Intelligent Automation, the next step in digitized enterprise, is the use of Artificial Intelligence and other smart tools to automate various operations of a business. Its benefits and use in businesses are being recognized world over.
Here’s what Intelligent Automation is all about.
Table of Contents
What is Intelligent Automation
Intelligent Automation is the use of intelligent technology such as Machine Learning (ML), Deep Learning (DL), Intelligent Character Recognition (ICR), Natural Language Processing (NLP), Process Mining (PM), and Data Mining (DM) into an interconnected and interoperable process that enables automated learning and adaptation in all business activities.
Intelligent Automation eliminates time-consuming labor, creates an efficient interface between humans and technology through applications such as chatbots, and evolves with use. The continuous learning aspect of AI enables accurate predictions and flagging of potential risks and threats to operations, which can, in turn, trigger automated remediation and course corrections in time.
How is Intelligent Automation different from RPA and Hyperautomation
Before we go into details of Intelligent Automation, it is important to understand the fundamental difference between Intelligent Automation, RPA, and Hyperautomation.
- RPA is a subset of Intelligent Automation. It is used to automate routine, repetitive, and predictable tasks through orchestrated activities that emulate human action. It eliminates time-consuming tasks such as swivel chair data entry. It is driven by rules.
- Intelligent Automation is the use of Artificial Intelligence tools to process higher-function tasks that require some level of reasoning, analysis, judgment, and decision. It is driven by AI and is in turn a subset of Hyperautomation.
- Hyperautomation is the interconnection of different automation tools serving multiple processes in order to create a common platform that unifies systems, data, and processes of an enterprise.
How does Intelligent Automation work
Intelligent Automation includes one or more of the following tools that serve multiple functions:
1. Intelligent Data Capture
Data is, building on Sherlock Holmes’ impatient cries, the clay that makes the bricks of an enterprise. The automated capture of data in the digital format and its classification and storage as a logical entity depends on intelligent processes that can recognize the data. OCR and ICR processes are increasingly leveraging AI and ML tools for smart capture of data from various sources.
An effective intelligent capture solution that is part of Intelligent Automation will:
- Extract structured, poorly structured, and unstructured data.
- Pull data from multiple sources.
- Classify extracted data according to pre-set rules.
- Make available the data for other
2. Intelligent Process Automation (IPA)
An efficient and successful company has structured processes that follow predictable steps and have (largely) predictable outcomes. Such processes can be easily automated to remove bottlenecks caused by manual delays in intermediate steps. Intelligent Process Automation (IPA) is a collation of technologies that help in such well-defined processes.
Intelligent Process Automation typically includes Digital Process Automation (DPA), Robotic Process Automation (RPA), and Artificial Intelligence (AI).
- Digital Process Automation or DPA, derived from Business Process Management practices, is the automation of various operations of a company and the optimization of the workflow. It typically involves the automation of tasks that involve human interaction such as in HR, management, sales, and marketing. It often involves external users such as customers, vendors, and other stakeholders, and helps in creating better user experiences. Some examples of the use of DPA include automatic background checks, transferring data across multiple applications (e.g. between ERP and ordering system), generating login credentials,, setting up accounts, and automatic email announcements.
- Robotic Process Automation or RPA is the automation of time-consuming, labor-intensive repetitive tasks that follow a predetermined set of rules. RPA is used to automate smaller processes that are part of larger, complex ones. RPA is frequently used in extracting information from invoices to input into ERPs.
- Artificial Intelligence or AI includes technologies such as ML, NLP, and computer vision, that enable systems to analyze, reason, judge, and decide based on available data. This is done by recognizing patterns in data and learning from past decisions to make increasingly intelligent choices.
3. Intelligent Communication Management
Communication is a critical aspect of business and includes internal interactions as well as communication with external vendors, clients, and customers. Intelligent tools are being increasingly employed in communication management in applications ranging from first-level customer support (e.g. chatbots), content creation, crisis management, and strategy development. This again leverages multiple tools such as OCR, Voice recognition, NLP, and ML.
4. Intelligent Data Management
The collection of all business information into structured databases is passé in the era of big data. AI and ML tools can manage data more intelligently than simply categorizing data in database tables. Intelligent data management uses tools from various areas of operation such as Business Intelligence (BI) and Online Analytical Processing (OLAP), Cluster Analysis, Network Analysis, Data Mining, NLP, ML, and cloud computing. It provides an efficient informational platform for better storage, security, analytics, and decision-making in various areas of business operation.
Benefits of Intelligent Automation
The use of Intelligent Automation in the mundane, labor-intensive activities of a company can result in significant cost savings. McKinsey showed that 45% of current paid activities that cost an equivalent of $2 trillion in total annual wages, can potentially be automated using AI tools. Furthermore, the manual performance of redundant, automatable tasks decreases the productivity of the company and low productivity can cost employers around USD 1.8 billion dollars annually.
Many repetitive, mundane, manual business processes eat up a lot of time, irrespective of the department and nature of work. For example, low-level, automatable tasks have been reported to consume 30% of IT departments’ time, 47% of the AP department’s time, and 75% of the time of HR and Payroll department staff. This naturally leads to time delays and associated penalties that have a ripple effect on the productivity of the team and company. Intelligent Automation can help avoid such delays and bottlenecks in the daily operations of the company.
It is said that a human being is likely to make 10 errors in every 100 steps when performing redundant work. Where the human brain can fail due to fatigue from repetitive action, Intelligent Automation can, in fact, improve in performance due to the deep and continuous learning processes involved. Intelligent Automation can not only eliminate errors but also increase the likelihood of predicting problems and bottlenecks through smart analytics, which can help in early resolution.
Intelligent Automation, through centralization of process and data management can enhance transparency across the board while also logically integrating the business functionalities spread out across the organization. Intelligent Automation can also set up security measures and traceability of information, which ensures better compliance with relevant regulations.
The adoption of Intelligent Automation by businesses around the world was found to have increased manifolds during the pandemic times. About 55 percent of products and/or services were found to be fully or partly digitized as of July 2020, compared to 35 percent in December 2019 and 28 percent in 2018. Nearly half of 800 executives surveyed accelerated the adoption of automation “moderately” during the pandemic, and roughly 20 percent reported “significantly increasing” automation. Intelligent Process Automation has been a key backbone in keeping businesses running with reduced staff, remote work, and digital coordination.
The ability to coordinate various Intelligent Automation tools into a larger hyperautomation platform within an enterprise can enhance data incoherence and eliminate process barriers.
Applications of Intelligent Automation
The procure-to-pay process is best suited for Intelligent Automation because of the existence of repetitive and time-consuming tasks. Vendor management, invoice management, and payment details from multiple sources and vendors lead to complicated manual management. With the increase in transaction volumes, and with more emphasis being paid in modern times to paperless and online transactions, the Intelligent Automation of the P2P cycle has assumed importance to businesses. Intelligent Automation of the P2P process streamlines the purchase process, reduces paper clutter, enhances the transparency of the invoice route, saves time and money, increases employee productivity, and improves vendor relationships.
The Q2C is the functional reverse of the P2P process; while the latter is associated with procuring products /services by the company, the Q2C deals with sales of products and services by the company. An Intelligent Automation-enhanced Q2C process can ensure quick and reliable cash flow, fulfillment of orders, and effective bill management. Specific Q2T tsks that benefit from Intelligent Automation include order fulfillment, new customer onboarding, and account provisioning.
The onboarding and offboarding of employees in an organization is a tedious process, especially when the organization exceeds a critical size defined by its competency. The management of employee paperwork, processing payment of remaining salary and expenses, and ensuring the safe return of company property are some of the activities in the offboarding process that, when performed suboptimally, can hurt the company. Onboarding activities such as initiation of the employee, employment paperwork, and HR management are important in preserving employee morale and loyalty. Intelligent Automation can be used to electronically capture information from documents such as resumes and employee records through robotic process automation (RPA) and automating communication with the employees (i.e., automated welcome emails).
A survey by Gartner in 2018 Customer Experience is the “new marketing battlefront.” While the elimination of humans from customer management is not considered a smart business move, Intelligent Automation can be used as a supplemental tool for first-level communication. Intelligent Automation chatboxes can save time and provide round-the-clock connectivity to the customer. It can identify and categorize topics of conversation for subsequent routing to the appropriate human agent.
The inventory control process includes activities such as generating work orders, creating invoices, and shipping. As the company scales up in operations or moves into an omnichannel of operation, AI can streamline complex back-office processes and prevent supply chain blocks.
Marketing is now an omnichannel activity with social media playing a vital role in enhancing visibility. Automated creation and posting of marketing content (including context-specific ads) can help with better reach and visibility to the company.
Adopting Intelligent Automation in an enterprise
Walter Lippman said that you cannot endow even the best machine with initiative. The initiative must come from the human behind the enterprise.
The adoption of Intelligent Automation in a business is not a trivial matter of changing technological tools. It requires an in-depth understanding of the core competence of the company, its business needs, and changes in the fundamental approach to the running of the business. Planning is, therefore, an essential prerequisite to the adoption of Intelligent Automation.
The key steps in the adoption of Intelligent Automation in a company’s portfolio are:
- Planning: What are the processes that would benefit from Intelligent Automation? Answering this is essential to deliver the value promised by Intelligent Automation and will serve as a baseline to stabilize, standardize, optimize and operate the Intelligent Automation tools.
- Tool assessment: There are various Intelligent Automation tools that are available in the market that can serve various segments of the industry. The budgetary restrictions, functionalities offered and service provision are some of the important factors to be assessed before choosing an Intelligent Automation tool.
- Installation of the Intelligent Automation solution: Once the tool(s) is/are chosen, it is installed with the assistance of the tool provide. The modification, adaptation of the Intelligent Automation tools to the requirements of the company’s activities and needs is an essential aspect here and must be discussed with the solutions provider before installation.
- Training: All stakeholders in the Intelligent Automation solution adopted by the company must be trained to operate/manage the Intelligent Automation. The training must be periodically updated to stay current.
- Performance audit: Even after the full deployment of the Intelligent Automation tool, periodic performance audits are required to ensure that the system is performing in accordance with the needs of the company. Such audits must be carried out with specific performance metrics that match industry and peer group benchmarks. These audits can be performed by the expert within the company or by the provider if such a service has been promised by them.
Nanonets for Intelligent Automation
Nanonets is an Intelligent Automation software that leverages OCR, AI and ML capabilities to automatically extract unstructured/structured data from PDF documents, images, and scanned files. Nanonets automation handles unstructured data without much difficulty and the AI also handles common data constraints with ease. The Nanonets AI also ensures a high accuracy while processing documents requiring minimal rework or revision.
Some specific benefits of using Nanonets as an Intelligent Automation solution are:
- The flexibility of using multiple data types
- Customizability and custom training of models to suit specific needs
- Dynamic learning of the ML engine for a better fit with the business activities
- No need for postprocessing, thus freeing employee time for better activities
- The Deep Learning and object detection techniques overcome common data constraints that affect text recognition and extraction
- Requires no in-house team of developers
Intelligent Automation is the future of business management. Intelligent Automation solutions can increase profits and productivity, enhance customer satisfaction, improve bottom lines, and build worker morale. When integrated as part of routine business management practices, Intelligent Automation can help with visualization, workflow automation, and no-code/low-code tools so that companies stay competitive in this increasingly digitized business world.