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Cognitive Process Automation: AI Enabling Next Generation RPA Applications

  • ID: 4793216
  • Report
  • June 2019
  • Region: Global, Global
  • 34 Pages
  • Frost & Sullivan
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Cognitive Process Automation: AI Enabling Next Generation RPA Applications

In a connected world, customers across industries are looking for faster and more efficient ways to connect with brands. Companies at the same time are trying to cut manual processes and to automate menial and repetitive tasks in order to reduce their labor costs and to inculcate more efficiency into their business processes. Robotic process automation (RPA) as technology is accurately positioned to address these needs of companies and their customers alike.

Artificial intelligence (AI) as a technology has also been in focus and has been adopted across industries in order to develop self-learning capabilities among business processes. Building AI capabilities in RPA solutions has helped companies offload many of their complicated tasks to AI bots and to make reduce the scope of error in their backend processes

In brief, this research service covers the following points:

  • RPA and AI– An overview
  • Drivers and challenges for adoption of RPA in enterprises
  • Areas of RPA implementation
  • Use cases and industry activity
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1.0 Executive Summary
1.1 Research Scope
1.2 Research Methodology
1.3. Research Methodology Explained

2.0 Synergies Between RPA And AI
2.1 Robotic Process Automation Allows Companies to Automate a Vast Majority of Menial and Repetitive Tasks
2.2 Intelligent Process Automation will Remain an Undeniable Catalyst of Growth for Businesses in the Future
2.3 Artificial Intelligence Allows Integration of Cognitive Capabilities into RPA, Making it a True Replacement to Manual Workflow
2.4 Automated Chatbots are Highly Scalable and Help Companies Address Sudden Influx of Queries While Reducing MTTR

3.0 Drivers and Challenges
3.1 Business Leaders Globally are Largely Supportive of RPA Implementation and are Aware of Cognitive Automation Technologies
3.2 Favorable Amount of Budget is Being Allocated Toward RPA Adoption and the Spending on RPA is Expected to Grow
3.3 Efficiency Gains, Cost Savings, and Process Standardization are Some of the Key Motivators Behind RPA Implementation
3.4 Companies with Siloed Business Structure and Finding It Difficult to Deal With the Complexities Associated With Process Standardization

4.0 Patent Landscape
4.1 The US and Canada Account for a Major Share of Patents Registered in the Domain of RPA

5.0 Areas of RPA Implementation
5.1 The Transformative Impact of RPA Cuts Across Domains, Helping Companies Bring Efficiency and Automation to Manual Tasks
5.2 The Need for Accuracy in Maintaining and Recording Transactions is Fueling the Adoption of RPA Among Healthcare and BFSI Companies
5.3 Telecom Operators are Making Efforts to Reduce Their Operating Cost and to Handle Large Volume of Customer Requests Using RPA
5.4 RPA in Retail is Being Utilized to Automate Backend Processes with a View to Support Business Models Such as Automated Checkouts

6.0 Use Cases and Market Activity
6.1 Many Leading Players in the Area of RPA are Working Toward Integration of AI-based Functionalities in their Solutions
6.2 BFSI Sector has been one of the Leading Adopters of RPA Solutions Globally
6.3 RPA has Helped Companies Deal With Unstructured Data in Order to Save Time and Costs Associated with Manual Data Entries
6.4 Implementation of RPA has Resulted in Tangible and Significant Time Savings

7.0 Conclusions and Recommendations
7.1 Conclusions and Recommendations

8.0 Industry Contacts
8.1 Industry Contacts

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