Intelligent automation overview

Optical character recognition (OCR), used to “read” paper or digital-based documents (like PDFs), is one example. Traditionally, OCR depended on pattern recognition software to identify letters and numbers. However, because fonts and especially handwriting can vary wildly, it had limits to how accurately it could do this.

The advent of machine learning in OCR negated this limitation. A machine learning neural network can “read” characters by identifying unique patterns in each one and learning new patterns to account for variation. Over time, this enables it to better consistently distinguish between Os and 0s or 5s and Ss—recognizing that a narrower circle matches the pattern of a zero more than a letter O. Some other applications of intelligent automation are:

• Document information extraction (DOX). The evolution of OCR. DOX can extract data formatted in headers and tables in spreadsheet files (structured data) as well as scanned documents (unstructured data). This enables enterprises to process large amounts of invoices, purchase orders, receipts, forms, applications, and others quickly and ensure they match other records.

• Smart customer interactions. Using natural language processing, intelligent process automation-powered chatbots understand and respond to customer inquiries with context-aware, tailored answers, which not only accelerates response times but also improves overall customer satisfaction while reducing the workload for support staff.

• Predictive maintenance. By continuously analyzing historical and real-time sensor data, intelligent process automation can detect early warning signs of equipment failure, allowing maintenance teams to schedule timely interventions and adjust service intervals dynamically, which reduces downtime and repair costs.

This shows how AI can automate previously manual processes to dramatically increase accuracy and speed. In this article, we’ll also explore how it leads to better decision-making, streamlined maintenance workflows, and more operational efficiency for an enterprise and its human workforce.

Components of intelligent automation

Intelligent automation consists of three main components. Together, they provide a strategic framework for how enterprises can increase operational efficiency:

1. Artificial intelligence (AI): Technology that enables computers to learn and solve problems like humans do. Fields of AI include machine learning, natural language processing, computer vision, and generative AI.

1. Business process management: This component encompasses the design, operation, and optimization of digitized business processes with formal rules.

1. Robotic process automation (RPA): Software robots that execute repetitive manual, low-level tasks like data extraction.

How does intelligent automation work?

To understand how intelligent automation works, it’s helpful to understand two more basic forms of automation first:

Traditional process automation is the use of software and technologies following programmed rules to automate repeated business processes. Integrating multiple automated processes creates an end-to-end process—this is known as workflow orchestration.

Robotic process automation is a primary component of BPA. It refers to bots programmed to emulate and copy human actions to complete repetitive tasks. By integrating them into business systems, RPA bots can automate a wide range of rule-based tasks.

Intelligent process automation is the next evolution. It integrates advanced AI techniques, including machine learning (ML) and natural language processing (NLP) capabilities, with robotic process automation to perform advanced tasks for which it isn’t necessarily preprogrammed. It can also infer the business context behind data and learn from its experiences, making it much more flexible and adaptable than older forms of automation. Examples:

• In the lead-to-cash process (the cycle of finding leads and turning them into paying customers), sales staff can use intelligent process automation to create sales orders from either structured data (like in Microsoft Excel spreadsheets) or unstructured data (like in scanned PDFs.) They can spend the time they save on providing personalized customer service instead.

• Within the hire-to-retire process (the cycle of finding employees and managing their journey until they exit), HR staff can use intelligent process automation to screen resumes during recruitment.

Why is intelligent automation important?

Intelligent automation is important because it merges the efficiency of automated, rule-based processes with advanced artificial intelligence capabilities that can analyze complex data and learn from experience. This enables enterprises to automate tasks that were previously beyond automation, leading to more consistency, better decision-making, and lower operational costs.

The benefits of intelligent automation

Intelligent automation enables enterprises to streamline processes and make better decisions, leading to these benefits:

• Increased efficiency, accuracy, and consistency: AI systems can run repetitive tasks at an increased rate of automation. This improves productivity and frees human workers to focus on higher-level, strategic activities.

• Improved ROI: With the cost reductions, time savings, and risk mitigation that comes with intelligent automation, enterprises that choose to invest in can expect significant ROI.

• Improved customer experience: Chatbots can offer personalized customer support 24 hours a day. The faster they can resolve their issues, the less likely they’ll consider a competitor.

Challenges of intelligent automation

The benefits are clear. However, enterprises that choose to adopt intelligent process automation should prepare for the effect it may have on their human workforce.

While some employees may feel liberated from menial tasks, others may feel threatened by it. Management can alleviate this anxiety by implementing policies that encourage employees to upskill and retrain. Ideally, an enterprise can emerge with more strategic and creative thinkers focused on innovation—their decision-making reinforced with insights provided by AI.

Enterprises contemplating AI adoption should also examine issues concerning bias in AI, transparency in how it draws conclusions, and accountability. These are all topics that an organization’s AI ethics committee can help define.

Another consideration is the technical debt that arises during the migration to AI technology while also maintaining older legacy apps. Factors like poor data quality, inadequate infrastructure, and skill gaps can all hinder the effectiveness of intelligent automation in an enterprise. AI can help organizations reduce years of technical debt by automating operational tasks, such as refactoring code, that developers must often handle.

Intelligent automation technologies

Intelligent automation emerges when enterprises apply AI technologies to traditional process automation. These technologies include:

• Artificial intelligence
  AI is the foundation of intelligent automation, enabling systems to process information, identify patterns, and make decisions without human intervention. Unlike traditional automation, which follows predefined rules and uses structured data, AI-driven systems can analyze complex data—both structured and unstructured—to extract insights and adapt to new information. This ability allows businesses to automate decision-making processes, enhance efficiency, and improve accuracy in ways that rule-based automation cannot.
• Machine learning
  Machine learning is a branch of AI involving complex algorithms. This enables advanced pattern recognition capabilities that go beyond traditional statistical methods. With large and diverse datasets, models can deliver insights that inform decision-making in real time. In addition to continuous learning, it can also adapt over time, factoring previous actions and outcomes in its recommendations.
• Natural language processing
  NLP is a subset of machine learning that specializes in language-based tasks and the unstructured voice and text data that come with it. NLP enables chatbots and virtual assistants to understand human commands through natural language, considering sentiment and intent to deliver tailored responses. The result is better customer service on behalf of the business. Automated document classification is another application of NLP. It enables faster routing and processing, easier compliance, and more efficient document management. Developers can also apply NLP during the coding process. It can help with everything from coding suggestions to debugging to document generation.
• Computer vision
  Computer vision is the application of intelligent automation to visual information (like image or video data). Enterprises can use this for tasks like the monitoring and inspection of wear on the transformers, substations, and powerlines of an electrical grid.

Intelligent automation use cases

Enterprises are already putting intelligent process automation to use. Here are a few examples:

• Finance and accounting
  The finance and accounting departments of businesses use intelligent automation to accelerate a range of workflows. With invoice processing, it can extract data from invoices and match them with purchase orders. With expense management, it can categorize employee expenses and flag anomalies for review. And with financial reporting, it can consolidate data from multiple sources, generate reports, and ensure compliance with regulations.

• Supply chain logistics
  Intelligent automation helps supply chains stay resilient by enhancing inventory management, automating stock replenishment, and optimizing shipping routes. This results in minimized delivery times and costs, reduced shortages and overstocking, as well as increased transparency through real-time shipment tracking.

• Customer service automation
  Enterprises are increasingly using intelligent automation to facilitate customer service. Chatbots and virtual assistants, for example, can handle customer inquiries and provide support. If an issue is too complex and they can’t, they direct it to a human agent in the appropriate department.

  AI can also analyze customer feedback and interactions to detect sentiment and improve response strategies. This is called sentiment analysis.

The future of intelligent automation

To adopt intelligent process automation is to remain competitive. Enterprises that find a strategy to orchestrate it with traditional process automation and agentic AI systems can expect to increase productivity, improve accuracy, and save on costs on their end-to-end processes.

AI agents can specialize in more complex tasks, enabling them to collaborate across different departments and organizations. Imagine one managing the inventory and delivery components of a supply chain and working with another to track the associated invoices and ledgers.

This shifts the burden of these repetitive workflows away from human teams, who are now free to focus on other, higher-level roles in R&D, corporate social responsibility, or technical support. These jobs require skills AI struggles with, like empathy, ideation, and nuance.

By investing in adopting intelligent automation and upskilling human talent, an enterprise can emerge with a workforce enhanced by AI—rather than one replaced by it. The collaboration between both can bring levels of efficiency previously thought impossible.