Introduction to Cognitive Automation
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The document provides an introduction to cognitive automation, defining it as software capable of making judgments and handling unstructured data through machine learning, differentiating it from robotic process automation (RPA). It highlights the importance and benefits of cognitive automation in organizations, including cost savings and improved accuracy, along with its various applications, challenges, and integration with RPA. The content emphasizes continuous learning and robust decision-making as key factors for future-proofing automation technologies.
Introduction to Cognitive Automation
- 2. Agenda • What is Cognitive Automation • Importance of Cognitive Automation • How Cognitive Automation Works • Uses of Cognitive Automation • Difference between RPA & Cognitive Automation • Challenges / Risk in Cognitive Automation • Cognitive Automation landscape • Examples of Cognitive Automation
- 3. Why Cognitive Computing Now?
- 4. What is Cognitive Automation • “Cognition” means “judgment” or “perception,” so Cognitive Automation is software that can make judgments and perceive knowledge. • Cognitive Automation is software with the ability to perform more complex work that involves unstructured data (like images, documents, or PDFs). Cognitive Automation is powered by Machine Learning. • “Cognitive Automation” is a term that allows software companies, industry analysts, and software users to define the type of work that automation can do. • Cognitive automation is not machine learning. Cognitive automation leverages different algorithms and technology approaches such as natural language processing, text analytics and data mining, semantic technology and machine learning.
- 6. How Cognitive Automation Works • It starts with Robotic Process Automation, which enlists software ‘robots’ to perform complex, nested routines that cut across applications reducing errors and eliminating mundane, time-consuming tasks. • Then, comes cognitive services to give dynamic and robotic automation a “brain.” • The cognitive services capability to understand natural language, think, learn and get smarter over time. • It is commonly associated with Robotic Process Automation (RPA) as the conjunction between Artificial Intelligence (AI) and Cognitive Computing. By leveraging Artificial Intelligence technologies, cognitive automation extends and improves the range of actions that are typically correlated with RPA, providing advantages for cost savings and customer satisfaction as well as more benefits in terms of accuracy in complex business processes that involve the use of unstructured information.
- 7. Importance of Cognitive Automation • Organizations can realize costs savings through the effective use of cognitive process automation. • Decreased cycle times and improved throughput • Flexibility and scalability • Improved accuracy • Improved employee morale • Detailed data capture • Combining automation and cognitive technology represents a fundamental shift in the way organizations can deliver more value to customers and ultimately create new revenue streams. • Based on our experience, we believe that companies can expect more than 50% in savings for FTE activities and relevant cost reductions (from 30% to 60% for email management, quote processing, etc.)
- 8. Cognitive Automation in IT Companies • India to remain fastest growing IT market in 2016 and to reach 85.3$ Billion in 2019, says Gartner. • India has around 30 lakhs direct IT employees and 60 lakhs indirect employees today.
- 9. Uses of Cognitive Automation • Identifying specific products or objects within an image • Extracting and matching relevant data from unstructured documents • Synthesizing large volumes of information into concise descriptions • Paired with RPA, Cognitive Automation can automate more complex judgement activities like data entry and reconciliations, even when unstructured data is prevalent. • Cognitive Automation will ask for human assistance when it encounters something it cannot understand, and will learn from those escalations to continuously improve its ability to automate.
- 10. Difference between RPA & Cognitive Automation • RPA enables macro level task automation. Basically standardizing things which have “finite number of rules” or have a set workflow to them. • If one were to talk about automation task of a standard data entry operator (which is more to do with reading a standard form and filling an excel). RPA can take care of this problem easily as there is a finite rule set associated with problems. • Now comes the second set of problem or the more complicated scenarios. There are fields like law , accounting , researcher , risk practitioners , data analysts (where a lot of unstructured data is present ) and people who work on loads of data to understand meaningful information by inferences. This task is done by cognitive and can’t be done by RPA. • To sum it up Cognitive can automate tasks which are non standard , do not follow a finite set of rules and are considered “value additive” in the world today. • But even with the power of cognitive, at end of the day there are always some rules that need to be followed in large organizations. This is where RPA combines with cognitive.
- 11. Categories of AI Application
- 16. Challenges / Risk in Cognitive Automation • General Incremental Learning • Automatic Goal Setting , braking into multiple Goals • Semantic Understanding world Knowledge (Google word vector) • Collaborative decision Making from Unexpected situation • Absolutely fault tolerance. • Retaining the Human skill for basic Operations • Time to handoff to Human
- 17. Examples of Cognitive Automation Cognitive Adaptive Testing
- 18. Examples of Cognitive Automation Asset management
- 19. Examples of Cognitive Automation • RPA Can help in Document Redaction • Redact anything that follows a certain pattern, like a social security or credit card number. • Redact anything with a repeating pattern, like a name. • Redact all names given in a list; clients, potential vendors, mergers and acquisition targets, and so on. • However, redaction is not always that straightforward. Decisions need to be made based on the context. For example, in a sentence like “President lives in the White House”, there is hardly anything that needs redaction. However, “President met with Mr.Comey at the White House.” may call for redaction of “President”, “Mr. Comey” and “White House”. Similarly consider the token “39%” in the following sentences – “IRS’s maximum tax slab is north of 39%”, and “Apple’s offshore cash reserves are 39% of total assets.” • Cognitive Automation builds on RPA’s qualities and introduces an extra level of sophistication; contextual adaptation. Like a business adapting its strategy to dynamic market conditions, Cognitive Automation can adapt the rules it uses to redact information depending on evolutions in the context of the data and workflow it processes.
- 20. Examples of Cognitive Automation Service Delivery Automation
- 21. Examples of Cognitive Automation Email Automation
- 22. Key Considerations It’s never late to future-proof your RPA. Here are the key considerations to make your RPA to an iRPA (Intelligent RPA): • Continuous learning—Machine Learning models should be trained frequently to match the decision-making frequency depending on the diversity of the input data. • Robust Decision making—Enabling your RPA to take decisions on input data that was never encountered before. • Taking your OCR to next level—Making your OCR intelligent is key to making your RPA self-sustained.
- 23. Thank You & Q&A