AI adoption: A practical guide

I have two voices in my head when it comes to new tech. One is the wide-eyed optimist who wants to try every tool and click every AI button. The other wants to delete every app on my phone, move to a cabin in the woods, and write by candlelight.

There’s no doubt AI is exciting. It’s already helping teams save time, be more creative, and get through the workday with fewer repetitive headaches. But it’s also introducing new challenges: messy rollouts, tool overload, unclear guidelines, and real questions about what work even is now. According to one survey, 97% of execs say AI is helping their teams but 42% admit the adoption process is leading to a good amount of chaos.

That’s the paradox. AI is both helpful and hard, useful and overwhelming. So this guide is here to help you walk that line: how to adopt AI in a way that’s thoughtful, strategic, and maybe even fun—without losing your mind (or your team’s trust) along the way.

Table of contents:

What is AI adoption?

AI adoption isn’t just about slapping ChatGPT onto your workflows and calling it a day. It’s the process of thoughtfully integrating artificial intelligence into the way your business runs.

At its core, AI adoption means making AI part of your organization’s everyday toolkit, just like email, spreadsheets, or your project management app of choice. It’s figuring out where AI can actually make a difference, testing and tweaking workflows, and eventually trusting it enough to take on real responsibility.

For example, here’s how a few real Zapier customers are integrating AI into different departmental workflows:

The best AI adoption stories don’t start with “We wanted to use AI.” They start with “We needed to solve X,” and AI happened to be the best way to get there. 

Generative AI adoption

Generative AI adoption refers to the process of integrating tools that create new content—like text, images, summaries, or code—into everyday workflows. Unlike broader AI adoption, which can include automation, data analysis, or decision support, generative AI focuses specifically on systems that create new outputs based on natural language prompts.

The magic of generative AI adoption is in how low-lift it can be to get started and how quickly it becomes second nature. You don’t need to overhaul your tech stack or train a custom model. You just need to start by asking: “What’s a repetitive or annoying thing we do every week that involves writing, summarizing, or brainstorming?”

From there, you can use Zapier to build generative AI helpers right into your workflows. 

It’s worth remembering, though, that generative AI isn’t perfect; it can get things wrong or hallucinate. So part of adopting it responsibly is building in checks with things like human-in-the-loop review, prompts that set clear boundaries, and choosing tools that prioritize trust and data safety.

What are the benefits of AI adoption?

Is it really worth it to rethink your workflows, nudge your team toward new tools, and even trust an AI to write something that goes in front of a customer?

Short answer: yes.

Long answer: Here are some of the very real benefits teams—including ours at Zapier—are seeing when they adopt AI in a thoughtful, strategic way. These aren’t pie-in-the-sky possibilities; these are benefits I’ve seen firsthand.

Time savings 

We’re not just talking about shaving a few seconds off here and there. When AI takes over repetitive and time-consuming tasks like summarizing meeting notes, triaging support tickets, or writing first drafts, you get hours back. That’s time your team can spend on the work only they can do: building, creating, problem-solving, and strategizing.

Better decisions, faster

AI can sift through data in seconds, identify patterns, and surface insights you might’ve missed while trying to make sense of yet another spreadsheet. It can do things like forecast trends, prioritize leads, and identify customer churn signals with reliability and efficiency.

Consistent, high-quality output

People get tired. AI doesn’t. It can help teams deliver more consistent work and reduce human error. You can train AI on your style guide, process documentation, and knowledge base so that the output matches your brand guidelines, every time.

Happier teams

Nobody takes a job to spend a quarter of their time copy-pasting text between tools or rewriting the same status update five different ways. When you adopt AI to reduce busywork, you free up your team to focus on work that feels meaningful—and that’s a win for both morale and retention.

Scalability without the burnout

Need to onboard 200 new users a week, launch a campaign in five markets at once, or handle double the support volume without doubling the team? AI helps you grow without stretching your people too thin. Think of it as a bandwidth boost, not a team replacement.

Common barriers to AI adoption

AI might feel like the promised land of productivity—but getting there is a different story. Even at Zapier, where our team is all-in on being AI first, we’ve run into blockers that stalled progress or killed momentum. The good news is that most of these barriers are solvable with the right mindset.

Let’s break down what typically gets in the way.

Not having the right tools

You can’t adopt AI if you don’t have tools that support it—or if your current stack makes it a nightmare to integrate anything new.

Some platforms are just starting to layer in AI features. Others expect you to bring your own model or write code to get started. And then there’s Zapier, which lets you plug AI into your workflows without needing an engineer on standby.

Before you go big, ask yourself: can your current tools support lightweight experimentation with AI?

Cost and resourcing

AI isn’t free. Whether you’re paying for premium AI tools, hiring consultants, or investing in new infrastructure, the costs can stack up quickly. Even the time cost of learning new systems can be a barrier.

The trick is to start small. Automate one high-impact task, prove the value, then scale. You don’t need a million-dollar budget to get meaningful ROI from AI. You just need to be strategic.

Technical complexity or infrastructure

Not every org has the backend setup (or IT capacity) to roll out complex AI systems. And unless you’ve got a team of machine learning engineers sitting around, you probably don’t want to build something from scratch.

Luckily, AI has become a lot more accessible. Many modern tools are low-code or no-code, and APIs like OpenAI’s or Claude’s can be dropped into existing workflows without a full rebuild. Zapier makes it easy to add AI to your existing workflows and apps, even if your tech stack doesn’t have AI features built in. 

Employee resistance or change management

Even the best AI tool is useless if no one wants to use it. Some employees worry AI will replace them, while others just don’t want to change how they work.

When tapping into AI means opening a separate app, learning a new workflow, or figuring out how to write the perfect prompt, most employees will default to what already works. Even if AI could save them time, the mental tax of switching contexts, rethinking their process, or navigating a new interface is often too high—especially if they could otherwise be re-watching Season 10 of Married at First Sight: Australia.

Successful AI adoption involves overcoming doubts and inertia to bring your team along for the ride. AI needs to feel less like a new initiative and more like a natural extension of existing workflows. Focus on transparency, and emphasize that AI is here to augment, not replace. And don’t just drop a tool in someone’s lap—show them how it makes their life easier.

Data governance and privacy concerns

Teams worry (rightfully) about where their data is going, how it’s being used, and whether it’s safe to feed customer info into third-party AI tools.

The best solution is to use platforms like Zapier that offer transparency, control, and secure AI integrations. Set clear policies around data sharing, and make sure your legal and security folks are looped in before anyone starts copy-pasting sensitive information into a chatbot.

AI hallucinations and missteps

AI can “hallucinate” facts, misinterpret prompts, or make confident guesses that are completely off-base. And that’s scary if you’re using it for anything public-facing or business-critical.

That’s why human review is key. Build in checkpoints, use narrow, well-scoped prompts, and educate your team on what AI can’t do yet—so no one ends up with a chatbot making promises your product can’t keep. For example, you might add a Slack “Request Approval” step to your Zapier workflow that lets a real person sign off on an action before it finishes running.  

These barriers are real, but they’re not deal-breakers. With the right tools, education, and a healthy dose of patience, AI adoption doesn’t have to be a moonshot.

How we adopted AI org-wide at Zapier

Adopting AI at Zapier didn’t start with a big strategy deck—it started with curiosity. A few of us were poking around with LLMs before ChatGPT was even a thing, but that casual experimentation turned into something more serious when GPT-4 dropped. 

Our CEO called a company-wide “Code Red,” not because we had a plan, but because doing nothing felt riskier than jumping in. That message created the urgency we needed to really shift gears. We didn’t over-engineer the rollout; we just said “This is big, it’s here, and we all need to engage with it.”

From there, we went all in. We paused work, ran a full-company hackathon, and got every team—tech and non-tech—building with AI. We paired that with real scaffolding: clear usage guidelines, fast procurement paths, internal enablement hubs, and a few deeply curious folks who helped others get started. We didn’t clean up the mess or wait for consensus. We just built, shared, and learned in public.

Over time, hackathons turned into habits, and AI is now helping us do things we couldn’t before. Workflows are being reimagined, headcount plans look different, and entire categories of “too tedious” work are now set to autopilot. It’s not always smooth or uniform, but the direction is clear: AI is part of how we think and operate.

The key to success for our team was that we didn’t wait for a perfect plan. We gave people room to explore, made enablement easy, and repeated the message. And most importantly, we kept showing up—at hackathons, in Slack threads, and in demos. The momentum built from there. Now, our team has an 89% AI adoption rate—and we’ve made AI part of our hiring process to ensure all new team members are fluent in AI, too. 

Best practices for starting (or scaling) AI adoption

So you’re ready to bring AI into your team—or take it beyond the early adopter crowd. Here’s what’s worked for us and what we’ve seen work for others.

Pick the right tools

You don’t need a custom-built solution to get started with AI. For many teams, the quickest and most practical entry point is using an out-of-the-box tool like Zapier. You can connect AI models from OpenAI or Anthropic directly to the apps you already use—no code required—and start experimenting right away. Zapier even has AI built into the platform, so you won’t need to manage a separate AI account just to test things out.

Of course, some use cases may require more advanced setups—like custom APIs, fine-tuned models, or access to internal data. If you’re working at that scale, you might need a deeper AIOps infrastructure with more control over how and where AI runs.

As you evaluate your options, consider:

• How is data stored, handled, and secured?

• Are prompts or user inputs being used to train future models?

• Can I monitor, audit, or version the AI logic I’m using?

• Will this tool scale as the team—or the use case—grows?

Look for tools that prioritize transparency, offer flexibility, and come with enough documentation to keep your team in control at every step.

Audit existing workflows and opportunities

When getting started with AI adoption, the best place to start is with tasks that are repetitive but still require a bit of thought or finesse. Think: triaging support tickets based on tone or topic, drafting initial versions of content, summarizing long blocks of customer feedback, or enriching lead records with contextual info. 

These are the kinds of workflows that happen often enough to warrant automation, but involve just enough nuance that a simple if-then automated workflow probably won’t cut it. That’s where AI’s language skills and pattern recognition can really shine.

There’s no need to rework your entire business overnight, either. Start by identifying a few areas where AI could step in to handle the mental heavy lifting behind routine tasks—reducing effort, speeding things up, or making outputs more consistent.

Ask yourself:

• Where are team members making the same judgment calls again and again?

• What slows down progress because it involves reading, writing, or sorting information?

• Where could faster decisions or insights make things better for customers—or less frustrating for your team?

If you’re not sure where to start, here are a few templates to show you the kinds of things you might do.

Start small: pilot projects in high-impact areas

You don’t need to transform the entire org in one go (in fact, please don’t). Start with a few pilot projects in teams that are open to experimentation. Look for problems that are small enough to solve but big enough to matter.

Then, run quick cycles. Build something, test it, and share the results widely. Those early wins will create momentum and give other teams permission to try their own experiments.

Get cross-functional buy-in from IT, ops, leadership, and legal

This part is easy to skip—and a mistake if you do. If your security or legal teams aren’t looped in early, you’ll hit blockers fast. The same goes for leadership: if execs aren’t bought in, it’s harder to get time, attention, or budget. And if IT or ops isn’t aligned, folks won’t get the tools they need.

Build feedback loops and a learning culture

AI is changing fast—and your org has to keep up. That means creating space to learn out loud. 

At Zapier, Slack channels like #fun-ai became hubs for sharing use cases, failed experiments, and new tools. We’ve also held live demos, internal Q&As, and recurring hack weeks to keep the energy going.

You don’t need all the answers up front, but you do need a place for people to ask questions, share wins, and learn from each other. AI adoption is an ongoing habit, not a one-time rollout.

If you’re still not sure where to start, my coworker Phil is on it. Get in touch for some help with your team’s AI adoption.

Move from AI curiosity to AI adoption

AI isn’t some distant frontier anymore. It’s here, it’s powerful, and it’s not just for tech giants or early adopters with PhDs and DevOps pipelines. It’s for you, your team, and the day-to-day work that makes your business run.

The challenge now is making AI easier to use, easier to trust, and easier to fold into existing workflows. That means starting small, building visibly, and making room for a little mess as people learn. It also means reducing friction wherever possible—so AI becomes less of a Big New Thing and more of a quiet teammate that’s just part of the process.

So whether you’re leading AI adoption at scale or just trying to write fewer status updates by hand, remember: you don’t have to do it perfectly. You just have to get started, and the rest will follow.

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