IDC Identifies 3 Stages in the Intelligent Automation Value Chain for Organizations Implementing Process Automation Solutions

A new report titled, Intelligent Automation Services Value Chain, from International Data Corporation (IDC) describes the intelligent automation value chain and provides insight into service providers’ consulting-to-operating models for their clients’ automation journey. The report describes the three main stages of IDC’s intelligent automation value chain for services, identifies the enabling technologies and use cases for each stage, and provides insight into developing a process automation strategy and choosing a services provider.

Since process automation, or Robotic Process Automation (RPA), is an important part of most organizations’ digital transformation strategy, automating key tasks and decisions has given organizations the potential to increase business process and IT operations efficiency. It can also impact customer experience, business and manufacturing processes, and strategies. Because many service providers are already embedding automation into their business process outsourcing (BPO) and business process as a service (BPaaS) offerings, organizations are turning to these firms for help with their own automation efforts.

“Cost reduction and workforce and process efficiency are a few of the benefits that are driving major interest in automation across many organizations. While most organizations are still in the early phase of RPA and AI-enabled automation adoption, developing an overarching automation strategy and developing the right use cases that map to specific business and IT outcomes will be crucial to the successful adoption of intelligent automation in the near term,” said Ali Zaidi, research director, IT Consulting and Systems Integration Strategies at IDC.

The three states in the intelligent automation value chain that IDC identifies in the report include:

Basic Automation is the automation of rules-based tasks (units of work performed by a human or computer) and documented process rules across applications. These are generally repeatable tasks leveraging structured data and addressed with basic technologies like macros and scripts. Use cases include executing data manipulations, creating new documents, completing manual data entry, or extracting data from multiple sources.

Machine Augmented Decision Making is process automation (or RPA) enabled by software tools that are programmed to automate processes that were formerly performed by a human by following a predetermined set of rules. When exceptions arise while using RPA, both humans and machines address them. Use cases include analyzing and processing invoices, best recommendations, route and track work across ecosystem, and connecting data sources to tasks at runtime based on context.

Autonomous Decision Making or decision-centric process automation is enabled by systems or machines solving nondeterministic tasks by continuously receiving and analyzing data to discover patterns that predict a decision and offer a recommendation to improve it. Use cases include recommendation engines, unplanned outage prevention, customer onboarding medical diagnostics, and Intelligent virtual agents.

According to IDC,” Organizations seeking to build process automation capabilities should consider the entire intelligent automation value chain, including solutions based on self-learning systems and decisions, rather than just looking at basic process automation. The ability to identify potential use cases should be an important attribute when selecting an intelligent automation vendor.”

Download the IDC report, Intelligent Automation Services Value Chain.

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