Cognitive Automation: What You Need to Know

As mentioned above, cognitive automation is fueled through the use of Machine Learning and its subfield Deep Learning in particular. And without making it overly technical, we find that a basic knowledge of fundamental concepts is important to understand what can be achieved through such applications. With light-speed jumps in ML/AI technologies every few months, it’s quite a challenge keeping up with the tongue-twisting terminologies itself aside from understanding the depth of technologies. To make matters worse, often these technologies are buried in larger software suites, even though all or nothing may not be the most practical answer for some businesses. Make your business operations a competitive advantage by automating cross-enterprise and expert work.

What’s important, rule-based RPA helps with process standardization, which is often critical to the integration of AI in the workplace and in the corporate workflow. For example, one of the essentials of claims processing is first notice of loss (FNOL). When it comes to FNOL, there is a high variability in data formats and a high rate of exceptions. Customers submit claims using various templates, can make mistakes, and attach unstructured data in the form of images and videos.

This article explores the definition, key technologies, implementation, and the future of cognitive automation. Irrespective of the concerns about this technology, cognitive automation is driving innovation and enhancing workplace productivity. Disruptive technologies like cognitive automation are often met with resistance as they threaten to replace most mundane jobs. Platform tools like Terraform and Ansible allow for version control and automation of infrastructure deployments. Infrastructure as Code (IaC) facilitates the supervision and provisioning of computing infrastructure via machine-readable configuration files rather than interactive configuration tools or physical hardware configuration.

How AI is Used in Fraud Detection – Benefits & Risks

Comparing RPA vs. cognitive automation is “like comparing a machine to a human in the way they learn a task then execute upon it,” said Tony Winter, chief technology officer at QAD, an ERP provider. Semi-structured information such as invoices and unstructured data such as customer interactions can be analyzed, processed, and classified into useful data fields for the next steps of automation. “To achieve this level of automation, CIOs are realizing there’s a big difference between automating manual data entry and digitally changing how entire processes are executed,” Macciola said.

This technology is behind driverless cars to identify a stop signal, facial recognition in today’s mobile phones. One concern when weighing the pros and cons of RPA vs. cognitive automation is that more complex ecosystems may increase the likelihood that systems will behave unpredictably. CIOs will need to assign responsibility for training the machine learning (ML) models as part of their cognitive automation initiatives. In healthcare, these AI co-workers can revolutionize patient care by processing vast amounts of medical data, assisting in accurate diagnosis, and even predicting potential health risks.

By using AI to automate these processes, businesses can save employees a significant amount of time and effort. By automating these more complex processes, businesses can free up their employees to focus on more strategic tasks. In the past, businesses used robotic process automation (RPA) to automate simple, rules-based tasks on computers without the need for human input.

Straight through processing vs. exceptions

AI-powered chatbots can automate customer service tasks, help desk operations, and other interactive processes that traditionally require human intervention. Cognitive automation is an aspect of artificial intelligence that comprises various technologies, including intelligent data capture, optical character recognition (OCR), machine vision, and natural language understanding (NLU). For enterprises to achieve increasing levels of operational efficiency at higher levels of scale, organizations have to rely on automation.

• The coolest thing is that as new data is added to a cognitive system, the system can make more and more connections.
• RPA uses technologies like screen scraping, workflow automation whereas Cognitive automation relies on technologies like OCR, ML and NLP.
• 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.

Text Analytics API performs sentiment analysis, key phrase extraction, language detection, and named entity recognition on textual data, facilitating tasks such as social media monitoring, customer feedback analysis, and content categorization. These services use machine learning and AI technologies to analyze and interpret different types of data, including text, images, speech, and video. Cognitive automation can automate data extraction from invoices using optical character recognition (OCR) and machine learning techniques. This makes it easier for business users to provision and customize cognitive automation that reflects their expertise and familiarity with the business. In practice, they may have to work with tool experts to ensure the services are resilient, are secure and address any privacy requirements. These systems are highly efficient in energy consumption and processing power, which aids scaling operations without a proportional increase in resource usage.

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. In this example, the software bot mimics the human role of opening the email, extracting the information from the invoice and copying the information into the company’s accounting system. These tasks can range from answering complex customer queries to extracting pertinent information from document scans. Some examples of mature cognitive automation use cases include intelligent document processing and intelligent virtual agents.

Cognitive automation has a place in most technologies built in the cloud, said John Samuel, executive vice president at CGS, an applications, enterprise learning and business process outsourcing company. His company has been working with enterprises to evaluate how they can use cognitive automation to improve the customer journey in areas like security, analytics, self-service troubleshooting and shopping assistance. For example, Digital Reasoning’s AI-powered process automation solution allows clinicians to improve efficiency in the oncology sector. With the help of deep learning and artificial intelligence in radiology, clinicians can intelligently assess pathology and radiology reports to understand the cancer cases presented and augment subsequent care workflows accordingly. Traditionally cognitive capabilities were the realm of data analytics and digitization. Robotic Process Automation (RPA) works best if you have a structured process, involves a large volume of data and is rule based.

The ability to capture greater insight from unstructured data is currently at the forefront of any intelligent automation task. “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 Chat GPT 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. Employee time would be better spent caring for people rather than tending to processes and paperwork.

RPA is typically programmed upfront but can break when the applications it works with change. Cognitive automation requires more in-depth training and may need updating as the characteristics of the data set evolve. But at the end of the day, both are considered complementary rather than competitive approaches to addressing different aspects of automation. By leveraging Artificial Intelligence technologies, this technology extends and improves the range of actions beyond those that are automated with RPA. Cognitive automation is also starting to enhance operational excellence by complementing RPA bots, conversational AI chatbots, virtual assistants and business intelligence dashboards.

RPA provides immediate Return on Investment (ROI) whereas Cognitive automation takes more time for realization. “Cognitive RPA is adept at handling exceptions without human intervention,” said Jon Knisley, principal, automation and process excellence at FortressIQ, a task mining tools provider. Cognitive automation is most valuable when applied in a complex IT environment with non-standardized and unstructured data. RPA has been around for over 20 years and the technology is generally based on use cases where data is structured, such as entering repetitive information into an ERP when processing invoices. While they are both important technologies, there are some fundamental differences in how they work, what they can do and how CIOs need to plan for their implementation within their organization.

You can foun additiona information about ai customer service and artificial intelligence and NLP. 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 coolest thing is that as new data is added to a cognitive system, the system can make more and more connections. This allows cognitive automation systems to keep learning unsupervised, and constantly adjusting to the new information they are being fed. These chatbots can understand natural language, interpret customer queries, and provide relevant responses or escalate complex issues to human agents. RPA developers within the CoE design, develop and deploy automation solutions using RPA platforms. They configure bots to mimic human actions, interact with applications, and execute tasks within defined workflows.

However, simply automating rote tasks is not sufficient to deal with the continuous changes those enterprises face. In order to provide greater value, these automation tools need to step up the ladder of cognitive automation, incorporating AI and cognitive technologies to see increased value. It goes beyond automating repetitive and rule-based tasks and handles complex tasks that require human-like understanding and decision-making. By leveraging NLP, machine learning algorithms, and cognitive reasoning, cognitive automation solutions offer a symphony of capabilities that revolutionize how businesses operate. 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.

Hyperautomation improves the customer experience through faster response times, more accurate results, faster time to market, and many other positive results that directly impact the customer and user experience. From healthcare to finance to manufacturing and beyond, the use of intelligent automation can provide benefits that improve the customer experience and impact the bottom line. Intelligent automation what is cognitive automation (IA) and hyperautomation are both contributors to the explosion of the use of AI-powered automation platforms and automation tools across the business and IT landscape. They both refer to the use of automation to streamline processes using advanced technologies and enhancements. In doing so, these tools help improve the quality of automation results and the quality of customer interactions.

RPA is a simple technology that completes repetitive actions from structured digital data inputs. Cognitive automation is the structuring of unstructured data, such as reading an email, an invoice or some other unstructured data source, which then enables RPA to complete the transactional aspect of these processes. While there are clear benefits of cognitive automation, it is not easy to do right, Taulli said. Then, as the organization gets more comfortable with this type of technology, it can extend to customer-facing scenarios. 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.

“With cognitive automation, CIOs can move the needle to high-value, high-frequency automations and have a bigger impact on the bottom line,” said Jon Knisley, principal of automation and process excellence at FortressIQ. IBM’s cognitive Automation Platform is a Cloud based PaaS solution that enables Cognitive conversation with application users or automated alerts to understand a problem and get it resolved. It is made up of two distinct Automation areas; Cognitive Automation and Dynamic Automation.

RPA bots can successfully retrieve information from disparate sources for further human-led KYC analysis. In this case, cognitive automation takes this process a step further, relieving humans from analyzing this type of data. Similar to the aforementioned AML transaction monitoring, ML-powered bots can judge situations based on the context and real-time analysis of external sources like mass media. A large part of determining what is effective for process automation is identifying what kinds of tasks require true cognitive abilities.

Cognitive automation can continuously monitor patient vital signs, detect deviations from normal ranges, and alert healthcare providers to potential health risks or emergencies. Automated diagnostic systems can provide accurate and timely insights, aiding in early detection and treatment planning. ML-based automation can assist healthcare professionals in diagnosing diseases and medical conditions by analyzing patient data such as symptoms, medical history, and diagnostic tests. Cognitive automation can optimize inventory management by automatically replenishing stock based on demand forecasts, supplier lead times, and inventory turnover rates. Organizations can mitigate risks, protect assets, and safeguard financial integrity by automating fraud detection processes.

It also improves organizations’ ability to achieve greater levels of automation in incident response, subsequently improving system resilience and reducing the need for manual intervention. Hyperautomation often employs other technologies — such as optical character recognition (OCR), intelligent document processing (IDP) and natural language processing (NLP) — to provide higher-quality automation using data from various sources. Digital twin or digital twin organization (DTO) are often used for modeling to improve operations and evaluate the impact of automation. Another way businesses can minimize manual mental labor is by using artificial intelligence (AI) to set up and manage robotic process automation (RPA).

“The problem is that people, when asked to explain a process from end to end, will often group steps or fail to identify a step altogether,” Kohli said. To solve this problem vendors, including Celonis, Automation Anywhere, UiPath, NICE and Kryon, are developing automated process discovery tools. No longer are we looking at Robotic Process https://chat.openai.com/ Automation (RPA) to solely improve operational efficiencies or provide tech-savvy self-service options to customers. Discover how our advanced solutions can revolutionize automation and elevate your business efficiency. One of the most exciting ways to put these applications and technologies to work is in omnichannel communications.

ML-based automation can streamline recruitment by automatically screening resumes, extracting relevant information such as skills and experience, and ranking candidates based on predefined criteria. This accelerates candidate shortlisting and selection, saving time and effort for HR teams. This accelerates the invoice processing cycle, reduces manual errors, and enhances accuracy in financial record-keeping.

Business processes like billing, customer interactions, inventory and payroll can use hyperautomation for Business Process Automation (BPA) to streamline the business on a broad scale. Cognitive automation tools such as employee onboarding bots can help by taking care of many required tasks in a fast, efficient, predictable and error-free manner. This can include automatically creating computer credentials and Slack logins, enrolling new hires into trainings based on their department and scheduling recurring meetings with their managers all before they sit at their desk for the first time. 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.

Know your processes

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%. Microsoft Cognitive Services is a platform that provides a wide range of APIs and services for implementing cognitive automation solutions. Implementing chatbots powered by machine learning algorithms enables organizations to provide instant, personalized customer assistance 24/7.

Businesses are increasingly adopting cognitive automation as the next level in process automation. Another benefit of cognitive automation lies in handling unstructured data more efficiently compared to traditional RPA, which works best with structured data sources. Advantages resulting from cognitive automation also include improvement in compliance and overall business quality, greater operational scalability, reduced turnaround, and lower error rates. By automating cognitive tasks, organizations can reduce labor costs and optimize resource allocation.

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.

Machine learning techniques like OCR can create tools that allow customers to build custom applications for automating workflows that previously required intensive human labor. It represents a spectrum of approaches that improve how automation can capture data, automate decision-making and scale automation. 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. While the trends discussed earlier pave the way for integrating advanced technologies like neuromorphic systems, this integration comes with its own set of complexities.

This flexibility makes Cognitive Services accessible to developers and organizations of all sizes. Microsoft Cognitive Services is a cloud-based platform accessible through Azure, Microsoft’s cloud computing service. Speaker Recognition API verifies and identifies speakers based on their voice characteristics, enabling applications to authenticate users through voice biometrics. This proactive approach to patient monitoring improves patient outcomes and reduces the burden on healthcare staff. This minimizes excess inventory, reduces carrying costs, and ensures product availability.

He expects cognitive automation to be a requirement for virtual assistants to be proactive and effective in interactions where conversation and content intersect. Cognitive automation can use AI to reduce the cases where automation gets stuck while encountering different types of data or different processes. For example, AI can reduce the time to recover in an IT failure by recognizing anomalies across IT systems and identifying the root cause of a problem more quickly.

Just about every industry is currently seeing efficiency gains, with various automation tasks helping businesses to cut costs on human capital and free up employees to focus on more relevant or higher-value tasks. Hyperautomation is the act of automating everything in an organization that can be automated. The intent of hyperautomation is to streamline processes across an organization using intelligent process automation (IPA), which includes AI, RPA and other technologies to run without human intervention. “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. The company implemented a cognitive automation application based on established global standards to automate categorization at the local level. The incoming data from retailers and vendors, which consisted of multiple formats such as text and images, are now processed using cognitive automation capabilities.

“One of the biggest challenges for organizations that have embarked on automation initiatives and want to expand their automation and digitalization footprint is knowing what their processes are,” Kohli said. “The biggest challenge is data, access to data and figuring out where to get started,” Samuel said. All cloud platform providers have made many of the applications for weaving together machine learning, big data and AI easily accessible. One example is to blend RPA and cognitive abilities for chatbots that make a customer feel like he or she is instant-messaging with a human customer service representative. Unlike traditional unattended RPA, cognitive RPA is adept at handling exceptions without human intervention. For example, most RPA solutions cannot cater for issues such as a date presented in the wrong format, missing information in a form, or slow response times on the network or Internet.

ML algorithms can analyze financial transactions in real time to identify suspicious patterns or anomalies indicative of fraudulent activity. The CoE fosters a culture of continuous improvement by analyzing automation outcomes, identifying opportunities for enhancement, and implementing refinements to maximize efficiency and effectiveness. Assemble a team with diverse skill sets, including domain expertise, technical proficiency, project management, and change management capabilities.

This team will identify automation opportunities, develop solutions, and manage deployment. A key aspect is establishing an Automation Center of Excellence (CoE), a centralized hub for managing automation initiatives across an organization. These innovations are transforming industries by making automated systems more intelligent and adaptable. These systems define, deploy, monitor, and maintain the complexity of decision logic used by operational systems within an organization. They analyze vast data, consider multiple variables, and generate responses or actions based on learned patterns. For instance, bespoke AI agents could automate setting up meetings, collecting data for reports, and performing other routine tasks, similar to verbal commands to a virtual assistant like Alexa.

Or, dynamic interactive voice response (IVR) can be used to improve the IVR experience. It adjusts the phone tree for repeat callers in a way that anticipates where they will need to go, helping them avoid the usual maze of options. AI-based automations can watch for the triggers that suggest it’s time to send an email, then compose and send the correspondence. Businesses that adopt cognitive automation will be able to stay ahead of the competition and improve their bottom line. Let’s take a look at how cognitive automation has helped businesses in the past and present. RPA uses technologies like screen scraping, workflow automation whereas Cognitive automation relies on technologies like OCR, ML and NLP.

While technologies have shown strong gains in terms of productivity and efficiency, “CIO was to look way beyond this,” said Tom Taulli author of The Robotic Process Automation Handbook. Cognitive automation will enable them to get more time savings and cost efficiencies from automation. “Ultimately, cognitive automation will morph into more automated decisioning as the technology is proven and tested,” Knisley said. Additionally, modern enterprise technology like chatbots built with cognitive automation can act as a first line of defense for IT and perform basic troubleshooting when end users run into a problem. This integration leads to a transformative solution that streamlines processes and simplifies workflows to ultimately improve the customer experience. Future AI models and algorithms are expected to have greater capabilities in understanding and reasoning across various data modalities, handling complex tasks with higher autonomy and adaptability.

He observed that traditional automation has a limited scope of the types of tasks that it can automate. For example, they might only enable processing of one type of document — i.e., an invoice or a claim — or struggle with noisy and inconsistent data from IT applications and system logs. As the digital agenda becomes more democratized in companies and cognitive automation more systemically applied, the relationship and integration of IT and the business functions will become much more complex. Cognitive automation promises to enhance other forms of automation tooling, including RPA and low-code platforms, by infusing AI into business processes. These enhancements have the potential to open new automation use cases and enhance the performance of existing automations. Most businesses are only scratching the surface of cognitive automation and are yet to uncover their full potential.

Dealing with unstructured data and inputs, fixing and validating data as necessary for context or virtual assistants to help with process development all require more cognitive ability from automation systems. Companies want systems to automatically perform reviews on items like contracts to identify favorable terms, consistency in word choice and set up templates quickly to avoid unnecessary exceptions. Now, with cognitive automation, businesses can take this a step further by automating more complex tasks that require human judgment.

An example would be robotizing the daily task of a purchasing agent who obtains pricing information from a supplier’s website. Applications are bound to face occasional outages and performance issues, making the job of IT Ops all the more critical. Here is where AIOps simplifies the resolution of issues, even proactively, before it leads to a loss in revenue or customers. We won’t go much deeper into the technicalities of Machine Learning here but if you are new to the subject and want to dive into the matter, have a look at our beginner’s guide to how machines learn. Check out the SS&C | Blue Prism® Robotic Operating Model 2 (ROM™2) for a step-by-step guide through your automation journey.

Cognitive neuromorphic computing, meanwhile, is a method of computer engineering in which elements of a computer are modeled after systems in the human brain and nervous system. Cognitive automation is an umbrella term for software solutions that leverage cognitive technologies to emulate human intelligence to perform specific tasks. By using cognitive automation to improve customer service, businesses can increase customer satisfaction and loyalty. This technology can handle semi-structured and unstructured data inputs and has the ability to “learn” to improve itself. It can also figure out complex situations and make predictions, which is something not possible with RPA.

After their successful implementation, companies can expand their data extraction capabilities with AI-based tools. Both cognitive automation and intelligent process automation fall within the category of RPA augmented with certain intelligent capabilities, where cognitive automation has come to define a sub-set of AI implementation in the RPA field. As confusing as it gets, cognitive automation may or may not be a part of RPA, as it may find other applications within digital enterprise solutions. RPA is referred to as automation software that can be integrated with existing digital systems to take on mundane work that requires monotonous data gathering, transferring, and reformatting. For example, businesses can use machine learning to automatically identify patterns in data. According to IDC, spending on cognitive and AI systems will reach $77.6 billion in 2022, more than three times the $24.0B forecast for 2018.

Processes that follow a simple flow and set of rules are most effective for yielding immediately effective results with nonintelligent bots. For example, employees who spend hours every day moving files or copying and pasting data from one source to another will find significant value from task automation. To overcome this challenge, organizations must put robust data validation and cleansing processes in place. Automated tools designed to provide real-time data monitoring and detecting anomalies are useful in identifying and addressing issues quickly and accurately.

A cognitive automation solution may just be what it takes to revitalize resources and take operational performance to the next level. Besides the application at hand, we found that two important dimensions lay in (1) the budget and (2) the required Machine Learning capabilities. This article will explain to you in detail which cognitive automation solutions are available for your company and hopefully guide you to the most suitable one according to your needs.

CPA surpasses traditional automation approaches like robotic process automation (RPA) and takes us into a workspace where the ordinary transforms into the extraordinary. However, once we look past rote tasks, enterprise intelligent automation become more complex. Certain tasks are currently best suited for humans, such as those that require reading or understanding text, making complex decisions, or aspects of recognition or pattern matching. In addition, interactive tasks that require collaboration with other humans and rely on communication skills and empathy are difficult to automate with unintelligent tools. KYC compliance requires organizations to inspect vast amounts of documents that verify customers’ identities and check the legitimacy of their financial operations.

These are integrated by the IBM Integration Layer (Golden Bridge) which acts as the ‘glue’ between the two. Levity is a tool that allows you to train AI models on images, documents, and text data. You can rebuild manual workflows and connect everything to your existing systems without writing a single line of code.‍If you liked this blog post, you’ll love Levity. The next step is, therefore, to determine the ideal cognitive automation approach and thoroughly evaluate the chosen solution.

Another prominent trend shaping the future of cognitive automation is the emphasis on human-AI collaboration. The field of cognitive automation is rapidly evolving, and several key trends and advancements are expected to redefine how AI technologies are utilized and integrated into various industries. These services convert spoken language into text and vice versa, enabling applications to process spoken commands, transcribe audio recordings, and generate natural-sounding speech output. Microsoft Cognitive Services is a suite of cloud-based APIs and SDKs that developers can use to incorporate cognitive capabilities into their applications. Organizations can optimize inventory levels, reduce stockouts, and improve supply chain efficiency by automating demand forecasting.