Intelligence automation – Top 7 technologies used by enterprises

Automation redefines how tasks are allocated and performed in a company. Intelligence automation (IA) was initially used in manufacturing and later in other functions in bits and pieces. However, it is now increasingly an integral part of different enterprise functions. Organizations are developing a healthy architecture of IA systems because of their critical potential as the critical vehicle for corporate transformation. The time has indeed arrived to embark on intelligence automation.

It’s no longer a question of automating or not! All companies have to integrate IA for their own benefit and survival, with such entities growing steadily. Over the past two years, the number of enterprises taking end-user process automation via the robotics process automation (RPA) route, the foundation block of the IA journey, has increased significantly.

RPA technologies have also evolved from piecemeal, isolated automation installations to a comprehensive, connected enterprise-class digital automation solution. The old doubts and concerns about RPA’s worthiness, manageability, and security are also long gone as they have been ably addressed and improvised significantly. Today’s RPA architecture is more robust, reliable and ready for the future.

As IA tools evolve and become smarter, we’ll also see them undertaking smarter, more complex tasks. This will further spur IA’s adoption across functions at an even higher level— even across areas such as planning, budgeting, analysis, and decision-making that were perceived to remain only within humans.

7 automation technologies in intelligence automation

1. Structured data interaction (SDI): These are traditional systems where integration involves exchanging well-structured information. Examples include system integration through RDBMS, data transformation tools, application programming interfaces (APIs) and web services.

2. Robotic Process Automation (RPA): This involves automating standardized, regulated system-based activities using scripts and other methods to support efficient business processes. It is suitable when performing a task or process is too expensive or inefficient for humans.

3. Machine Learning (ML): Machine Learning involves systems that learn by handling variations not anticipated upfront. These systems are trained on-the-go by assimilating data and decision learning and can make simple algorithm-backed predictions or classifications. A simple case could be a scenario where a well-defined identifier needs to be mapped to more descriptive / free form text, e.g. mapping a vendor name to the vendor ID on a system invoice. Vendor name may appear in different forms.

4. Natural Language Processing (NLP): NLP uses statistical methods and algorithms to analyze text and unstructured information to understand the meaning, feeling, and intent. A sample use case could be the customer service function, where a customer raises a support ticket in the form of free text that is analyzed to understand and determine levels of urgency, feeling or frustration and then determine the severity/priority of the ticket.

5. Natural Language Generation (NLG): It’s a technology that helps create text as we speak or write from structured information like fields and numerals. It is used where sections of financial analysis reports and insights are generated, e.g. numbers reflecting a company’s performance.

6. Chatbots and virtual agents: These are systems that can interpret voice/text in free form (chat) with standard predefined answers. A simple example is the customer service function where a chatbot could answer queries. These chatbots can continually learn and build vocabulary to interpret unstructured information.

7. AI-Decision Systems: These are systems that use a range of technologies, algorithms, and models to solve complex, inter-related decision-making issues. Deep learning systems and cognitive abilities can drive these to recognize patterns and apply statistical models and algorithms to make choices and decisions. These could also potentially address multiple decision points, e.g. determining the demand for certain products for a weather forecast geography/location, thereby helping to determine the inventory to be housed in a store and identifying the best possible location and route to be chosen for fulfillment.