The people's voice of reason
The people's voice of reason
AI is already everywhere at work. It's the invisible hand behind your inbox drafts, the reason your reports build themselves faster, and maybe even why your boss thinks they're suddenly a "data person."
The real question isn't if you'll use AI at work—it's how you'll make it work for you. Because while AI can crank through tasks, it can't replace your judgment, experience, or uncanny ability to find the perfect gifs to make number-heavy slides less of a snooze. That's where you come in.
Zapier discusses five ways you can work with AI—skills and mindsets that'll help you adapt without losing what makes you excel at your job in the first place.
Adapting to AI doesn't mean overhauling everything you do. Instead, it's about making small shifts that add up to significant results. Here are five strategies to help you do just that.
AI makes execution faster, but that raises the bar on your role as the human in the loop. Instead of manually cranking through tasks, practice giving precise, outcome-focused instructions. For example, if you need a report, describe what metrics matter, how it should be formatted, and the audience it's for—then let AI generate the draft.
This skill—sometimes described as moving from creation to allocation—turns you into the strategist while AI handles the mechanics.
This shift away from doing is even easier when you pair AI with automation. You're not just delegating a single task to AI—you're orchestrating an entire process that keeps work moving without extra effort.
Writers often fall into one of two camps: "pantsers" or "plotters." Pantsers are the fly-by-the-seat-of-their-pants type, while plotters like to plan ahead. AI rewards the latter. The clearer your upfront structure, the better your results. For example, instead of "draft an email," try "draft a three-paragraph email for enterprise customers announcing a product update, with a CTA to schedule a demo."
There's no one "right" way to collaborate with AI, but there are two common approaches.
Try both, and notice which feels more natural for different types of projects. For data-heavy tasks, you might lean centaur. For creative work like drafting copy, cyborg may be better.
It's tempting to trust AI outputs at face value, but skipping careful review is risky. Even as AI tools get better at avoiding errors, your expertise is still essential. For many people, that's a shift, especially if you've spent years building deep subject matter knowledge. The real value going forward won't be human or AI expertise alone, but the combination of both.
The challenge is that reviewing requires a different kind of focus than creating. When you build something from scratch, you naturally engage more deeply with the material. Reviewing, on the other hand, can feel surface-level, which makes it easier to miss subtle mistakes.
The key is to treat reviewing as an active process, not a passive one. This includes:
If AI feels like it's changing by the hour, that's because it is. Blink, and there's a new tool, model, or "revolutionary" feature to tinker with. It's exhausting.
Stay flexible. Instead of fighting AI, roll with the changes. Stay curious. Keep experimenting. Think of it less like bracing for impact and more like surfing a wave—chaotic, sure, but also kind of thrilling once you catch it.
This story was produced by Zapier and reviewed and distributed by Stacker.
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