Robotic process automation: A path to the cognitive enterprise Deloitte Insights

Anthony Macciola, chief innovation officer at Abbyy, said two of the biggest benefits of cognitive automation initiatives have been creating exceptional CX and driving operational excellence. In CX, cognitive automation is enabling the development of conversation-driven experiences. He expects cognitive automation to be a requirement for virtual assistants to be proactive and effective in interactions where conversation and content intersect. Although much of the hype around cognitive automation has focused on business processes, there are also significant benefits of cognitive automation that have to do with enhanced IT automation.

• By augmenting human cognitive capabilities with AI-powered analysis and recommendations, cognitive automation drives more informed and data-driven decisions.
• It also suggests a way of packaging AI and automation capabilities for capturing best practices, facilitating reuse or as part of an AI service app store.
• Accounting departments can also benefit from the use of cognitive automation, said Kapil Kalokhe, senior director of business advisory services at Saggezza, a global IT consultancy.
• Although much of the hype around cognitive automation has focused on business processes, there are also significant benefits of cognitive automation that have to do with enhanced IT automation.
• According to a 2019 global business survey by Statista, around 39 percent of respondents confirmed that they have already integrated cognitive automation at a functional level in their businesses.
• For example, an attended bot can bring up relevant data on an agent’s screen at the optimal moment in a live customer interaction to help the agent upsell the customer to a specific product.

Processors must retype the text or use standalone optical character recognition tools to copy and paste information from a PDF file into the system for further processing. Cognitive automation uses technologies like OCR to enable automation so the processor can supervise and take decisions based on extracted and persisted information. Where little data is available in digital form, or where processes are dominated by special cases and exceptions, the effort could be greater. Some RPA efforts quickly lead to the realization that automating existing processes is undesirable and that designing better processes is warranted before automating those processes. IBM Consulting’s extreme automation consulting services enable enterprises to move beyond simple task automations to handling high-profile, customer-facing and revenue-producing processes with built-in adoption and scale. Cognitive automation maintains regulatory compliance by analyzing and interpreting complex regulations and policies, then implementing those into the digital workforce’s tasks.

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It is worth noting that RPA’s ability to wring substantial process improvements from legacy systems, often at relatively low cost, can undermine the business case for large-scale replacement of systems or enterprise application integration initiatives. The integration of these components to create a solution that powers business and technology transformation. When implemented strategically, intelligent automation (IA) can transform entire operations across your enterprise through workflow automation; but if done with a shaky foundation, your IA won’t have a stable launchpad to skyrocket to success. Sentiment analysis or ‘opinion mining’ is a technique used in cognitive automation to determine the sentiment expressed in input sources such as textual data.

In its most basic form, machine learning encompasses the ability of machines to learn from data and apply that learning to solve new problems it hasn’t seen yet. Supervised learning is a particular approach of machine learning that learns from well-labeled examples. Companies are using supervised machine learning approaches to teach machines how processes operate in a way that lets intelligent bots learn complete human tasks instead of just being programmed to follow a series of steps. This has resulted in more tasks being available for automation and major business efficiency gains.

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It is rule-based, does not require extensive coding, and uses an ‘if-then’ method to processing. Our feature-rich capabilities empower businesses to automate complex tasks, gain actionable insights from data, and deliver personalized experiences to their users. You can think of RPA as “doing” tasks, while AI and ML encompass more of the “thinking” and “learning,” respectively. It trains algorithms using data so that the software can perform tasks in a quicker, more efficient way.

This is less of an issue when cognitive automation services are only used for straightforward tasks like using OCR and machine vision to automatically interpret an invoice’s text and structure. More sophisticated cognitive automation that automates decision processes requires more planning, customization and ongoing iteration to see the best results. Deloitte explains how their team used bots with natural language processing capabilities to solve this issue. You can also check our article on intelligent automation in finance and accounting for more examples.

Intelligent Document Processing (IDP), a form of intelligent automation enables accurate data extraction from various documents, streamlining information processing. CPA’s adaptive learning ensures continuous improvement, allowing it to adapt to dynamic business scenarios. By harnessing the power of NLP, IDP, and adaptive learning, CPA tools liberate humans from mundane and time-consuming tasks, enabling them to focus on higher-value initiatives and fostering a more productive and efficient work environment. As enterprises continue to invest and rely on technologies, intelligent automation services will continue to prove powerful additions to the enterprise technology landscape. “The ability to handle unstructured data makes intelligent automation a great tool to handle some of the most mission-critical business functions more efficiently and without human error,” said Prince Kohli, CTO of Automation Anywhere. He sees cognitive automation improving other areas like healthcare, where providers must handle millions of forms of all shapes and sizes.

For example, customer data might have incomplete history that is not required in one system, but it’s required in another. The ability to capture greater insight from unstructured data is currently at the forefront of any intelligent automation task. Among them are the facts that cognitive automation solutions are pre-trained to automate specific business processes and hence need fewer data before they can make an impact; they don’t require help from data scientists and/or IT to build elaborate models.

The applications of IA span across industries, providing efficiencies in different areas of the business. IA or cognitive automation has a ton of real-world applications across sectors and departments, from automating HR employee onboarding and payroll to financial loan processing and accounts payable. Cognitive automation has the potential to completely reorient the work environment by elevating efficiency and empowering organizations and their people to make data-driven decisions quickly and accurately.

“The shift from basic RPA to cognitive automation unlocks significant value for any organization and has notable implications across a number of areas for the CIO,” said James Matcher, partner in the technology consulting practice at EY. Automated process bots are great for handling the kind of reporting tasks that tend to fall between departments. cognitive process automation tools If one department is responsible for reviewing a spreadsheet for mismatched data and then passing on the incorrect fields to another department for action, a software agent could easily manage every step for which the department was responsible. You can also check out our success stories where we discuss some of our customer cases in more detail.

Intelligent automation is undoubtedly the future of work, and companies that forgo adoption will find it difficult to remain competitive in their respective markets. Cognitive automation utilizes data mining, text analytics, artificial intelligence (AI), machine learning, and automation to help employees with specific analytics tasks, without the need for IT or data scientists. Cognitive automation simulates human thought and subsequent actions to analyze and operate with accuracy and consistency. This knowledge-based approach adjusts for the more information-intensive processes by leveraging algorithms and technical methodology to make more informed data-driven business decisions. Robotic process automation is often mistaken for artificial intelligence (AI), but the two are distinctly different.

To deal with unstructured data, cognitive bots need to be capable of machine learning and natural language processing. A large part of determining what is effective for process automation is identifying what kinds of tasks require true cognitive abilities. While machine learning has come a long way, enterprise automation tools are not capable of experience, intuition-based judgment or extensive analysis that might draw from existing knowledge in other areas. Because cognitive automation bots are still only trained based on data, these aspects of process automation are more difficult for machines. However, once we look past rote tasks, enterprise intelligent automation become more complex.

What are the key differences between cognitive automation and RPA?

Intelligent automation simplifies processes, frees up resources and improves operational efficiencies, and it has a variety of applications. An insurance provider can use intelligent automation to calculate payments, make predictions used to calculate rates, and address compliance needs. However, simply automating rote tasks is not sufficient to deal with the continuous changes those enterprises face.

These tools can port over your customer data from claims forms that have already been filled into your customer database. It can also scan, digitize, and port over customer data sourced from printed claim forms which would traditionally be read and interpreted by a real person. In contrast, cognitive automation or Intelligent Process Automation (IPA) can accommodate both structured and unstructured data to automate more complex processes. CIOs are now relying on cognitive automation and RPA to improve business processes more than ever before. “We see a lot of use cases involving scanned documents that have to be manually processed one by one,” said Sebastian Schrötel, vice president of machine learning and intelligent robotic process automation at SAP.