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Going Beyond RPA with AutomationEdge: Hyperautomation Platform and ServiceNow

RPA with AutomationEdge

Enterprise architecture and technology innovation leaders lack defined digital transformation strategy to scale automation with tactical and strategic goals. The need of the hour is to deliver end-to-end automation which goes beyond RPA by complimenting technologies. Meet our own platform AutomationEdge to reach Hyperautomation.

All the industries are currently facing various challenges due to massive disruptions due to digital transformation. Due to this fact, the technology leaders have created short-sighted view of going to automation instead of forming a long term Hyperautomation roadmap. The organizations have risked their credibility with this move. Even due to ‘n’ number of RPA (Robotic Process Automation) vendors, there have been hindrances in assembling RPA for the right processes. AI-based intelligence with integrated strategy might solve these problems as well.

By 2022, 65% organizations will have introduced robotic process automation, machine learning and natural language processing.

Enterprise architecture (EA) and technology enterprise leaders are getting pressurized by their business colleagues to focus on tactical enhancements for routine process automation with RPA. However, these process are extremely complex as parameters of routines and repetition are variable as per situation. The real challenge of routine process automation cannot be solved by siloed strategies.

Hyperautomation with AutomationEdge focuses on effective combination of complementary sets of tools which can integrate within functional and silo units to automate and augment business processes. Gartner has named Hyperautomation as trend for 2020 especially for customer facing units like banking, retail, etc.

Hyperautomation is enabled by DigitalOps which is a business process framework designated to measure and manage processes across the complete organization. AutomationEdge combined with ServiceNow offers various technologies as RPA, Workflow management, decision management, low-code platform etc.

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For organizations, we at DxSherpa Technologies help to capitalize on DigitalOps competencies and automation with AI

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Planning your Hyperautomation journey with ServiceNow and AutomationEdge

A roadmap is the basic of any implementation when it comes to long term sustenance and acts as a layout for desired business outcomes. The roadmap helps us optimize and automate DigitalOps.

Planning your Hyperautomation journey with ServiceNow and AutomationEdge

Defined Outcomes

The most important part is goal definition. Enterprise architects and business process owners must collaborate to set a vision for digital business initiative.

The process automation goals are based on three main objectives:

  1. Revenue: The key drivers for revenue (Process Enhancement, Task Automation, Improved Customer Engagement)
  2. Costs: Key parameters for optimized costs. The enterprise can automate tasks but also redesign some processes and reduce cost of errors
  3. Risks: Identify compliance risks. By redesigning and automating processes, risk of non-compliance can be reduced

Use cases are needed to be identified which can improve efficiency of a process. With an aim to transform business process experimentation is an key to deliver value.

Optimize process

The biggest myth is automation of a bad process can solve the problem. In fact it makes it worse. It is essential to structure business with high performance. This is especially applicable when it comes to automation of data related processes.

  1. Right-size the process IQ
  2. Industrialize and scale the core processes which drive products and services
  3. Enhance your process with structured and standardized data

Assemble with other tools

The DigitalOps aligns with business model driven automation to address the all the steps in process automation (discover, analyze, design, automate, measure and monitor). This step matches the tools with critical processes and technology which can be connected with end-to-end automation.

Assemble with other tools

ServiceNow Platform and AutomationEdge

Intelligent Now Platform has solid foundation of tools for orchestration and automation of processes within those tasks. The Now platform with AutomationEdge capabilities consolidates Integration, decision management, SecOps, governance, automation and advanced analytics.

The ServiceNow platform focuses on:

  1. Managing cross-organization processes that span between people, processes and machines improving digital workflow transformation
  2. Acts as a master orchestrator of process and managing tasks
  3. AutomationEdge support through triggering RPA bot/script to automate tasks within a process
  4. Monitoring metrics and creating analytics dashboard
  5. Provides direct integration with API, services and devices

AutomationEdge RPA and Hyperautomation

The AutomationEdge Hyperautomation bot uses noninvasive integration technology to automate routine, repetitive and predictable tasks through orchestrated and predefined UI interactions that replicate human actions.

The AutomationEdge bot focuses on:

  1. Delivering quick wins by automating routines and repetitive tasks
  2. Creating API facades with legacy applications. Use of non-invasive techniques of RPA to interact with legacy applications
  3. Transporting, consolidation and validation of data from disparate sources
  4. Rapidly experimenting with process prototype

Process Mining and Analytics

With an combination of ServiceNow and AutomationEdge, the platform is designed to discover, monitor and improve real processes by extracting knowledge from logs. This process includes discovery, conformance checking and analytics.

  1. Identify process inefficiency
  2. Discover, monitor and configure tasks
  3. Extract knowledge
  4. Create process documentation
  5. Repair or extend a model
  6. Process recommendation

To realize the business value, AutomationEdge and ServiceNow are a great combination that can deliver specific, measurable outcomes for targeted use cases. Collectively, the capabilities of AI and machine learning can make quantifiable business outcomes.

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