Let’s start by clarifying the difference between artificial intelligence and automation.

Automation includes many technologies like machine learning and natural language processing. These technologies perform repetitive tasks without human help. These tasks may be rule based (often robotic process automation or business rules) or they could involve cognitive automation like AI. Automation can also occur at the task-level or be part of a broader process that passes work between automation technologies and humans. Its primary goals are to save time, reduce human error, and free up workers for more high-value tasks.

AI refers to systems that mimic human intelligence and is a specific type of automation called “cognitive automation.” Artificial intelligence can perform data analysis, learn from it, and make decisions without being explicitly programmed for every scenario. AI technologies can adapt to new situations by learning patterns and improving performance over time. It’s worth noting that artificial intelligence can come in several subtypes such as:

• Generative AI which creates new content like text or photos.

• Natural language processing which understands and interprets human language.

• Machine learning which allows systems to learn from data and algorithms.

• Predictive artificial intelligence which uses patterns to forecast future events. 

In short, business process automation tools handle everything from routine, rule-based tasks to more complex problems that require learning and decision-making.

When AI is combined with other business process automation tools like robotic process automation, the result is intelligent automation. This strong combination helps businesses to automate not only repetitive tasks but also tasks that require some level of decision-making and analysis. While the term AI automation sounds like it would be synonymous with intelligent automation, it typically refers to the broader concept of using AI to automate tasks. Intelligent automation is used in situations where these technologies are automating complex workflows.

For example, let’s say you use traditional automation to send out marketing emails to customers. Automation can send the emails, but AI automation brings in machine learning algorithms that can analyze which customers are most likely to engage with certain types of content based on data from past behavior, making the entire process smarter.

Intelligent automation may bring in generative AI to draft the emails first for humans to review for accuracy, then continue to move on to the next steps in the workflow depending on recipient actions. By combining AI with other forms of automation in these more complex workflows, you can make your business workflows more adaptive, personalized, and efficient.

Here’s a simple breakdown of AI workflow automation:

• Automation: Performs routine tasks and orchestrates processes.
• AI: A form of automation that learns from data and makes decisions. 
• Intelligent Automation: Combines artificial intelligence and other automation tools like RPA to automate and streamline more complex tasks and processes that involve decision-making and learning.

Let’s dive into some real-world examples where artificial intelligence and automation are working together to streamline processes.

AI-powered chatbots have become a staple in customer support. Traditional automation could only handle basic, scripted conversations, but artificial intelligence takes chatbots to the next level. AI-powered chatbots can understand natural language, process customer queries, and provide relevant responses using generative AI. They learn from previous interactions to improve customer insights over time. This reduces the need for human customer service agents to handle repetitive inquiries, allowing them to focus on more complex customer support needs.

Processing invoices manually is time-consuming and prone to error. Automation software has been used to streamline this process, and when combined with AI, the results are even more impressive. AI can read and interpret both structured and unstructured data from invoices using technology like natural language processing. It can then match them to purchase orders, flag discrepancies, and extract data automatically for use in applications, making data entry more accurate.

Manufacturing companies use AI-driven automation to predict maintenance needs. Traditional automation systems can schedule regular maintenance tasks. AI can enhance this by using predictive analytics to determine when machinery is likely to fail based on historical data. AI can analyze equipment performance data and predict issues before they happen, enabling companies to perform maintenance only when needed. This minimizes downtime and extends the lifespan of equipment.

AI technologies are transforming HR processes, especially onboarding. AI can process onboarding documents, saving time for HR professionals. It can even suggest potential training opportunities for employees. This makes the entire process more efficient and ensures that HR teams can focus on more strategic tasks.

Financial institutions use AI to detect fraudulent transactions in real time. Automation can flag certain transactions based on predefined rules (e.g., transactions over a certain amount). However, AI goes beyond this by learning patterns of normal behavior and flagging transactions that deviate from those patterns. AI can detect fraud that would be missed by traditional rule-based systems, improving security and reducing financial risk.

There are several key benefits to integrating AI with other business process automation technologies.

1. Increased efficiency: AI can process vast amounts of data quickly, identifying patterns and optimizing workflows. This leads to faster, more accurate results. Automating with AI frees up employees to focus on more value-added tasks and improves employee experiences. It also reduces errors by cutting down on copy-paste chaos during data entry, so less rework is needed.
2. Cost reduction: By reducing human error, streamlining processes, and minimizing downtime, AI-driven automation helps businesses cut costs. Predictive maintenance, for example, can significantly reduce the cost of unplanned machinery breakdowns. By adding AI to compliance processes, banks can reduce the risk of being fined by regulators.
3. Better decision-making: AI enables businesses to make data-driven decisions faster and more accurately. From predicting customer behavior to spotting financial fraud, AI helps businesses stay ahead of the curve.
4. Scalability: AI-powered automation can scale with your business. Whether you're handling customer service, finance, or HR, AI-driven automation can handle increasing amounts of data without compromising accuracy or speed.
5. Improved customer experiences: With AI, businesses can provide more personalized and efficient customer service. AI chatbots, for instance, can handle a wider range of customer interactions, leaving them more satisfied and allowing employees to focus on complex issues.

Artificial intelligence and broader automation tools like software robots and document extraction tools work best when combined into intelligent automation. By leveraging AI’s ability to learn, adapt, reason, and create, businesses can make their automated processes smarter and more efficient. Whether it’s in customer service, HR, or finance, intelligent automation offers enterprises a way to streamline processes, cut down on busy work, reduce costs, and improve decision-making.

As artificial intelligence grows, AI workflow automation will have more possibilities. Consider that autonomous AI agents are now able to act as digital workers, making decisions within a set of boundaries to perform tasks with speed and accuracy. Businesses that embrace the combination of artificial intelligence and automation will be well-positioned to thrive in an increasingly competitive world.