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How does AI enhance RPA?

According to Gartner, in the next 12 to 18 months, 81% of IT organizations will automate more tasks and workflows. When asked why their company is investing in Robotic Process Automation (RPA), business and IT leaders responded that they want to free up employees who can do higher-value work.

So, what is Robotic Process Automation and how does it work?

Robotic Process Automation (RPA) refers to creating and deploying software ‘bots’ that mimic human actions and behavior on a computer. RPA bots are programs that can perform tasks such as reading and interpreting documents and images.

The scope of RPA solutions can be extended to:

  • Extracting data from structured and unstructured files
  • Analyzing system logs from business applications to identify processes
  • Automating repetitive tasks performed by employees
  • Triggering approval workflows based on predefined rules

Intelligent Automation – Embedding AI/ML Models in RPA Solutions

Artificial Intelligence and Machine Learning (AI/ML) algorithms leverage pattern recognition to classify and process data.

Machine Learning Models can be trained to recognize speech, identify speakers, separate text from images and videos, and process documents intelligently.

Extracting data from structured files can be handled by RPA bots without AI assistance. But ML models can train the bots to process semi-structured (ex: invoices, emails) and unstructured data (ex: scanned images and social media posts).

AI and Machine Learning Models help RPA bots to do the following:

  1. Rationalize applications in your portfolio and identify duplicate and redundant apps, workflows, services, and APIs.
  2. Eliminate duplicates and inform users about the changes.
  3. Prioritize applications based on criticality to business operations.
  4. Map each application with the best modernization strategy – rehost, refactor, rearchitect, rebuild or retire.
  5. Migrate, upgrade, or modernize the lowest priority applications first.
  6. Plan for downtime and inform users. Provide alternate and interim solutions to users to ensure business operations are not affected.
  7. Move users from legacy systems to the modern environment in phases. Perform load testing on refactored and rebuilt applications before going live.

Enabling Intelligent Automation with Cognitive RPA

Cognitive computing uses context extraction and sentiment analysis to understand human thought processes and emotions.
Cognitive RPA bots have integrated computer vision and machine learning capabilities. These bots can establish polarity – the sentiment from extracted information can either be positive or negative.

Applications of Cognitive RPA Bots:

  • Determine customer sentiment by analyzing social media posts, reviews on eCommerce sites, or customer chat conversations
  • Process invoices without human intervention leveraging ML models to understand custom fields and validate documents in multiple formats
  • Expedite insurance claims processing through accurate fraud detection
  • Automate Know Your Customer (KYC) validations to ensure faster customer onboarding for customers of banks and financial institutions
  • Match CVs with job descriptions to shortlist suitable candidates
  • Automate customer email classification, prioritization, and case creation
  • Reduce time to resolve IT tickets by deploying chatbots to automate ITSM operations
  • Integrate RPA bots within Microsoft Teams for self-service application provisioning

Value Drivers for Intelligent Automation

The Big Ideas 2022 Survey by ARK Investment Management identifies “Productivity Gains” as a top value driver for implementing AI-powered intelligent automation which is the next frontier for Robotic Process Automation. According to this survey, RPA and AI has the potential to increase output of global knowledge workers at an annual rate of 9% by 2030.

Customer Success Story – How we implemented Intelligent Automation at Enterprise Scale

A leading automotive retailer in the US, Cochran Automotive, had multiple digital assets and customer touchpoints. They had deployed chatbots, but the abandonment rate was more than 59%. Softura built a multi-channel chatbot embedded within Microsoft Teams to handle processes related to:

  • Inventory Management
  • Lead Management
  • Sales Process Automation
  • Service Management

The automation solution leveraged Azure Components such as Power Automate, Power BI (dashboards), Azure Bot Services and Cognitive Services to make automation accessible to all employees through Microsoft Teams.

Softura Bot Connector provided the training data, intents, deployment plan, conversational user interface and microservices that integrates with line-of-business applications such as ERP, CRM, and Dealer Management Systems.

The benefits of this cognitive RPA solution were:

  1. Reduced Mean Time to Resolve (MTTR) for customer enquiries and service requests
  2. Increased user engagement through self-service capabilities
  3. Improved Net Promoter Score (NPS) used as a measure of customer experience
  4. Handled more volume of service requests simultaneously

Conclusion

In conclusion, here are the key value drivers for implementing AI-powered Automation:

  1. Reduce costs associated with labor, staffing, overtime, and regulatory compliance
  2. Accelerate Order-to-Cash cycle by automating processes related to customer engagement and conversion
  3. Make automation accessible to employees within business communication platforms
  4. Improve customer and employee satisfaction by introducing self-service capabilities

This article is brought to you by Softura.

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