How To Use Cognitive Automation To Optimize Operational Workflows

Employee onboarding is another example of a complex, multistep, manual process that requires a lot of HR bandwidth and can be streamlined with cognitive automation. Some predict that by the year 2020, over 90% of all data in the enterprise will be unstructured. Instead of focusing on complete workflows, organizations can start by optimizing a particular section of a workflow with the maximum data leakage and drop-offs to create an impact. These organizations can also consider low-code or no-code platforms that allow users to create applications with minimal coding, accelerating application development and can be cost-effective. A proper needs assessment enables leaders to understand whether cognitive automation can fit their organization well. It’s not an easy path, and there is no perfect solution, but I believe the benefits usually outweigh the risks.

Most companies struggle to extract information from unstructured data, although the potential to achieve zero-touch operations lies in their ability to handle it. This class of data further consists of subgroups; unstructured images in document form, unstructured texts, unstructured images in picture form, unstructured audio, and unstructured video. Each of the subgroups might pose different challenges or possibly different technical solutions when it comes to extraction. Inefficient workflows within an organization can bring about delayed payments, document frauds, dataset oversights, time-consuming decision-making processes and more. Cognitive automation leverages natural language processing, computer vision and machine learning algorithms to mimic human cognition.

Cognitive Automation Use Cases Highlighting its Importance

These are complemented by other technologies such as analytics, process orchestration, BPM, and process mining to support intelligent automation initiatives. Meanwhile, hyper-automation is an approach in which enterprises try to rapidly automate as many processes as possible. This could involve the use of a variety of tools such as RPA, AI, process mining, business process management and analytics, Modi said.

• This means using technologies such as natural language processing, image processing, pattern recognition, and — most importantly — contextual analyses to make more intuitive leaps, perceptions, and judgments.
• Upon claim submission, a bot can pull all the relevant information from medical records, police reports, ID documents, while also being able to analyze the extracted information.
• Then, the bot can automatically classify claims, issue payments, or route them to a human employee for further analysis.
• Craig received a Master of International affairs from Columbia University’s School of International and Public Affairs, and a Bachelor of Arts from NYU’s College of Arts and Science.
• Today’s modern-day manufacturing involves a lot of automation in its processes to ensure large scale production of goods.

Change used to occur on a scale of decades, with technology catching up to support industry shifts and market demands. Processing claims is perhaps one of the most labor-intensive tasks faced by insurance company employees and thus poses an operational burden on the company. Many of them have achieved significant optimization of this challenge by adopting cognitive automation tools. He focuses on cognitive automation, artificial intelligence, RPA, and mobility. The integration of these components creates a solution that powers business and technology transformation. For instance, Religare, a well-known health insurance provider, automated its customer service using a chatbot powered by NLP and saved over 80% of its FTEs.

The incoming data from retailers and vendors, which consisted of multiple formats such as text and images, are now processed using cognitive automation capabilities. The local datasets are matched with global standards to create a new set of clean, structured data. This approach led to 98.5% accuracy in product categorization and reduced manual efforts by 80%. In another example, Deloitte has developed a cognitive automation solution for a large hospital in the UK. The NLP-based software was used to interpret practitioner referrals and data from electronic medical records to identify the urgency status of a particular patient.

The parcel sorting system and automated warehouses present the most serious difficulty. They make it possible to carry out a significant amount of shipping daily. These processes need to be taken care of in runtime for a company that manufactures airplanes like Airbus since they are significantly more crucial. Managing all the warehouses a business operates in its many geographic locations is difficult. Some of the duties involved in managing the warehouses include maintaining a record of all the merchandise available, ensuring all machinery is maintained at all times, resolving issues as they arise, etc. Let’s see some of the cognitive automation examples for better understanding.

Thus, cognitive automation represents a leap forward in the evolutionary chain of automating processes – reason enough to dive a bit deeper into cognitive automation and how it differs from traditional process automation solutions. For instance, at a call center, customer service agents receive support from cognitive systems to help them engage with customers, answer inquiries, and provide better customer experiences. CIOs are now relying on cognitive automation and Chat PG RPA to improve business processes more than ever before. 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. For example, accounts payable teams can automate the invoicing process by programming the software bot to receive invoice information — from an email or PDF file, for example — and enter it into the company’s accounting system.

It imitates the capability of decision-making and functioning of humans. This assists in resolving more difficult issues and gaining valuable insights from complicated data. The cognitive solution can tackle it independently if it’s a software problem.

Adopting Cognitive Automation In Organizations

Sentiment analysis or ‘opinion mining’ is a technique used in cognitive automation to determine the sentiment expressed in input sources such as textual data. NLP and ML algorithms classify the conveyed emotions, attitudes or opinions, determining whether the tone of the message is positive, negative or neutral. The solution, once deployed helps keep a track of the health of all the machinery and the inventory as well.

Additionally, it can gather and save staff data generated for use in the future. Cognitive automation can then be used to remove the specified accesses. Once implemented, the solution aids in maintaining a record of the equipment and stock condition. Every time it notices a fault or a chance that an error will occur, it raises an alert. 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. “The whole process of categorization was carried out manually by a human workforce and was prone to errors and inefficiencies,” Modi said.

What are examples of cognitive automation?

While powerful, cognitive automation, like most artificial intelligence, has limitations and challenges. As the founder of a document processing startup, I’m thrilled by the potential it creates, but I also feel a responsibility to address its risks. Cofounder and CEO of Docsumo, a document AI platform that helps enterprises read, validate and analyze unstructured data. Upon claim submission, a bot can pull all the relevant information from medical records, police reports, ID documents, while also being able to analyze the extracted information. Then, the bot can automatically classify claims, issue payments, or route them to a human employee for further analysis.

With AI, organizations can achieve a comprehensive understanding of consumer purchasing habits and find ways to deploy inventory more efficiently and closer to the end customer. Through cognitive automation, enterprise-wide decision-making processes are digitized, augmented, and automated. Once a cognitive automation platform understands how to operate the enterprise’s processes autonomously, it can also offer real-time insights and recommendations on actions to take to improve performance and outcomes. Given its potential, companies are starting to embrace this new technology in their processes. 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.

We’ve invested heavily in image recognition and will continue to do so by incorporating deep learning in our platform to enable the robots to understand any screen, similar to the way humans do. Our image recognition engine uses powerful algorithms that are optimized to find images on screen in https://chat.openai.com/ under 100 milliseconds. This step involves combining information with past trends and rules to decide on a course of action. It can be easily split into two types; rules-based judgment and trends-based judgment. You can foun additiona information about ai customer service and artificial intelligence and NLP. Unstructured images (documents) require OCR/ICR capabilities to extract the data.

Before integrating cognitive automation, knowing if it is essential to your organization’s needs is crucial. By augmenting human cognitive capabilities with AI-powered analysis and recommendations, cognitive automation drives more informed and data-driven decisions. Its systems can analyze large datasets, extract relevant insights and provide decision support. In contrast, cognitive automation or Intelligent Process Automation (IPA) can accommodate both structured and unstructured data to automate more complex processes. The foundation of cognitive automation is software that adds intelligence to information-intensive processes. It is frequently referred to as the union of cognitive computing and robotic process automation (RPA), or AI.

Thus, the customer does not face any issues with browsing and purchasing the item they like. In case of failures in any section, the cognitive automation solution checks and resolves the issue. Else it takes it to the attention of a human immediately for timely resolution. Cognitive automation solutions can help organizations monitor these batch operations. Yet the way companies respond to these shifts has remained oddly similar–using organizational data to inform business decisions, in the hopes of getting the right products in the right place at the best time to optimize revenue. The human element–that expert mind that is able to comprehend and act on a vast amount of information in context–has remained essential to the planning and implementation process, even as it has become more digital than ever.

For the next step, enable your team with a comprehensive training and adoption module. When considering a purchase, security and compliance are some factors to keep in mind. For example, in accounts payables, SOX, or the Sarbanes-Oxley Act, ensures that proper attention is paid to the issues affecting accounts payable, including payables risk management. Data governance is essential to RPA use cases, and the one described above is no exception. An NLP model has been successfully trained on sufficient practitioner referral data. For the clinic to be sure about output accuracy, it was critical for the model to learn which exact combinations of word patterns and medical data cues lead to particular urgency status results.

Finally, there are unstructured videos, with data inputs that are seldom used in companies, and where technology still has a lot of catching up to do to interpret them. As cognitive automation learns from the data and improves its performance cognitive automation examples over time, this becomes the go-to option for companies with ever-changing requirements. In the incoming decade, a significant portion of enterprise success will be largely attributed to the maturity of automation initiatives.

Cognitive automation leverages different algorithms and technology approaches such as natural language processing, text analytics and data mining, semantic technology and machine learning. If your organization wants a lasting, adaptable cognitive automation solution, then you need a robust and intelligent digital workforce. That means your digital workforce needs to collaborate with your people, comply with industry standards and governance, and improve workflow efficiency. Intelligent virtual assistants and chatbots provide personalized and responsive support for a more streamlined customer journey.

These AI-based tools (UiPath Task Mining and Process Mining, for example) analyze users’ actions and IT systems’ data to suggest processes with automation potential as well as existing gaps and bottlenecks to be addressed with automation. Typically, organizations have the most success with cognitive automation when they start with rule-based RPA first. After realizing quick wins with rule-based RPA and building momentum, the scope of automation possibilities can be broadened by introducing cognitive technologies.

Essentially, cognitive automation within RPA setups allows companies to widen the array of automation scenarios to handle unstructured data, analyze context, and make non-binary decisions. Cognitive automation tools can handle exceptions, make suggestions, and come to conclusions. Cognitive automation performs advanced, complex tasks with its ability to read and understand unstructured data. It has the potential to improve organizations’ productivity by handling repetitive or time-intensive tasks and freeing up your human workforce to focus on more strategic activities. The company implemented a cognitive automation application based on established global standards to automate categorization at the local level.

Cognitive automation maintains regulatory compliance by analyzing and interpreting complex regulations and policies, then implementing those into the digital workforce’s tasks. It also helps organizations identify potential risks, monitor compliance adherence and flag potential fraud, errors or missing information. AI and ML are fast-growing advanced technologies that, when augmented with automation, can take RPA to the next level.

As a Director in the U.S. firm’s Strategy Development team, he worked closely with executive, business, industry, and service leaders to drive and enhance growth, positioning, and performance. Craig received a Master of International affairs from Columbia University’s School of International and Public Affairs, and a Bachelor of Arts from NYU’s College of Arts and Science. When introducing automation into your business processes, consider what your goals are, from improving customer satisfaction to reducing manual labor for your staff.

IA is capable of advanced data analytics techniques to process and interpret large volumes of data quickly and accurately. This enables organizations to gain valuable insights into their processes so they can make data-driven decisions. And using its AI capabilities, a digital worker can even identify patterns or trends that might have gone previously unnoticed by their human counterparts. Task mining and process mining analyze your current business processes to determine which are the best automation candidates.

There are a lot of use cases for artificial intelligence in everyday life—the effects of artificial intelligence in business increase day by day. With the help of AI and ML, it may analyze the problems at hand, identify their underlying causes, and then provide a comprehensive solution. RPA operates most of the time using a straightforward “if-then” logic since there is no coding involved. If any are found, it simply adds the issue to the queue for human resolution.

They can also identify bottlenecks and inefficiencies in your processes so you can make improvements before implementing further technology. A cognitive automation solution can directly access the customer’s queries based on the customers’ inputs and provide a resolution. Besides the application at hand, we found that two important dimensions lay in (1) the budget and (2) the required Machine Learning capabilities.

With 20% of the searches performed with mobile being voice-based, conversational interactions are set to become increasingly pervasive even in an enterprise context. UiPath tightly integrates cognitive technology from Stanford NLP, Microsoft, Google, and IBM Watson and has just announced a strategic partnership with Google Cloud Contact Center AI to deliver a no-touch center automation solution. There is a lot of excitement about how RPA can be used to automate more processes by discovering opportunities automatically. Concurrently, we are researching new possibilities to auto-generate process templates by studying in great detail the user-machine interaction and all of its traces in the system. RPA imitates manual effort through keystrokes, such as data entry, based on the rules it’s assigned. But combined with cognitive automation, RPA has the potential to automate entire end-to-end processes and aid in decision-making from both structured and unstructured data.

It also suggests how AI and automation capabilities may be packaged for best practices documentation, reuse, or inclusion in an app store for AI services. 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. By enabling the software bot to handle this common manual task, the accounting team can spend more time analyzing vendor payments and possibly identifying areas to improve the company’s cash flow. Unstructured images (pictures) are the type of input documents where a picture needs to be interpreted to extract information. For example, an engineering diagram of a building that needs to be converted into a bill of material rapidly due to the competitive nature of the bid process.

It is up to the enterprise now to incorporate it and use it the way it deems fit. Splunk has helped Bookmyshow with a cognitive automation solution to help them improve their customer interactions. Digitate’s ignio, a cognitive automation solution helps handle the small niggles in the system to ensure that everything keeps working.

Automation is a fast maturing field even as different organizations are using automation in diverse manner at varied stages of maturity. As the maturity of the landscape increases, the applicability widens with significantly greater number of use cases but alongside that, complexity increases too. In addition, cognitive automation tools can understand and classify different PDF documents. This allows us to automatically trigger different actions based on the type of document received.

This can aid the salesman in encouraging the buyer just a little bit more to make a purchase. To assure mass production of goods, today’s industrial procedures incorporate a lot of automation. Additionally, it assists in meeting client requests and lowering costs.

• Training AI under specific parameters allows cognitive automation to reduce the potential for human errors and biases.
• The scope of automation is constantly evolving—and with it, the structures of organizations.
• Unlike other types of AI, such as machine learning, or deep learning, cognitive automation solutions imitate the way humans think.
• This is why robotic process automation consulting is becoming increasingly popular with enterprises.
• Identifying and disclosing any network difficulties has helped TalkTalk enhance its network.
• Attempts to use analytics and create data lakes are viable options that many companies have adopted to try and maximize the value of their available data.

However, this rigidity leads RPAs to fail to retrieve meaning and process forward unstructured data. According to IDC, in 2017, the largest area of AI spending was cognitive applications. This includes applications that automate processes that automatically learn, discover, and make recommendations or predictions. Overall, cognitive software platforms will see investments of nearly $2.5 billion this year. Spending on cognitive-related IT and business services will be more than $3.5 billion and will enjoy a five-year CAGR of nearly 70%.

This “brain” is able to comprehend all of the company’s operations and replicate them at scale. Most businesses are only scratching the surface of cognitive automation and are yet to uncover their full potential. A cognitive automation solution may just be what it takes to revitalize resources and take operational performance to the next level.

We can shape cognitive automation into a force for good with responsible development. Upgrading RPA in banking and financial services with cognitive technologies presents a huge opportunity to achieve the same outcomes more quickly, accurately, and at a lower cost. Cognitive RPA can not only enhance back-office automation but extend the scope of automation possibilities. Cognitive RPA has the potential to go beyond basic automation to deliver business outcomes such as greater customer satisfaction, lower churn, and increased revenues. The human brain is wired to notice patterns even where there are none, but cognitive automation takes this a step further, implementing accuracy and predictive modeling in its AI algorithm.