AI compliance: How to successfully integrate AI into your compliance workflows

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AI compliance: How to successfully integrate AI into your compliance workflows

It’s easy to think that the only way “AI” and “compliance” can belong in the same sentence is in the context of a robot overlord giving monotone but terrifying lectures to humans about complying with its commands. But as it turns out, AI can actually play a helpful role in compliance workflows without requiring an AI apocalypse first.

Compliance teams can use AI without compromising security or creating more problems than they solve. The trick is to avoid replacing human judgment with a chatbot in a suit, and instead find the right balance between automation and expertise.

Zapier spoke to experts who have been in the trenches. They’ve tested, failed, fine-tuned, and figured out what actually works. Here’s their best advice for smarter, safer, and saner compliance—where the humans still run the show, and the machines just help you get through the paperwork a little faster.

Start with low-risk wins

For many compliance professionals, AI can feel like that overly confident coworker who means well but doesn’t understand the stakes yet.

Elena Shturman, a corporate compliance expert, puts it bluntly: “You can’t just drop sensitive info into a system without risking privilege or exposure.”

In heavily regulated functions like compliance and legal, AI adoption hasn’t exactly been speedy. And it’s not because the tools aren’t useful—it’s because the data is often too sensitive. Between attorney-client privilege and the uncertainty of how AI systems handle privacy, there’s a real risk of a misstep. As Elena points out, “most of us avoid it” for anything that touches confidential information.

But that doesn’t mean AI can’t be helpful. Elena has had success in places where the data is less risky but the time suck is still real. Take expense review: Tools like qordata use AI to flag duplicate charges, policy violations, or fishy spending patterns in minutes—saving her hours of manual review.

She’s also leaned into process automation in areas like audit prep, using AI to send reminders and centralize evidence request forms. These “safe automations” don’t touch privileged data but still cut prep time almost in half.

Where AI hasn’t worked is in policy creation and risk assessments. “Those tasks need human context,” Elena explains. AI can churn out content, sure—but in these high-stakes areas, it often creates noise instead of clarity. Elena concludes, “The lesson for me is that automation is great for repetitive, low-risk tasks, but real compliance decisions still need a human brain until the privilege and security issues are sorted out.”

AI should support decision-making, not replace it

Mircea Dima has seen both the magic and the mess when it comes to AI in compliance. As a CTO and software engineer at AlgoCademy who’s built enterprise-grade systems, he’s all for automation, but only when it plays the right role.

Take one fintech startup he worked with. They used AI to streamline policy review, starting by training a model on three years of historical compliance data. Once up and running, the system “automatically classified incoming regulatory updates, marked applicable areas to be read by humans, and proposed policy changes.” That AI workflow alone now lets the team do the same policy review work in a quarter of the time.

But for every win, there’s a warning. “The most spectacular collapse I observed was a firm attempting to automate evidence collection to accommodate a SOC 2 audit,” Mircea shared. The AI couldn’t connect the dots between controls, leading to gaps that auditors spotted right away. (And you really don’t want auditors spotting anything right away.)

As it turns out, AI is brilliant at pattern recognition but not so great with “regulatory complexities and inter-departmental interdependence,” Mircea said. Translation: It can help gather puzzle pieces, but don’t expect it to finish the picture.

That’s why Mircea lives by a new rule: “Do the menial labor with a computer, and the computer labor with a human.” It’s a kind of Goldilocks zone of compliance automation. Let AI scan documents, track deadlines, and flag risks—but keep humans in the loop to assess “materiality, control effectiveness, and regulatory interpretation.”

The sweet spot, according to Mircea, is using AI as a “smart assistant,” or a tool that surfaces data and proposes actions without cutting compliance professionals out of the process. This hybrid model can roughly halve your work time without sacrificing audit quality.

The trick is not to aim for full automation. Aim for augmented intelligence—AI that supports decision-making, not replaces it.

Automate evidence-collection

Matt Mayo, owner of Diamond IT, has a relatable origin story when it comes to compliance automation: “manual screenshots, tracking shared drives, and chasing down engineers for access reviews.” If you’ve ever prepped for a SOC 2 audit, you know it’s like herding cats—if the cats controlled access to production servers.

So when Matt’s team used AI tools to help with audit readiness, the relief was immediate. “We integrated GitHub, Google Workspace, and AWS to automatically collect evidence for access controls, code changes, MFA enforcement, and vendor risk reviews,” he explains. That shift reduced their audit prep time by at least 70% and transformed compliance from a once-a-year scramble into something continuous and manageable.

Better yet, the system not only collects receipts, but also flags issues as they happen. “The system alerts us if something deviates from policy,” Matt says, “so we’re addressing issues in real-time, not retroactively.” No more sweating bullets in Q4 trying to remember why Jenkins wasn’t enforcing MFA six months ago.

But—because there’s always a but—not all tasks are ripe for automation. Matt’s team ran into trouble when they tried using AI tools to write policies. “The generated policies were technically accurate but lacked business context,” he explains. They missed key operational realities, like how specific tools were configured or why certain exceptions existed in the first place.

Now, they write policies the old-fashioned way—with a human brain—and only use AI “for grammar checks or cross-referencing controls.”

The lesson Matt’s team learned is a familiar one: “Automation works well for tasks with clear inputs and outputs—evidence collection, monitoring, ticket logging—but policy writing and risk assessments still require human judgment.”

Keep humans in charge of the fine print

Peter Murphy, CEO and founder of Track Spikes, discovered firsthand that AI is a massive time-saver for compliance workflows. His team was able to “reduce the time required for our product compliance documentation from weeks to hours.” That includes safety certifications and material compliance forms, which his team drafts with the help of ChatGPT before reviewing them for accuracy.

Peter’s team also automated audits of their inventory. Instead of manually combing through spreadsheets, their Shopify integration “identifies spike inventory anomalies and compiles reports” automatically. That means they can catch discrepancies before they turn into full-blown problems.

But not every attempt to automate was a win. When the team tried to fully automate customer service compliance, especially for international orders, the AI tripped over the details. “AI ignored minor shipping regulations that caused delays at ports and angered clients,” Peter recalls. It’s a helpful reminder that even small errors in compliance can have outsized impacts—especially when they show up at customs.

Still, AI has its place in policy-making. “One policy-making activity that can easily be aided by AI is drafting initial versions of policies,” Peter says. His team uses it to generate first drafts of return policies and terms of service, which are then refined and finalized by their legal advisor. In this model, AI sets the table, and humans decide what’s actually for dinner.

Peter puts it simply: “The point of convergence is AI taking care of routine duties while human beings handle the judgmental duties.” Automation shines at “gathering and structuring data,” but “business decisions require human experience and background.”

It’s a division of labor that works—machines handle the structure, while humans bring the sense.

‘AI’ and ‘compliance’ actually do belong in the same sentence

Whether you’re drowning in manual reviews, knee-deep in audit prep, or just trying to decode your third regulatory update of the week, AI can be an ally. But only if you implement it thoughtfully.

Instead of choosing between human expertise and artificial intelligence, successful AI integration in compliance means finding the sweet spot where both work together. As each of the experts consulted for this story learned, AI excels at handling repetitive, data-heavy tasks like expense reviews, document classification, and evidence collection. But when it comes to nuanced decisions about risk assessment, policy creation, and regulatory interpretation, human judgment remains irreplaceable.

The most successful implementations follow a clear pattern: Start with low-risk, high-volume tasks where AI can provide immediate value, then gradually expand to more complex workflows while maintaining human oversight at critical decision points. This approach not only reduces the risk of costly mistakes but also builds confidence in AI systems over time.

This story was produced by Zapier and reviewed and distributed by Stacker.