What is intelligent automation + how to use it | Zapier TechTricks365

The more TikToks I watch of robots that look and sound a little too human, the more amazed—and slightly terrified—I am. There’s a lot that goes into robots like these (and the ones we interact with online, like ChatGPT): automation, artificial intelligence, and machine learning, to name a few.

In case all the artificial intelligence buzzwords floating around weren’t hurting your brain enough, I have another one for you: intelligent automation—the merging of artificial intelligence and automation.

Here, I’ll demystify this term and explain how and why you should use it to make your life (and the lives of everyone in your organization) easier—and maybe slightly terrifying.

Table of contents:

What is intelligent automation (IA)?

Intelligent automation (IA) is the combination of automation technologies with artificial intelligence (AI) to create systems that tackle complex work by learning, adapting, and making smart decisions. This creates systems that can practically think on their feet, analyzing information, learning from experience, and constantly getting better at what they do.

For your business, this means tackling more complex projects with automation and making your workflows dramatically more efficient and largely self-sufficient.

Benefits of intelligent automation

You’re probably already envisioning all the ways IA could make your life easier. Here are some key reasons to implement it.

• Saves employee time and energy: Automation alone is enough to turn an otherwise seven-hour manual task into a five-minute one. But an automation tool that can also collect new data, learn from it, and handle complex decision-making? That can expedite processes exponentially, allowing your employees to prioritize higher-value tasks that are more deserving of their energy and attention.

• Reduces the likelihood of errors: When programmed correctly, robots can be far more accurate than humans (sorry, humans). People don’t follow structured algorithms to the T quite like automated tools, and AI enables IA to analyze huge amounts of data to inform decisions. 

• Identifies opportunities: IA can quickly identify and surface opportunities for your business. When you integrate AI tools into your existing workflows, it can use your data to flag blind spots and possibilities.

• Improves customer satisfaction: The more streamlined and intelligent your process of delivering your products or services, the happier your customers will be. Plus, imagine chatbots actually understanding your customers’ needs, surfacing relevant answers and resources, and knowing when it’s time to connect them to a human representative. A dream come true.

• Helps identify and patch security vulnerabilities: When allocated enough processing power, IA is a speedy process. It can scan your software, surface potential security risks, and even correct those vulnerabilities far faster and more accurately than a human could.

• Increases organizational agility: IA helps your business keep up with zig-zagging market trends without missing a beat. It can quickly spot changes in customer behavior or supply chain hiccups, then help adjust your operations on the fly. 

• Drives innovation: IA is a powerful R&D assistant. It can crunch vast datasets for that aha insight, prototype a web app for a partner, or rapidly test new product features while your team dreams up the next big thing. It’s about turning those “what if” moments into “what’s next” realities faster.

Key components and technologies that intelligent automation uses

To really get a handle on IA, it helps to know the key technologies that work behind the scenes. We’re talking about everything from the workhorses that automate routine tasks to the brains that understand language, interpret images, and even think up new content. Here’s a rundown of some of the essential intelligent automation tools and technologies that power IA.

Robotic process automation (RPA)

RPA bots can quickly accomplish repetitive, routine tasks, such as data extraction and transfers, to save you time. These bots follow predefined scripts to do things like fill out forms, shuttle data between spreadsheets and CRMs, process payroll, or generate routine reports. In IA, RPA often lays the groundwork by tackling these high-volume, predictable tasks, freeing up the more advanced AI components to focus on complex decision-making and learning.

Artificial intelligence

AI is software that mimics human thinking, and you may have noticed, it’s been getting pretty good at it in recent years. It can learn from previous choices, quickly analyze data to make accurate predictions, and make quick decisions. While many technologies on this list are specific parts of AI, its overarching role in IA is to imbue automated processes with these cognitive capabilities. This means IA systems can, for example, intelligently route customer support tickets based on their actual content and urgency, dynamically optimize supply chain logistics as conditions change, or provide remarkably accurate sales forecasts by analyzing complex datasets.

Machine learning and deep learning

Machine learning (ML) is a core type of AI that allows systems to learn directly from data, spotting patterns, making predictions, and improving their performance over time without being explicitly programmed for every variation. 

Deep learning takes this a step further as an advanced subset of ML, using complex “neural network” structures to decipher highly intricate patterns in massive datasets. These learning capabilities are crucial for IA, enabling systems to adapt and get smarter. 

Natural language processing (NLP)

Natural language processing is the AI that makes it possible for computers to understand, interpret, and even generate human language, whether it’s written text or spoken words. This is vital for intelligent automation because so much business information is unstructured language.

Thanks to NLP, IA systems can engage in (mostly) sensible chatbot conversations with customers, automatically analyze thousands of reviews to gauge overall sentiment, instantly translate documents, or transcribe meeting notes into text, making sense of our primary mode of communication.

Generative AI

Generative AI is a particularly exciting frontier of artificial intelligence, where models don’t just analyze existing information but actually create entirely new, original content. By learning deep patterns from vast datasets, these types of systems can produce novel text, images, audio, software code, and more. 

In the realm of IA, this opens up powerful possibilities for automating creative and content-heavy tasks. For instance, generative AI can draft initial marketing copy or email campaigns, design unique visuals based on text prompts, compose original music tracks, or even generate segments of code to speed up software development.

Computer vision

Computer vision lets software interpret and understand visual information from images and videos. While a common application in business automation is optical character recognition (OCR), which “reads” text from scanned documents or images and converts it to digital data, the field is actually much more exciting. 

Computer vision allows systems to identify objects, recognize patterns, and analyze scenes. In IA, this capability means processes can react to visual inputs, automating tasks that previously needed human sight. For example, systems can use computer vision to inspect manufactured goods for quality control, monitor security footage for specific events, assist in analyzing medical scans for anomalies, or identify products and shelving in retail environments.

Intelligent document processing (IDP)

Intelligent document processing technology is designed to tackle the challenge of handling huge volumes of documents by doing much more than just basic text extraction. While it builds on capabilities from computer vision, like OCR, IDP is a distinct solution because it adds a significant layer of artificial intelligence.

Using AI techniques such as machine learning and natural language processing, IDP doesn’t just “read” documents; it aims to understand them. This means it can automatically classify different document types (like an invoice versus a purchase order), intelligently extract specific pieces of information (like names, dates, amounts, or even complex clauses from contracts), validate that data, and then feed it into other business applications. 

For example, businesses use IDP to automate their accounts payable by extracting details from supplier invoices and inputting them into accounting systems to speed up insurance claims processing. They do this by accurately pulling information from various claim forms, or to improve customer onboarding by quickly processing application forms and verifying ID documents.

Business process management (BPM)

BPM streamlines workflows to improve company efficiency. It’s a structured way of looking at your end-to-end workflows, finding the kinks, and redesigning them for better results. Intelligent automation often comes into the picture through BPM strategies like process mining. 

Process mining uses specialized software to analyze your existing processes as they actually happen, creating a clear map that helps diagnose where things are getting stuck, where tasks are taking too long, or what’s ripe for an automation upgrade. Once these areas are identified, IA can then automate specific steps, inject smart decision-making into the flow, or help monitor the improved process, ultimately leading to more streamlined and effective operations, such as faster customer onboarding or more efficient supply chain management.

How to implement IA

Implementing intelligent automation isn’t a set-it-and-forget-it kind of deal. It’s something you’ll have to constantly monitor and adjust as your business and the tech itself grow. That said, here are a few key phases to guide you from an initial idea to a fully functioning smart workflow: 

• Find: Think of this as the scouting mission. You’re looking for those processes that are a bit clunky, data-heavy, or involve tricky decisions that could really benefit from an AI boost. This might involve looking at common bottlenecks, listening to what your team says takes up too much time, or using tools like process mining to get a clear picture of where work gets stuck.

• Analyze: Once a process is selected for review, this is where you put it under the microscope. You’ll map out how it currently runs, identify precisely where it’s causing problems, and figure out if an IA solution makes solid business sense. This step is all about clear objectives and knowing what “better” actually looks like.

• Build: With your analysis done, the next step is designing and constructing your IA solution. This involves mapping out the smarter workflow and picking the right technologies. If you have the team, you might map out these core intelligent components in-house. Alternatively, for specialized expertise or faster implementation, you could turn to automation as a service (AaaS), where experts can construct and configure these sophisticated systems for you.

• Automate and integrate: This is the go-live stage where you bring your intelligent workflow to life. Using Zapier, you can connect your apps and orchestrate the entire process, setting up triggers, actions, and conditional logic that put the AI models to work. It’s where the theoretical design from the “Build” phase becomes a practical, automated reality that actively runs your business processes. Data will flow cleanly between your automation and core enterprise systems (like a central database or ERP) without causing chaos or data integrity issues. 

• Optimize: Since AI models learn from data, their performance can degrade as the real world changes—a concept known as “model drift.” Optimizing an IA system means keeping the AI sharp by monitoring AI-specific metrics, like its confidence level in its own decisions, and periodically retraining it with fresh data. More importantly, it’s about refining the human-AI feedback loop: when a person corrects an AI’s mistake or handles an exception, that action should be used as new training data to make the model smarter for the next time. The goal isn’t just to keep the automation running; it’s to ensure it’s continuously improving its own intelligence.

Intelligent automation use cases

IA’s ability to handle complex processes, analyze vast amounts of data, and enable smarter decision-making is driving fundamental shifts in how businesses operate. Let’s look at some examples.

Hyper-personalization at scale

Hyper-personalization at scale follows through on a pretty big promise: make every customer interaction feel like it was crafted just for them. And it does so thanks to intelligent automation. 

Using machine learning, IA systems analyze extensive customer data. They can browse history, purchase patterns, and even the sentiment in past communications (thanks to NLP). This allows businesses, especially in sectors like retail, eCommerce, or media, to move beyond generic messaging. Instead, they can automatically generate and deliver highly relevant product recommendations, content suggestions, or tailored support responses, making customers feel understood and valued. 

With Zapier, for example, you can orchestrate a fully personalized lead nurture workflow—from automatically capturing lead info to enriching data, segmenting by behavior or source, and sending the perfect follow-up email. By connecting data and using AI across your tech stack, you’ll boost engagement, drive revenue, and free up your team from time-consuming manual segmentation.

Read more: How Vendasta recovered $1M in revenue with automation and AI

End-to-end intelligent document processing

For many companies, especially in fields like finance, healthcare, or logistics, the sheer volume of documents can be a major operational drag. IDP can automate and make sense of it all. It’s a significant step up from basic OCR; IDP uses a blend of computer vision, machine learning, and NLP to:

• Automatically ingest and classify various document types.

• Intelligently extract specific data fields with high accuracy, even from varied or complex layouts.

• Validate the extracted information against existing business rules or databases.

• Route the processed data to the correct systems or trigger the next steps in a workflow.

Consider the generic welcome email, a classic of the “better than nothing” school of marketing. Intelligent automation offers a practical fix. Using Zapier, a workflow can have an AI step analyze a new lead’s info to make a simple judgment: expert or beginner? The expert then automatically gets the technical docs; the beginner gets the welcome video. It’s a simple, automated fork in the road that creates a welcome that feels surprisingly thoughtful and human.

Read more: How Bergen Logistics saves time with AI automation

AI-augmented research, development, and product innovation

Bringing fresh ideas and new products to life is the engine of business growth, but the R&D process can often feel like getting stuck in traffic on a Friday evening. IA changes this by giving research and product teams the ability to fly right over the rush-hour jam directly to the party. Instead of relying on traditional methods, IA helps accelerate innovation in several key ways:

• Machine learning models can dive deep into enormous datasets—be it scientific research, customer feedback (which a Zapier Agent can automatically analyze for sentiment and feature requests, then neatly organize into your team’s product feedback board), or complex market trends—to uncover insights and patterns that humans might miss or take months to find. This helps in areas like predicting material performance or identifying truly unmet customer needs.

• Generative AI can act as a creative partner, helping to generate novel design concepts, draft initial technical specifications, or even suggest new avenues for exploration based on existing knowledge.

• AI-powered simulations allow for testing thousands of design tweaks, material combinations, or experimental setups in a virtual environment. This means R&D teams can iterate much faster, identify potential flaws early on, and explore a wider range of possibilities without costly physical prototypes for every idea.

The upshot of bringing IA into the R&D fold is a significantly faster innovation cycle, products that are often better designed and more thoroughly vetted, and a quicker journey from that initial “what if?” to a market-ready solution.

Read more: How SmartClick Systems helps founders and operators automate millions in revenue

AI-powered supply chain resilience

Supply chains, the unsung heroes that keep businesses stocked and customers happy in sectors from manufacturing to retail, can be surprisingly fragile. IA builds much-needed muscle and foresight into these complex networks. Instead of just reacting to problems after they hit, IA uses machine learning to constantly scan historical data, logistics information, news updates, and even weather reports to get ahead of potential disruptions.

This means businesses can:

• Use AI to model different scenarios and figure out the best backup plans quickly.

• Optimize inventory levels with more precision, avoiding both costly overstock and frustrating shortages.

• Sometimes even automatically reroute shipments or identify alternative suppliers before most people realize there’s a problem.

The goal here is to make the entire supply chain more agile and predictable, which translates to more reliable operations and less stress when things inevitably get complicated.

Read more: How Pretto drives 10% of its annual revenue with automation and AI

Examples of intelligent automation processes

I’ve thrown a lot of technical jargon at you—I’ll make up for it now by talking (without jargon this time!) about how to practically apply it in a business setting.

• Write emails: Sure, your email app can help you draft a reply, but IA can help you simplify the entire process around that email. Instead of just writing, IA can act as a central dispatcher for a shared inbox like support@ or sales@. Zapier can analyze an incoming email’s intent, then automatically route the work to the right place—creating a ticket in Jira for a bug report, updating a lead record in Salesforce for a sales query, and alerting the right team on Slack about an urgent issue, all while drafting a tailored response for each scenario.

• Analyze leads: Let an AI do the prospecting work for you. When a new lead arrives, a Zapier Agent can automatically investigate them online, visiting their company website to understand the business and finding their job title from public sources to see if they’re a good fit. The Agent then scores the lead based on this research and delivers a rich summary of its findings right to your CRM, arming your sales team with the intel they need to focus on the best prospects first.

• Adjust production: IA can use data on supply and demand to reconfigure manufacturing equipment, programming it to produce more or less product, minimizing the likelihood of surpluses or shortages. Essentially, the smarter robots tell the automation robots what to do and when to do it.

• Predict maintenance: For businesses with heavy machinery—think factories or transport companies—IA can predict when equipment needs maintenance before it breaks down. Instead of just reacting to a system alert, the data can be fed to AI to analyze the signal, assess its severity against historical data, and even diagnose the probable cause. Based on the AI’s diagnosis, the workflow can then create a highly detailed work order in a maintenance platform, specifying the urgency and required parts, while simultaneously alerting the correct team via their preferred channel. 

• Optimize supply chains: IA can make supply chains much smarter and more responsive. For instance, AI can analyze historical sales, current stock levels, and even external factors like shipping forecasts to help businesses make better decisions about ordering and inventory. Zapier can connect these AI insights to your inventory management or purchasing systems, for example, by using a Zapier inventory template to trigger a reorder request when an AI predicts stock will fall below a critical level for a popular item.

• A/B test: IA can compare side-by-side versions of assets, whether it be product prototypes or CTAs, and provide insight into which is more effective in a matter of seconds. It’s the type of tedious process IA was made to replace.

• Automate document processing: If your team is swamped with invoices, contracts, or forms, IA can automate much of that document handling. Tools leveraging AI can “read” these documents and extract key information like dates, amounts, or names, and then Zapier can route that data exactly where it needs to go. For instance, Zapier can create a bill in your accounting software from an invoice PDF received via email, or add contract details to a spreadsheet, using AI along the way to handle any discrepancies.

• Automate the path from vibe to production code: A non-technical team member can submit their AI-generated prototype code through a simple form. Zapier could then trigger an AI step to perform an initial code review, automatically checking for common issues or bugs. That AI analysis, along with the prototype code itself, could then be used to create a perfectly formatted, detailed ticket in your development team’s project management tool, bridging the gap from a creative “vibe” to a formal, reviewed development task.

• Automate content creation: You can integrate your favorite generative AI tools with Zapier to draft initial marketing copy, outline articles, or even generate different versions of ad text based on your prompts—all within your existing workflow automations. It’s a great way to overcome writer’s block and speed up content production, leaving you to refine and add the final human touch.

Ready to try intelligent automation? Get started with one of these templates.

Increase sales leads from support tickets

Identify whether support tickets contain buying signals so you can easily route new leads to sales.

Automated changelog

Keep your team aligned by automatically logging and tracking process changes using this customizable template.

IT help desk

Improve your IT support with AI-powered responses, automatic ticket prioritization, and knowledge base updates.

What’s the difference between IA and RPA? 

Simply put, RPA is a less intelligent IA. RPA replaces manual and repetitive work using automation tools like bots. IA introduces cognitive technologies like AI and computer vision into the mix to automate processes that formerly required human thought.

For example, a bot that automatically categorizes users in a CRM based on how they subscribed to a newsletter is a form of RPA. IA might involve using subscriber interaction data (clicks, bounce rate, etc.) to add suggestions to a company’s CRM, informing future newsletter content.

How to kickstart your IA strategy

Intelligent automation can completely revolutionize your organization’s processes, so it’s important to be strategic when implementing it. Don’t pull the rug out from under your employees without developing a game plan.

1. Get buy-in from top management: Don’t just bullet out the generic ways IA is helpful—explain how your organization can uniquely apply it to improve efficiency and see tangible ROI.

2. Start slow: Once implementation becomes viable, don’t throw IA at everything all at once—take baby steps. Prioritize automating the most time-consuming tasks at your organization before moving into the “it would be nice…” category.

3. Focus on data governance and quality: Remember the old saying, “garbage in, garbage out?” It’s especially true for IA, as your AI systems are only as good as the data they’re fed. Establish solid data governance practices to ensure your data is accurate, consistent, secure, and handled ethically.

4. Develop an automation-first mindset: Before assigning a person to a specific task or project, ask if it can be completed (and especially if it can be completed better) by IA. As AI continues to advance with every passing day, you’ll likely find that there are more and more ways IA can make your life easier and your organization more profitable.

5. Address ethical considerations: IA is powerful stuff, and like any new powerful tech, it comes with its own set of ethical questions. Think about things like potential bias in AI decision-making, how customer data is being used, and being transparent about how these systems work. It’s smart to consider these aspects upfront to build and use IA responsibly.

6. Plan for workforce upskilling: As IA takes on more tasks, the roles and skills your team needs will likely evolve. Plan ahead to help your employees learn how to work alongside these new AI tools and automated processes. Offering training and development opportunities not only helps your team adapt but also ensures you get the most value out of your IA investments.

7. Implement learnings: There’s a chance that IA implementation won’t go as planned at times. That’s fine, but try to avoid making the same mistakes twice by documenting your learnings and applying them.

As you work your way through each of these steps, you can lean on Zapier to bring your IA initiatives to life.

Automate intelligently with Zapier

Intelligent automation is about making your workflows not just automated, but truly smart, adaptive, and capable of learning, bringing a new level of operational savvy to your business.

Whether you’re looking to use basic automated sequences or upgrade to complex, end-to-end AI orchestration, Zapier Canvas lets you visually design, understand, and collaborate on these intricate intelligent automation sequences, creating the blueprint for your intelligent operations that includes all your essential apps. 

Think of Canvas as your strategic design space for IA. Once you’ve visualized the flow, it connects the tools you need to build and run it. Here’s how:

• Embed AI decision-making and content generation directly into your Workflows with a library of 8,000+ app integrations.

• Deploy Zapier Agents as AI teammates to perform research, analyze data from your connected sources, and handle multi-step tasks autonomously.

• Build custom Zapier Chatbots in minutes to provide intelligent, 24/7 support or engage leads, drawing information directly from your knowledge bases and automatically sending lead data wherever you need it.

• Use Zapier Copilot to suggest steps or AI-powered code generation for custom logic.

• Create custom web pages, forms, and dashboards with Zapier Interfaces, and add AI automations that help you glean insights from the interactions.

Intelligent automation is no longer just a futuristic buzzword; it’s a practical set of tools and strategies you can use today to make your business more efficient, responsive, and innovative. Zapier provides the platform to connect your apps, layer in intelligence, and orchestrate powerful automated workflows that drive real results.

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This article was originally published in April 2023 and has also had contributions from Michael Kern. The most recent update was in June 2025.