Industry

How to make the move from basic to intelligent business process automation

Share this post:

According to a recent IBM Institute for Business Value (IBV) report on the evolution of process automation, organizations can be separated into three groups based upon the types of business process automation they use.

  • 52 percent of organizations surveyed are employing basic process automation, which is the automation of simple applications and data management tasks following predetermined pathways.
  • 27 percent of organizations are a step ahead, using advanced process automation that automates workflows across multiple systems with complex calculations that trigger downstream activities, often enabled by discrete AI capabilities.
  • 12 percent of organizations fall into the most advanced group and have already implemented intelligent process automation. This most advanced stage employs robots with autonomous decision-making capabilities that may interact with humans through a combination of advanced algorithms and multiple types of artificial intelligence.

Why evolve?

According to the IBV report, basic process automation can, “eliminate errors, reduce biases and perform transactional work in a fraction of the time it takes humans. These basic technologies have demonstrated up to 75 percent cost savings on repetitive tasks compared to human performance, with 25 to 50 percent being the generally reported outcome.”

By moving to intelligent automation, users can change not only the speed, but the scale at which work gets done. The report explains:

“AI-driven processes can automatically scan millions of documents in a fraction of the time a human could — if they had a few hundred lifetimes — enabling processes as varied as legal contract reviews, medical treatment decisions, claims analysis and fraud management. Intelligent automation systems can analyze data up to 25 times faster than the human brain, function around the clock every day of the week, and interact with employees and customers in natural language, all with incredible accuracy.”

Companies pursue intelligent automation to help them compete, not only by super-optimizing business processes, but also by enabling personalized customer experiences and improving forecasting and decision making.

How do you move from basic to intelligent automation?

To move from basic to intelligent automation, you should first be able to collect and analyze data from across your operations and combine that with data from enterprise resource planning (ERP) and customer relationship management (CRM) systems. This will provide full, real-time visibility into your operations. Then, you can apply machine learning algorithms to that data to make recommendations or automatic adjustments to workflows and decisions.

Claims processing is one example of how intelligent automation can be applied to a workflow. For example, an insurance company may be looking to process more claims with the same workforce while enabling employees to spend more time on critical, and potentially costly, cases. The company automates several tasks, processes and decisions using basic to advanced automation capabilities. But now it is looking to use machine learning to determine which claims can be automatically processed. Claims with a high confidence score, according to the model, would be automatically approved or rejected while those with a low confidence would be routed to a human for processing. To do this, the machine learning model must be trained using a large set of data including claims processes, decisions and outcomes from across the company’s operations.

Introducing IBM Business Automation Insights

is a new capability now available within the IBM Automation Platform for Digital Business that collects and continuously feeds end-to-end business data from one or more platform components (such as IBM Business Automation Workflow) into a data lake. Each component is preconfigured to understand the data flowing through the platform. This provides users with a 360-degree view of operations and enables artificial intelligence to improve automation projects like in the insurance example above.

IBM Business Automation Insights does four key things:

  1. Collect: It captures end-to-end business data from different components of the IBM Automation Platform for Digital Business into a data lake.
  2. Visualize: It provides real-time visibility to business managers through predefined or user-configured dashboards.
  3. Measure: It correlates and measures the data based on user-defined business and operational metrics.
  4. Learn and guide: It enables data scientists to apply machine learning to the operational data lake to make recommendations to business managers and knowledge workers on how to improve the efficiency and effectiveness of business processes and decisions.

Try it now

Download and install the no-charge developer edition of IBM Business Automation Insights today.

More Industry stories

IBM Cloud Garage helps Grupo Planetun improve auto inspection app capabilities

Investopedia describes “insurtech” (the term inspired by its commonly known cousin, “fintech”) as the use of technology to create savings and efficiency in the insurance industry. Investopedia also suggests that the insurance industry is ripe for innovation and disruption. At Grupo Planetun, we know this to be especially true in Brazil. In the Brazilian insurance […]

Continue reading

Why Serima Consulting developed its smart grid solution on IBM Cloud Private

Germany is in the process of completely transforming its energy sector at a pace unmatched by other industrialized nations. Nuclear power is phasing out as renewables are gradually taking over, according to Deutsche Welle, Germany’s international broadcaster. The country’s politically supervised shift in direction from nuclear and fossil fuels to renewable sources of energy is […]

Continue reading

How bioscientists tackle data overload to advance medical research

The International Center for Scientific Debate explains that the ability to better collect, store, organize, integrate, analyze and share biomedical data provides opportunities to advance the detection, diagnosis, treatment and prevention of disease. Yet the greatest challenge bioscientists face is how to handle the flood of information coming from various sources and every instrument that […]

Continue reading