What Is Intelligent Automation?

Intelligent automation can revolutionize business operations by combining automation technologies and AI to improve efficiency, save costs, and enhance accuracy. Data shows almost half of businesses use automation in some way to reduce errors and speed up manual work. Intelligent automation takes this trend to the next level. It is essential for businesses to understand its definition and various applications as it becomes table stakes for companies worldwide.

What Is Intelligent Automation (IA)?

Sometimes called intelligent process automation, intelligent automation combines artificial intelligence (AI) and automation to improve and streamline business processes. Intelligent automation uses a combination of techniques, such as robotic process automation (RPA), machine learning (ML), and natural language processing (NLP), to automate repetitive tasks, and in the process, extract insights from data.

Intelligent automation can improve a business process by letting automation take on tasks such as data entry, document processing, and increasingly complex customer service responses. For example, an organization might use artificial intelligence–driven natural language processing and other machine learning algorithms to automate customer service interactions and quickly resolve queries with no human intervention. Or an insurance company might use intelligent automation to route documents through a claim process without employees needing to oversee it. Automations such as these and many others can be applied across a wide range of industries, including finance, healthcare, manufacturing, and retail. While intelligent automation can deliver significant benefits, it requires careful planning and execution to ensure success.

What Is Intelligent Automation (IA)?

• Artificial intelligence/Machine learning
• Integration platform
• Robotic process automation

Intelligent automation results in satisfied customers, lower costs, and business process improvement

Key Takeaways

• Intelligent automation is the application of advanced machine learning and artificial intelligence to automate repetitive manual tasks.
• Intelligent automation is being applied to both customer-facing operations, such as customer service, and internal operations, such as purchase order administration.
• The chief benefits of IA are speed, cost savings, and the elimination of errors in a repetitive task. IA can also identify trends and suggest improvements over time.
• One challenge of intelligent automation is technical, involving the integration of systems and the training of ML algorithms. The other is human, communicating organizational changes in the way people work.
• Implementing IA requires a combination of technologies, from integration and business process platforms to AI chatbots and Internet of Things sensors.

Intelligent Automation Explained

Intelligent automation is an evolution of business process automation. It’s made possible by the recent availability of cloud-based AI tools, such as machine learning, speech recognition, natural language processing, and computer vision. These allow businesses to automate tasks that were once thought too complex or human centric for machines to accomplish.

These tasks might include handling a customer service interaction using a chatbot that can understand intent and deliver answers using a natural language generator or successfully guiding a document through the many handoffs of an insurance claim. Both tasks are assisted by an AI model that’s trained on vast amounts data to make decisions and recommendations. This combination of robotic process automation and artificial intelligence can eliminate tasks that are repetitive yet not entirely predictable, improving a process while allowing employees to focus more on high-value and nuanced work.

How Does Intelligent Automation Work?

Intelligent automation systems are designed to help businesses work more efficiently. For example, an intelligent automation process might help a customer get a quick answer from a chatbot without human intervention, or a business partner receive an automated purchase order based on low inventory levels. It does this by enabling a workflow that tracks business data in real time and then uses artificial intelligence to make decisions or recommend best next steps. It’s designed to assist and augment human decision-making by presenting facts organized to help make better decisions or by taking on repetitive tasks that otherwise sap an employee’s time and focus.

The main tools involved in intelligent automation are business process automation software, operational data, and AI services. The AI algorithms are trained using operational data so they can take on a variety of tasks, such as providing computer vision to an inventory process, adding anomaly detection to fast-paced financial transactions, or providing nuanced chatbot responses in customer conversations and handing off to an employee at the right time.

In all these cases, intelligent automation helps bring calm efficiency and fewer errors to a business’s hectic day-to-day transactions. Meanwhile, the machine learning algorithms can learn over time to detect trends in the business data and even suggest improvements to a workflow.

Why Is Intelligent Automation Important?

Intelligent automation is important because it helps businesses find a higher level of efficiency, even as it enables more connection with customers and other stakeholders. With AI-driven sight, sound, language, and responsiveness, intelligent automation can let companies foster better customer relationships, let employees avoid busy work, and present them with information to confidently make decisions in the midst of fast-paced business operations.

Imagine the competitive advantage of a manufacturing automation that predicts an imminent breakdown, orders the parts, and schedules the maintenance—all based on the collection of daily business data and requiring no time from a human expert. Or a financial close operation that understands context in text and stores documents to meet regulatory compliance. Examples abound in industries as different as banking, shipping logistics, or fashion retail. The advantages continue as the machine learning algorithms that drive intelligent automation constantly learn from their data sets, improving or suggesting process design optimizations over time.

Intelligent Automation vs. Robotic Process Automation (RPA)

Intelligent automation encompasses more than just robotic process automation (RPA). RPA is a type of automation that uses software robots to mimic human actions and automate repetitive tasks. Intelligent automation, on the other hand, combines several technologies, including integration platform as a service (iPaaS), artificial intelligence (AI), machine learning (ML), and natural language processing (NLP), to automate complex processes that require humanlike decision-making. Intelligent automation not only automates repetitive tasks but also assists humans in making better decisions by providing insights, recommendations, and predictions based on the analysis of large data sets.

9 Components of Intelligent Automation

Intelligent automation is a combination of integration, process automation, AI services, and RPA technologies that work together to execute repetitive tasks and augment human decision-making. Intelligent automation can include NLP, ML, cognitive automation, computer vision, intelligent character recognition, and process mining.

1. Artificial intelligence (AI): The ability of machines, trained on large data sets, to perform tasks that typically require human intelligence, such as perception, language, learning, and problem-solving.
2. Integration platform as a service (iPaaS): A cloud-based platform that lets organizations integrate different applications, systems, and data sources, and automate workflows across their IT ecosystem.
3. Robotic process automation (RPA): Software bots that can perform repetitive tasks, such as data entry, invoice processing, and customer service responses, with high accuracy and speed.
4. Natural language processing (NLP): The ability of machines to understand, interpret, and generate human language, enabling them to interact with humans through chatbots, voice assistants, and other conversational interfaces.
5. Machine learning (ML): A subset of AI that lets machines learn from data and improve their performance over time without being explicitly programmed.
6. Cognitive automation: The combination of AI and RPA technologies to automate complex decision-making processes that require cognitive abilities, such as pattern recognition and reasoning.
7. Computer vision: The ability of machines to interpret and analyze visual data from the real world, enabling them to perform tasks, such as object recognition, image and video analysis, and autonomous navigation.
8. Intelligent character recognition (ICR): The ability of machines to recognize and interpret handwritten or printed text, enabling them to automate tasks, such as data entry and document processing.
9. Process mining: The use of AI and data mining techniques to analyze and optimize business processes, enabling organizations to identify inefficiencies and improve operational performance.

Benefits of Intelligent Automation

Using intelligent automation, an organization can increase productivity and efficiency, improve the customer experience, lower costs, and make better decisions faster. The goal is not to replace human experts but to free up their time for the kinds of strategic and nuanced activities that help grow the business.

1. Improved customer satisfaction: Delivers faster and more accurate customer service responses using natural language generation and freeing human employees to give more time to the toughest responses.
2. Reduced costs: Shrinks the time needed for common tasks, eliminating the need to pay skilled employees for mundane work and automating tasks that can include combing vast amounts of data and documents.
3. Improved decision-making: Provides real-time insights and data analytics, enabling better decision-making on the fly and more informed business strategies.
4. Increased agility: Helps businesses track and quickly adapt to changing market conditions and customer needs, enabling them to stay ahead of the competition.

Challenges of Intelligent Automation

Intelligent automation presents many challenges due to the complexity of the technology and its continuous evolution, and that artificial intelligence is still fairly new as an everyday enterprise software tool. When it comes to implementing intelligent automation, think of the challenges in two main buckets—technical challenges and organizational challenges.

Technical challenges include:

• Integration complexity: Intelligent automation often requires integration with existing systems and processes, which can be complex and time-consuming. This can include integrating IA solutions with legacy systems, managing multiple vendors, and ensuring smooth data flows across systems.
• Data quality issues: Intelligent automation relies heavily on high-quality data, and organizations may struggle with data silos, incomplete or inaccurate data, or data security concerns. Poor data quality can make it difficult to achieve the full potential of IA solutions.
• Governance and compliance challenges: Organizations must ensure that IA solutions meet regulatory requirements and comply with internal policies and procedures.
• Integration with legacy systems: Many organizations have legacy systems that are not designed to work with modern intelligent automation technologies, making integration challenging and potentially requiring costly technical expertise and custom development work.
• Security concerns: IA solutions can introduce new security risks, particularly when it comes to handling sensitive data or interacting with other systems. Organizations must ensure that these systems are secure and compliant with relevant security standards and protocols.

Organizational challenges include:

• Lack of executive leadership and sponsorship: Executives must provide the vision, resources, and leadership to ensure intelligent automation initiatives are successful.
• Limited understanding of IA potential: Many organizations may not fully understand the capabilities and potential of IA, leading to missed opportunities or unrealistic expectations. Leaders must educate themselves to realize the full potential of these technologies.
• Talent gaps: There may be a shortage of skilled professionals who can design, implement, and maintain IA, making it difficult to scale these initiatives.
• Change management: IA can lead to significant changes in processes and job roles, which can be difficult for employees to adapt to without proper communication and support. Organizations must ensure that employees understand the benefits of IA and are prepared for changes.
• Return on investment uncertainty: Measuring the ROI of intelligent automation initiatives can be challenging, particularly when it comes to soft benefits, such as increased productivity or improved customer experience. Organizations must establish clear metrics and goals for IA initiatives and regularly evaluate and report on their success.

Intelligent Automation Use Cases

Intelligent automation has a wide range of applications across industries, including healthcare, finance, retail, manufacturing, and logistics and transportation. Examples include:

1. AI-driven chatbots can be used for customer service inquiries and help desk questions, which can deliver efficient resolutions and a consistent experience. This type of chatbot “…empowers us to quickly deliver solutions for emerging channels while ensuring that our customers have a consistent experience, regardless of how they interact with us,” says Michael Menendez, Vice President of IT at Exelon Corporation.
2. ML algorithms combined with Internet of Things (IoT) sensors to run predictive maintenance on a factory floor.
3. Digital bots that automate repetitive tasks, such as data entry, order processing, or invoice generation, to make processes more efficient while delivering real-time feedback on the business.
4. Computer vision that can support activity tracking, such as inventory level, work-in-progress, and vehicle movements, in manufacturing and transportation.

Intelligent Automation Technologies

Intelligent automation is achieved by tying together a set of technologies to perform a complex task. Building around a process automation platform, these technologies include:

1. Robotic process automation (RPA): Software bots that perform simple, repetitive tasks with high accuracy and speed, such as data entry or document management.
2. Machine learning bots: Software programs that integrate with business systems to simplify repetitive tasks based on specific business rules.
3. Internet of Things (IoT) systems: Devices, sensors, and software that connect and communicate through IoT using a centralized management application.
4. Chatbots: AI-driven chatbots that use natural language processing to grok intent in a text or voice communication and take appropriate action, including replying with conversational text or voice.
5. Text messaging applications: Texting is the chosen front-end for many customer service interactions or business notification processes managed by intelligent automation.
6. Integration services: These connect applications and data sources, which is necessary to automate and manage business processes using IA.
7. IT Infrastructure: IT infrastructure composes the back end of any intelligent automation process by providing the computing power, data management, data integration, and automation software.

Intelligent Automation Technologies

1. RPA platform
2. Machine learning/AI
3. Internet of Things
4. Chatbots/Natural language processing
5. Text messaging
6. Integration services
7. Infrastructure services

How to Implement Intelligent Automation

Intelligent automation begins with process automation and then brings in AI-driven tools to expand the scope of automations across the business. Along the way, people will need time and guidance on how to adjust their workdays to accommodate the new AI-driven processes. Here’s what the road to success looks like: First, visualize your end goal. Ask yourself, What would a process look like if you could remove performance bottlenecks and use AI to take on select, high-volume tasks?

Once you have your goal, learn or find expertise on the kinds of technology infrastructure that will allow you to design and track these processes and can provide algorithms you can tailor to your specific needs. From there, you can form a strategy and build partnerships. You’ll need to enlist in-house experts to walk through the finer points of business interactions to maximize the accuracy and value of your intelligent automation. Remember, the IA system will, in some cases, replace human decision-making and communication with clients, so keen insight into the process is important. Now, make sure your back-office IT and cloud partners are ready to scale up and evolve with you.

Next is change management. You’ll need to prepare your people. Part of any IA implementation is to redefine your organizational structure and prepare your culture. As automation increases, some manual tasks and client communication will be handled, and employee time will open up to focus on higher-value tasks and business relationships.

Finally, it’s time to execute and iterate. One of the benefits of intelligent automation is that the machine learning algorithms should continue to improve. Make sure you have a way to capture and implement possible improvements. It’s equally important to monitor user feedback and be prepared to make changes. Getting the most out of any intelligent automation requires a process of constant feedback and iteration.

The Future of Intelligent Automation

The future of intelligent automation will be closely tied to the future of artificial intelligence, which continues to surge ahead in capabilities. As it does, expectations from customers for faster results at lower costs will only increase.

The momentum among leading companies is already strong. Many are implementing intelligent automation successfully; others are experimenting and refining their strategies and preparing their organizations. Like any AI-supported program, intelligent automation is an investment in the future—and there will be false starts. But like all in-demand technology trends, look for cloud providers to begin to offer off-the-shelf systems for intelligent automation based on their software integration platforms and business process automation offerings.

Improve Processes and Decision-Making with Intelligent Automation

As part of the growing sophistication and practical applications of AI technologies, intelligent automation is poised to become a powerful competitive advantage. The time to get started is now. When you do, you’ll want a partner with a proven track record in enterprise integration and business process automation. Oracle has been helping businesses automate work processes for decades and has built that expertise into Oracle Cloud Infrastructure (OCI) services. You will find OCI integration services that connect applications and data sources to help you automate processes and centralize management. The service enables event-driven workflows to automate and accelerate approvals. OCI also offers cloud-based AI services trained to specific workloads, such as natural language processing, anomaly detection, and computer vision, which companies can apply as needed.

Intelligent Automation FAQs

What is meant by the term “intelligent automation”?

Intelligent automation is the name given to a business practice that uses a combination of techniques, such as robotic process automation (RPA), machine learning (ML), and natural language processing (NLP), to automate repetitive tasks, extract insights from data, and enable data-driven decision-making.

What are some examples of intelligent automation?

Intelligent automation can drive a customer service chatbot that understands the intent of text or voice questions and offers options. Another example might be a shipping or manufacturing process that uses computer vision to accurately identify objects and help workers make quick decisions on the fly.

Is robotic process automation (RPA) a form of intelligent automation?

No. While they are both used to automate tasks, you can think of intelligent automation as a smarter version of robotic process automation. Where robotic process automation uses digital bots to do simple, repetitive tasks, intelligent automation can do more subtle, human-centric tasks and provide responses in natural language when needed.

What industries use intelligent automation?

Intelligent automation is being used in nearly every industry, including insurance, investing, healthcare, logistics, and manufacturing. The application of intelligent automation is growing in pace with the surging capabilities of artificial intelligence.