The Intelligent Automation Services RadarView 2020 edition addresses both the conventional expectations of the enterprises and the new strategic requirement of the industries to comprehend both the RPA and the AI automated automation space separately and comprehensively. It is the desire of every business enterprise to build up more automation services with a view to improve productivity and at the same time minimize human input. This desire has driven the creation of a comprehensive dashboard solution architecture for enterprise intelligence solutions. The main objective of this project is to make the dashboard solutions of the major enterprise software products available to end-user users at a single point of time and at an affordable price level. Moreover, this will also facilitate a single platform for all businesses across the industries for better collaboration and sharing of information and data.
Automation technology is now converging with cognitive technologies for improved customer and employee satisfaction. The enterprise solution should support real-time and deep integration of diverse input data from diverse sources in order to provide improved and complete solutions. Therefore, the dashboards must not only be user-friendly but must also be able to adapt to the changing organizational environment. This implies that the dashboards should be dynamically responsive to the varying organizational requirements. For instance, the present trend is towards a more personalized approach in the form of natural language processing (NLP) and social intelligence. In such a scenario, the intelligent automation should be able to generate different types of customized reports tailored specifically to the organizational requirements and the divergent business factors that dictate the course of action.
Today, the majority of enterprises are leveraging the power of artificial intelligence (AI) and machine learning for improving decision-making. This implies that the dashboards for the enterprise needs to support natural language processing, financial intelligence, manufacturing, health care and other verticals. However, the cost of implementing such an approach is prohibitive for all but a few organizations that have robust IT budgets. Hence, the dashboards must also be robust enough to support and respond to any number of complex and rapidly changing inputs.
Another important trend that has been growing steadily over the last decade is the adoption of an “artificial intelligence second tier” approach to enterprise dashboard deployment. This approach integrates traditional business intelligence (BI) technologies with machine learning in order to derive and apply insights from the massive amounts of data produced by these technologies. In effect, this enables organizations to leverage on the capabilities of both artificial intelligence and machine learning for improving decision-making. The end result is an increase in speed, efficiency, and accuracy with reduced costs. However, the combination of multiple technologies typically requires an expert developer to build a deep understanding of each technology in order to properly integrate the solutions into an enterprise dashboard.
Similar to the latter example, the process automation industry has recently been revolutionized by a new concept – process reengineering. This approach uses highly advanced machine learning technologies for improving decision-making while simultaneously reducing cost. Rather than relying on a single solution for all business functions, process reengineering leverages multiple solutions for various business activities. A prime example of this technology is the use of artificial intelligence to automatically identify bottlenecks in an organization and then applies optimization techniques in order to speed up the process and lower overall costs. Consequently, it not only allows a business to utilize all the benefits of machine learning, it also significantly reduces cost.
Automation has evolved to a point where it is now possible to replace a human resource manager with a machine learning expert to effectively reduce redundant activities and streamline operations. Automation has also enabled businesses to effectively reduce their need for highly skilled staff by effectively automating business processes. Although repetitive tasks still play a critical role in ensuring that the modern organization runs smoothly, the impact of automation can no longer be ignored.