I came back to work this July after more than a year of parental leave. I expected to feel behind on Slack threads. I did not expect to feel behind on my own job title.
While I was gone, someone drafted my new job description. I opened it, and my stomach dropped. Not from the workload. “Social Media Strategist” had quietly become “Social Media AI Visibility Strategist,” and I did not recognize half of what was on the page.
Was that easy to sit with? Not even close.
I already knew AI. Just not like this
AI was not new to me before my leave. I had already worked with it, just not at this scale, and not with these results. What changed is how deeply it is now woven into the tools my team at Coevera uses every day, including the one helping me write this article, and how far our own CRM has grown around it. I thought I knew our product inside out. I had to relearn parts of it.
The real shock was not the tool. It was the role
Before my leave, my job as a social media strategist at Coevera looked like this:
- Planning and writing daily posts across LinkedIn, Facebook, and X for a team spread across Austria, Slovakia, the US, and the UK
- Coordinating timing with sales so a launch or milestone landed when the right audience was actually awake
- Reading analytics to understand what really worked, not just what felt good to post
- Keeping tone consistent across four countries and time zones
- Syncing with the newsletter team so posts and email content matched
- Building a voice and an audience, post by post
Today, a meaningful part of it looks like this instead:
- Checking whether ChatGPT, Perplexity, Gemini, or Google AI Overviews mention Coevera at all when someone asks a category question with zero prompting, and if they don’t yet, figuring out what needs to change so they eventually do
- Making sure “Coevera, formerly Pipeliner CRM” reads identically on LinkedIn, G2, YouTube, Crunchbase, Wikipedia, and every social bio
- Contributing authentically on Reddit and Quora, because AI systems cite these platforms heavily, and never faking it
- Messaging colleagues by name for a reaction, instead of one broadcast to the whole channel
- Valuing one verified customer review over one well-performing post
- Building a weekly tracker from scratch so decisions run on real numbers, not gut feeling
- Working out which reactions come from actual customers and which come from the same five colleagues liking everything
The work did not get smaller with AI. A second, harder layer got added underneath it.
AI will not think for you
It makes you faster. It does not make the decisions for you. If anything, the job got more demanding, not less. The tools only produce something useful once you bring the direction and decide what actually needs to happen. Skip that step, and neither a human nor a machine knows what comes next.
Understand the machine. Do not just operate it
Learning to use AI is not about learning which buttons to press. It is about understanding how these systems actually work under the surface, what they can genuinely do, and how to turn that into something that moves your brand forward, not in theory, in the specific, unglamorous tasks of the job, one at a time. Question every output, no matter how convincing it sounds. Convincing and correct are not the same thing. And keep moving, because what is true about AI today may not be true in a few months.
The gap closes when you ask out loud
Admitting what I did not know yet was more uncomfortable than I expected. It was also the only way back in.
If there is one lesson in this for anyone returning to a fast-moving industry, whether after leave, a career break, or just a slow quarter, it is this: the gap you are afraid of is usually smaller than it feels, and it closes faster once you stop trying to catch up quietly and start asking out loud instead. The industry did not wait for me. It is not going to wait for you either. That is fine. It just means staying a beginner on purpose is part of the job now, more often than it used to be.
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