Cognitive Automation: Free-Will Robots?

Find out the power of cognitive automation

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. Cognitive automation can uncover patterns, trends and insights from large datasets that may not be readily apparent to humans. To reap the highest rewards and return on investment (ROI) for your automation project, it’s important to know which tasks or processes to automate first so you know your efforts and financial investments are going to the right place. You can also check out our success stories where we discuss some of our customer cases in more detail. As mentioned above, cognitive automation is fueled through the use of Machine Learning and its subfield Deep Learning in particular.

These systems are capable of developing new patterns and relationships out of large scale data sources such as Big Data platforms. They are able to analyse in real-time, and create actionable, value-add insights from within billions of data points. Many technologies within these categories can be adopted and utilised across almost any industry. When combined within a single business, these capabilities work together to enable integrated automation. Since it has proven effects on saving time and effort, all while cutting down costs, it is expected that healthcare RPA will become a staple in the healthcare industry. Implementation of RPA, CPA, and AI in healthcare will allow medical professionals to focus on patients themselves.

Catalyzing Change: The Future of Work with Enterprise Process Automation

But, instead of using it to automate a process or reveal hidden information and patterns in large amounts of data, cognitive computing is meant to simulate the human thought process and assist humans in finding solutions to complex problems. Cognitive computing systems use artificial intelligence and its many underlying technologies, including neural networks, natural language processing, object recognition, robotics, machine learning and deep learning. The IBM Cloud Pak® for Automation include a single, expert system and cognitive automation meaning library of purpose-built automations – pre-trained by experts – and draws on the extensive IBM domain knowledge and depth of industry expertise from 14,000+ automation practitioners. Cognitive automation, or IA, combines artificial intelligence with robotic process automation to deploy intelligent digital workers that streamline workflows and automate tasks. It can also include other automation approaches such as machine learning (ML) and natural language processing (NLP) to read and analyze data in different formats.

However, it also increases the complexity of the technology used to perform those tasks, which is bad, argued Chris Nicholson, CEO of Pathmind, a company applying AI to industrial operations. „RPA is a technology that takes the robot out of the human, whereas cognitive automation is the putting of the human into the robot,” said Wayne Butterfield, a director at ISG, a technology research and advisory firm. CIOs also need to address different considerations when working with each of the technologies.

A good time for a (behavioural) change – Consulting Blog Deloitte Australia

Finally, cognitive computing can also help companies combat fraud by analyzing past parameters that can be used to detect fraudulent transactions. Cognitive computing and AI are often used interchangeably, but they are not the same thing. “If you add all these three things — start with the basic information, ontology and taxonomy, and then add some aspect of learning with reinforcement — then you’ll have a pretty decent system, which can interact with humans,” Sudhakar told Built In. Now let us look at some uses of cognitive automation in some of the common industries.

Personalisation of user engagements is also a well-known application for many technologies in the cognitive domain. Personalised ads and direct marketing are often underpinned by technologies such as analytics and machine learning. Many organisations have adopted next-best-offer / next-best-conversation programs which use big data and machine learning capabilities to drive consumer behaviour based on their individual circumstances. Natural Language Understanding (NLU) technologies accept natural language from users and determine the intent of speech, allowing for downstream systems to catch and fulfill the user’s requests.

Harnessing Advanced Data Validation Techniques in Accounts Payable Process Automation

By nature, AI requires large amounts of data for training machines to accomplish specific tasks, recognize patterns, and make decisions. A common introduction to AI is presented where data is extracted, processed, or loaded. Furthermore, intelligent cognitive automation is developed so that it can be used by business users with ease without the assistance of IT staff to build elaborate models. It builds more connections in the datasets allowing intuitive actions, predictions, perceptions, and judgments. This digital fabric is weaved to outshine other technologies with its capability to imitate human thinking thus learning the intent of a given process and adapting accordingly. According to the latest technology trends, the software industry is heavily dependent on intelligent process automation solutions and they are widely used by the testing community, service organizations, and testing product/tools firms to list a few.