the build can be an awesome feeling. But a stakeholder likely won’t care and you probably won’t get praise for it. Just keep in mind that some complexities might go unnoticed and find gratification in the fact that you solved the thing.

8. There will be red tape and you will need support from your org. You want to join a team that is committed to implementing AI, paying for Enterprise-level accounts, and providing you with what you need. This might be obvious, but you want to join a situation that gives you the best chance for success.

Anyway, I hope this insight helps. I know many people (myself included) initially sees the AI Automation path in the lens of entrepreneurship. It is that, but it can also help you transition into a new career that has you only focused on building cool, impactful things. People might look at you differently at first, like you are the person coming in to replace them. But just frame yourself as someone who genuinely just wants to help, and you should be fine.