What Is AI Automation? A Guide for Data Teams Building Smarter Workflows

Manual automation has its limits. It often struggles with undetected errors and can leave teams feeling overwhelmed. AI automation solves this by adding an intelligence layer to decision points, reducing manual effort and minimizing errors.

Due to these advantages, 78 percent of organizations use AI in at least one business function. As a result, employees focus more on responsibilities that directly drive business value, and businesses cut unnecessary costs.

In this article, we’ll explore what AI automation is, the benefits it offers, the challenges it presents, and how to design a strategic framework to overcome those challenges.

Key characteristics that differentiate AI automation: 

Data-aware decision-making 

AI systems can process different data formats in real time, using algorithms to evaluate options and select the best course of action. This means they can provide more thoughtful responses, like responding to customer questions based on the emotions they expressed in their messages. 

Continuous learning and improvement 

AI models that power automated systems can improve over time as new data and outcomes are generated without requiring human intervention. A process that struggles with a certain type of exception today might handle it easily a month from now. 

Handling unstructured data 

While traditional tools typically struggle with non-tabular formats, AI systems can interpret images, videos, audio, and free-form text. This ability opens doors to applications like extracting insights from scanned documents or analyzing social media for trends. 

The role of AI process automation 

AI process automation applies intelligence to create an agentic workflow that coordinates multiple tasks with minimal oversight. It takes over time-consuming work and manages interconnected tasks step by step, making decisions along the way that would otherwise need someone’s intervention. 

Unlike traditional automation, which is like a rigid assembly line that works well for predictable outputs but is prone to breakdowns with changes, AI process automation acts like an adaptable [or flexible?] coordinator. It can reroute tasks based on live insights and improve overall work efficiency. 

By embedding AI into their processes, organizations can achieve hyper-personalization and agility. A PwC survey shows that companies using AI automation report 3x higher revenue growth per employee compared to those relying on traditional methods.  

Now that we’ve defined what AI automation is, let’s take a look at how it can help. 

The benefits of introducing AI automation 

Incorporating AI automation into your workflows offers numerous benefits that extend from the data team to the C-suite. Here’s a closer look at some of the key advantages: 

• Cost reduction and efficiency: AI reduces manual labor and errors by automating complex tasks, which leads to notable savings. Processes that previously took hours for people to review can now be finished in seconds, allowing people more time to focus on more highly valued work. ‍
• Improved decision-making: AI automation delivers deeper and more contextual insights by analyzing large data sets that would be difficult for people to handle. This leads to accurate forecasting, better resource distribution, and smarter data-backed choices. ‍
• Greater flexibility and adaptability: AI automation systems can quickly adapt to new conditions, like shifting customer behaviors, supply chain disruptions, or regulatory changes. They allow organizations to pivot much faster than competitors using traditional static methods. ‍
• Increased customer and employee satisfaction: Personalized interactions and streamlined internal processes increase satisfaction. A report from Microsoft notes that AI improves customer engagement by creating tailored experiences while reducing employee workloads. 

Now, let’s compare AI automation with traditional automation approaches. 

The difference between traditional automation and AI process automation 

While both methods aim to make operations more efficient, there are still significant differences between AI-driven automation and traditional automation. Below is a comparison: 

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Let’s look at how AI automation applies to business processes and day-to-day workflows. 

Real-world AI automation examples 

Beyond theory, automation is already changing industries and creating measurable results. Here are some key applications:  

AI automation in customer service 

AI automation systems can categorize, prioritize, and resolve support tickets by using natural language processing (NLP) to analyze content and sentiment. In a typical setup, AI takes over repetitive steps that once required manual effort, such as sorting tickets, retrieving context, and suggesting responses.  

Before automation, support engineers often had to manage hundreds of tickets daily, manually searching through resources to find answers. That approach worked at small scale, but as ticket volumes grew, the manual work became a bottleneck.

With AI automation, companies can build intelligent workflows that automatically handle much of this process. Each new ticket can trigger an agentic workflow that uses natural language prompts to interpret the issue, gather relevant data, and create a draft response for a human to review.

These workflows can pull context from internal knowledge bases, ticket histories, and community forums, compiling relevant information within seconds. Over time, the AI agent gets more accurate by learning from approved responses and past resolutions, creating a self-improving support system that scales as demand grows.

AI automation in supply chain management 

AI automation introduces predictive adjustments and real-time route planning into logistics. An AI system can continuously track weather, traffic, and port congestion. If it predicts a delay, it can reroute shipments, update inventory forecasts, and automatically notify everyone involved. This turns the supply chain from a reactive system into a proactive and self-improving one. 

AI automation in financial services 

Finance departments are mostly overwhelmed with unstructured documents like invoices, receipts, and contracts. AI automation uses Optical Character Recognition (OCR) and LLMs to reveal key information like vendor names, invoice amounts, and due dates, then routes them for approval and payment. 

AI in call monitoring and compliance 

For regulated industries, it’s imperative to comply with regulations when interacting with customers. AI can transcribe and analyze all call center chats in real time. It can spot when agents deviate from scripts, sense how customers feel to identify risky relationships, and make sure all legal disclosures are made. It offers oversight impossible with manual call sampling. 

AI automation in marketing 

Marketers can use AI workflows to create personalized marketing campaigns at scale. An AI system can analyze a customer’s browsing history, purchase data, and engagement patterns to place them in the most relevant marketing segment. 

The AI automation system can then send personalized emails, suggest products, and change advertising spending in real time based on campaign performance. 

Now, let’s see the challenges organizations must prepare for regarding AI automation. 

The challenges of AI automation 

Despite the immense benefits of AI automation, implementing it presents several challenges. Recognizing these challenges is the first step to overcoming them. 

• Integration with existing systems: AI tools must work well with your existing technology stack, including CRMs, ERPs, and legacy databases. Poor integration can lead to siloed data and prevent the AI from accessing the information it needs to function effectively. ‍
• Data quality and availability: An AI models’ performance depends on high-quality data. Inaccurate, incomplete, or biased data will lead to flawed decisions and unreliable automation. Many organizations may need to start a major data cleansing and governance effort. ‍
• Ethical considerations: AI automation raises ethical issues, including data privacy, bias, and transparency. Organizations must set governance policies to ensure AI systems are fair, accountable, and explainable. 

Let’s discuss the strategies to ensure AI automation adoption is successful and sustainable. 

Strategic framework for adopting AI automation and overcoming challenges 

A successful AI automation strategy is more of a business transformation effort than merely a technology project. 

1. Align with business strategy: Start by identifying the specific business problems you want to address. Don’t adopt AI just for the sake of it. Focus on impactful use cases that match your strategic goals, like improving customer retention or lowering operational costs. ‍
2. Start small, scale smart: Pilot a single workflow and measure success via KPIs like time saved. Once you have an early win, you can use that momentum to broaden your efforts across the organization. 
3. ‍Use platforms that support integration and prototyping: Modern data platforms like Domo offer pre-built connectors, no-code/low-code environments for testing, and the flexibility to integrate with various AI models. They provide a unified space to build and deploy intelligent workflows without much engineering effort. ‍
4. Establish governance and responsible AI practices: Establish bias audits, data privacy protocols, and ethical guidelines from the start. ‍
5. Invest in skill development, teamwork and leadership buy-in: Upskill your existing teams and foster collaboration among data scientists, business analysts, and IT. Leadership buy-in is vital for championing the initiative and managing the cultural transition . 

The future of AI automation: What’s next for data teams 

AI automation will evolve, and data teams must shift from reactive tasks to proactive orchestration, piloting adaptable, intelligent systems. Below are some key trends to watch: 

• Agentic AI evolution: Autonomous agents that can plan, execute, and work together on multi-step tasks are set to become more prominent, moving from reactive responses to proactive, forward-thinking systems. ‍
• Multimodal understanding powers smarter actions: AI automation is overcoming its single-sense limitations. New multimodal AI systems will analyze and interpret data from text, visuals, audio, and structured inputs, enabling more advanced automation of complex tasks. ‍
• Smarter, self-repairing, and autonomous data ecosystems: AI will enhance data integration with self-healing workflows and smart schema adjustments. These systems will automatically detect errors, redirect data flows, and resolve mismatches to keep uninterrupted pipelines and improve data quality. 

Let’s see how Domo can help teams turn their AI automation vision into life. 

Bring AI automation to your business processes with Domo 

Knowing the future of AI automation is one thing, but knowing how to implement it effectively requires the right platform. At Domo, we’re here to help your data teams with flexible tools to design, test, and deploy AI workflows more quickly and easily. Here’s how Domo can help: 

• Unify your data foundation: For AI automation, you need clean, governed, and accessible data. We provide over 1,000 connectors and data integration tools to help you create a reliable data foundation that your intelligent workflows can trust. ‍
• Build intelligent workflows with ease: With Domo, you don’t need a team of AI engineers to get started: 
• With Domo Workflows, you can easily orchestrate complex, multi-step processes with a visual, drag-and-drop interface. 
• Our Magic ETL tool simplifies data preparation and transformation, ensuring your AI models are fed high-quality data. 
• And with App Studio, you can build and launch custom, AI-powered applications for both internal and external customers. ‍
• Deploy AI agents, not models: With Domo’s AI Agent catalyst framework, you can take it a step further. You can build, govern, and scale true AI agents that can reason, act, and communicate within your business processes. ‍
• Scale without the friction: Domo provides the governance, security, and scalability that your enterprise demands. As your AI automation initiatives grow, you’ll maintain full control and visibility. 

Ready to see AI automation in action? Watch our Future of AI video series to see how you can build smarter workflows with Domo.