The world of business technology processes is evolving rapidly. Intelligent process automation is emerging as a game-changing paradigm that converges multiple tools and technologies to optimize business process outcomes.
Over the last decade, there has been steady progress towards the automation of business processes under the umbrella of robotic process automation (RPA).
However, intelligent process automation represents a major step forward. It integrates artificial intelligence (AI) and related technologies to enable software to not only perform repetitive tasks, but to learn, adapt, and make decisions, mimicking human cognitive abilities.
The purpose of intelligent process automation is to offer organizations a path towards improved business efficiency by minimizing human involvement in simple tasks, reducing errors, and accelerating decision-making processes.
Intelligent process automation holds the potential to revolutionize how businesses operate in the digital age.
What are intelligent process automation solutions?
Intelligent process automation solutions can be defined as the convergence of software, AI, and machine learning approaches to automate manual operations.
These solutions extend beyond traditional automation by incorporating cognitive capabilities that enable them to replicate human behavior and decision-making processes.
Intelligent process automation solutions are able to handle unstructured data through technologies such as machine learning, natural language processing (NLP), and image processing.
Therefore, it’s best to think of intelligent process automation as a technology suite, rather than a standalone system. The following subsections will outline the key intelligent process automation tools and technologies.
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Robotic process automation
RPA is a technology in business process automation that creates software bots designed to mimic human interactions with digital systems. The primary goal of RPA is to automate routine tasks such as data extraction and cleaning through existing user interfaces.
By operating at the user interface level, it offers the shortest route to automation without requiring deep integration with underlying application code or databases.
RPA bots are assigned user IDs, allowing them to access systems, create documents, and perform calculations just like human employees. The bots can perform repetitive tasks, saving time across the enterprise.
Machine learning
Machine learning is a subset of AI that allows systems to learn and improve from experience without requiring explicit programing. It applies probabilistic frameworks to produce models that explain patterns in data.
The models require large volumes of data to function effectively. Some algorithms—such as neural networks—mimic the human brain’s problem solving abilities, allowing AI to perform autonomous actions based on collected data and previous outcomes. There are several types of machine learning:
- Supervised learning - Algorithms learn from structured and labeled datasets of inputs and outputs to make predictions on new, unseen data.
- Unsupervised learning - In this method, the algorithm observes and identifies patterns in unlabeled datasets and provides insights on inherent patterns or structures.
- Semi-supervised learning - This approach combines elements of both supervised and unsupervised learning. It uses a small amount of labeled data with a larger set of unlabeled data for model training.
- Reinforcement learning - This approach trains systems to make decisions and perform optimal actions through trial and error by interacting with an environment.
Natural language processing (NLP) & natural language generation (NLG)
NLP is a subfield of AI and linguistics that explores how machines can be used to understand and manipulate natural language text or speech.
As a discipline, NLP is concerned with making the human and computer interaction easy and efficient, and involves making computer systems perform meaningful tasks with natural and human-understandable language.
The aim of NLP research is to acquire knowledge on how humans understand and use language so that appropriate tools and techniques can be developed to make computer systems understand natural language and perform desirable tasks.
NLG is a technology that produces meaningful text from structured data. It converts internal representations into human-readable phrases, sentences, and paragraphs. NLG follows specific rules to translate data observations into free text, enabling seamless human-technology interactions.
Computer vision
Computer vision is a branch of AI that enables machines to interpret and understand visual information from digital images and videos.
It aims to replicate and even surpass human visual capabilities by teaching computers to see, observe, and comprehend the visual world. This technology goes beyond simple image capture, allowing machines to make inferences and automate tasks based on visual data.
Computer vision systems use advanced algorithms, deep learning and neural networks to identify, process, and analyze objects, people, and scenes in images and videos.
Recent advancements have led to significant improvements, allowing computer vision to outperform humans in certain detection and labeling tasks.
Optical character recognition (OCR)
OCR is a technology that enables computers to recognize text from images. Modern intelligent OCR systems can eliminate background noise, recognize handwriting, and even interpret complex layouts.
These tools use machine learning algorithms to adapt and improve their accuracy over time. They can extract structured data from various sources, including damaged or unclear handwritten documents, paper bills, and even zip codes on letters or license plates.
By incorporating extra textual information like document layout and visual cues, intelligent OCR systems can provide more accurate and context-aware text recognition. This makes them a valuable tool in digitizing and processing a wide range of physical documents.
What are the benefits of intelligent process automation?
Intelligent process automation offers a number of advantages for increasing operational efficiency, reducing errors, and improving accuracy.
- Increased efficiency - Intelligent process automation systems can work continuously, significantly accelerating process completion times. Tasks that might take humans hours or days can often be completed by intelligent process automation systems in minutes or seconds, leading to dramatic improvements in operational efficiency.
- Improved accuracy - Intelligent process automation uses AI to drive decision-making. These systems can achieve levels of accuracy that surpass human capabilities. Machine learning algorithms can analyze vast volumes of data to make precise predictions and decisions.
- Data-driven insights - Optimizing the collection, processing, and analysis of vast amounts of data can provide valuable business intelligence to organizations.
- Improved customer experience - Providing customers with a higher quality, more reliable product at superior speed and scale provides a more positive interaction. Faster, more accurate processes and consistent results translate to a better service and increased customer satisfaction overall.
- Enhanced compliance boosts business performance - Many industries are required to adhere to specific regulatory policies. Intelligent process automation can leverage task automation features to provide a more consistent approach to compliance.
Intelligent process automation examples
There are a number of different intelligent process automation practices that can be applied to businesses. This section will present three examples of intelligent process automation use cases:
Streamlining internal business processes
Intelligent process automation offers benefits for streamlining internal business processes across various departments. In human resources, IPA can automate employee onboarding, resume screening, and performance review.
Finance and accounting departments can apply intelligent process automation to automate accounts payable and receivable, reconcile financial statements, and process expense reports.
In IT Operations, intelligent process automation streamlines ticket routing, manages software updates, and monitors network performance. Supply chain management can benefit from automated inventory management, optimized logistics, and streamlined supplier communications.
Supporting customer service
Supporting customer service through intelligent process automation can transform customer interactions. IPA powers chatbots that reduce the need for direct human involvement across business processes.
Bots can automate tasks such as placing orders, paying and following invoices, database querying, and external service inquiries.
Analytics and business planning
Another key use case for intelligent process automation is analytics and business planning. One of the advantages to automating systems and processes is the increased use of data analysis to improve accuracy and accelerate production.
It also enables predictive analytics capabilities for sales forecasting, demand prediction, and human resource requirements. Intelligent process automation can also be applied to risk management, predicting how different process components will evolve over time and anticipating potential risks.
Tips for implementing intelligent process automation for legacy systems
Combining RPA with the self-learning capabilities of AI and automation technologies can provide technology solutions to improve business operations and customer interactions.
Many companies are recognizing the potential advantages of implementing intelligent process automation tools, especially when applied to large legacy systems or older business processes with complex integration patterns.
Intelligent process automation practices combine two different yet complementary technologies. For example, AI can help process a variety of unstructured data and build a knowledge base, which then can be utilized by RPA technology to automate tasks.
Similarly, NLP can standardize and extract relevant data from documents, emails, and transcripts from disparate sources and file formats.
As a general rule of thumb, it’s important to remember that you can’t have AI for legacy systems without APIs. APIs are what make the legacy systems accessible and compatible with modern digital services and tech stacks, including AI-powered solutions.
The APIs, too, must be AI-friendly—meaning they’re well-documented so they can access the right data to facilitate natural language queries from legacy systems.
Take OpenLegacy’s new AI Powered Chat for IBM i (AS/400) as an example. It allows users of legacy, on-premises IBM i (AS/400) systems to get answers, generate insights, and execute transactions using simple chat and voice interfaces—without the need for costly system upgrades or migrations.
They could, for instance, ask the solution questions or demand actions like:
- "List all contracts, orders, and products"
- "List all the orders that are open and for which there are enough products in stock to ship"
- "If I am selling two [PRODUCT NAME] to [CUSTOMER] what will the price be?"
- "List all contracts and tell me which one was the highest price"
The OpenLegacy AI Powered Chat for IBM i (AS/400) intelligently understands context and nuance to provide accurate, relevant answers and actions.
For instance, for question three above, it can account for things like bulk discounts that apply to specific customers. Or, for question four, while “highest price” is ambiguous, as both sales and contract prices may be included within the dataset, because the question is asked in the context of contracts that would be the answer it provides.
Intelligent process automation is just one route to modernization
Intelligent process automation platforms are a perfect fit for companies looking to make a digital transformation. This can encompass businesses with varying needs, from those with enormous tech stacks, large volumes of active customers, and vast amounts of data, to those simply looking to eliminate repetitive tasks and take advantage of AI automation capabilities.
However, decision-makers are not looking for one-size-fits-all systems. They expect business systems to be adaptive and finely tuned to provide insights that support decision-making.
A simple lift and shift approach to new technologies will not provide the desired efficiencies. Real change requires intelligent systems that adapt to changing business dynamics and customer expectations.
The OpenLegacy Hub together with OpenLegacy AI-Powered Chat for IBM i (AS/400) can play a pivotal role in transitioning to intelligent process automation systems.
OpenLegacy Hub offers hybrid integration to deliver a fast, simple, and risk-free way to generate, extend, and manage digital services from complex legacy and core systems to the cloud.
It also provides in-built flexibility—easily adjusting to your unique technology stack with customizable options for low/no/full code, language, end-point, and deployment pipeline.
The potential benefits of intelligent process automation has rightly created much excitement. Although RPA can provide substantial benefits, the mix of AI capabilities in intelligent process automation means that outcomes are sustainable across the enterprise.
To get started, it’s important to establish a set of organizational priorities and create a heatmap of intelligent automation opportunities across your business processes. From there, you can begin your intelligent automation journey by identifying pilot scenarios, creating roadmaps, and integrating relevant processes and technologies into your existing infrastructure.
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