How Cognitive Automation Tools Improve Customer Service Decision-Making

Consider you’re a customer looking for assistance with a product issue on a company’s website. Instead of waiting for a human agent, you’re greeted by a friendly virtual assistant. They’re phrased informally or with specific industry jargon, making you feel understood and supported.

In this domain, cognitive automation is benefiting from improvements in AI for ITSM and in using natural language processing to automate trouble ticket resolution. 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. Cognitive automation describes diverse ways of combining artificial intelligence (AI) and process automation capabilities to improve business outcomes. 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.

“RPA is a great way to start automating processes and cognitive automation is a continuum of that,” said Manoj Karanth, vice president and global head of data science and engineering at Mindtree, a business consultancy. 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. 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.

• TCS’ Cognitive Automation Platform (see Figure 1) helps BFSI organizations expand their enterprise-level automation capabilities by seamlessly integrating legacy systems, modern technologies, and traditional automation solutions.
• It’s also important to plan for the new types of failure modes of cognitive analytics applications.
• Moreover, clinics deal with vast amounts of unstructured data coming from diagnostic tools, reports, knowledge bases, the internet of medical things, and other sources.
• However, simply automating rote tasks is not sufficient to deal with the continuous changes those enterprises face.
• It then uses these senses to make predictions and intelligent choices, thus allowing for a more resilient, adaptable system.

Let’s rewind and think about when companies across the globe were drowning in large amounts of paperwork. 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. Yet these approaches are limited by the sheer volume of data that must be aggregated, sifted through, and understood well enough to act upon.

A Four-Part Framework for Explaining The Power of Intelligent Automation

An example would be robotizing the daily task of a purchasing agent who obtains pricing information from a supplier’s website. “A human traditionally had to make the decision or execute the request, but now the software is mimicking the human decision-making activity,” Knisley said. “Cognitive automation, however, unlocks many of these constraints by being able to more fully automate and integrate across an entire value chain, and in doing so broaden the value realization that can be achieved,” Matcher said.

After their successful implementation, companies can expand their data extraction capabilities with AI-based tools. In any organization, documentation can be an overwhelming and time-consuming process. This problem statement keeps evolving as companies scale and expand their operations.

Cognitive automation may also play a role in automatically inventorying complex business processes. 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. Karev said it’s important to develop a clear ownership strategy with various stakeholders agreeing on the project goals and tactics. For example, if there is a new business opportunity on the table, both the marketing and operations teams should align on its scope.

Integrating cognitive automation into operational workflows can create a pivotal shift in augmenting operational efficiency, mitigating risks and fostering unparalleled customer-centricity. It has become important for industry leaders to embrace and integrate these technologies to stay competitive in an ever-evolving landscape. The initial investment for a digital transformation setup can be expensive for certain small-sized companies, making it difficult to incorporate. Before integrating cognitive automation, knowing if it is essential to your organization’s needs is crucial. As cognitive automation learns from the data and improves its performance over time, this becomes the go-to option for companies with ever-changing requirements. Most businesses are only scratching the surface of cognitive automation and are yet to uncover their full potential.

These chatbots are equipped with natural language processing (NLP) capabilities, allowing them to interact with customers, understand their queries, and provide solutions. 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. 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. Cognitive automation is an umbrella term for software solutions that leverage cognitive technologies to emulate human intelligence to perform specific tasks. While powerful, cognitive automation, like most artificial intelligence, has limitations and challenges.

Marketplace supported cognitive capabilities

In other words, the automation of business processes provided by them is mainly limited to finishing tasks within a rigid rule set. That’s why some people refer to RPA as “click bots”, although most applications nowadays go far beyond that. The biggest challenge is that cognitive automation requires customization and integration work specific to each enterprise. 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.

Hence, the ability to swiftly extract, categorize and analyze data from a voluminous dataset with the same or even a smaller team is a game-changer for many. Small-sized companies with budget constraints can consider alternatives like including collaborative document-sharing tools with cloud access, which fosters teamwork and can be cost-effective. The past few decades of enterprise automation have seen great efficiency automating repetitive functions that require integration or interaction across a range of systems.

With disconnected processes and customer data in multiple systems, resolving a single customer service issue could mean accessing dozens of different systems and sources of data. To bridge the disconnect, intelligent automation ties together disparate systems on premises and/or in cloud, provides automatic handling of customer data requirements, ensures compliance and reduces errors. For enterprises to achieve increasing levels of operational efficiency at higher levels of scale, organizations have to rely on automation. Organizations adding enterprise intelligent automation are putting the power of cognitive technology to work addressing the more complicated challenges in the corporate environment. Traditional RPA is mainly limited to automating processes (which may or may not involve structured data) that need swift, repetitive actions without much contextual analysis or dealing with contingencies.

Driving Decision Quality and Fidelity by removing Cognitive Biases

However, cognitive automation extends the functional boundaries of what is automated well beyond what is feasible through RPA alone. By augmenting RPA with cognitive technologies, the software can take into account a multitude of risk factors and intelligently assess them. This implies a significant decrease in false positives and an overall enhanced reliability of autonomous transaction monitoring. You can foun additiona information about ai customer service and artificial intelligence and NLP. ML-based cognitive automation tools make decisions based on the historical outcomes of previous alerts, current account activity, and external sources of information, such as customers’ social media. 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.

This is being accomplished through artificial intelligence, which seeks to simulate the cognitive functions of the human brain on an unprecedented scale. 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. As the predictive power of artificial intelligence is on the rise, it gives companies the methods and algorithms necessary to digest huge data sets and present the user with insights that are relevant to specific inquiries, circumstances, or goals. Through cognitive automation, it is possible to automate most of the essential routine steps involved in claims processing. 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.

But as those upward trends of scale, complexity, and pace continue to accelerate, it demands faster and smarter decision-making. Learn how to optimize your employee onboarding process through implementing AI automation, saving costs and hours of productive time. It’s also important to plan for the new types of failure modes of cognitive analytics applications. These technologies are coming together to understand how people, processes and content interact together and in order to completely reengineer how they work together. “As automation becomes even more intelligent and sophisticated, the pace and complexity of automation deployments will accelerate,” predicted Prince Kohli, CTO at Automation Anywhere, a leading RPA vendor. 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.

While chatbots are gaining popularity, their impact is limited by how deeply integrated they are into your company’s systems. For example, if they are not integrated into the legacy billing system, a customer will not be able to change her billing period through the chatbot. Cognitive automation allows building chatbots that can make changes in other systems with ease. Another important use case is attended automation bots that have the intelligence to guide agents in real time. From your business workflows to your IT operations, we got you covered with AI-powered automation.

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%. 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. In addition to being a large and successful hotel chain, Wyndham has begun to invest in providing exactly the customer service needed, when and where customers want it. Again, it starts with cloud technology, uniting data across platforms and 20 different brands, reducing the need for customers to repeat information already stored elsewhere in the system.

The UIPath Robot can take the role of an automated assistant running efficiently by your side, under supervision or it can quietly and autonomously process all the high-volume work that does not require constant human intervention. 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.

Automation of various tasks helps businesses to save cost, reduce manual labor, optimize resource allocation, and minimize operational expenses. This cost-effective approach contributes to improved profitability and resource management. While many companies already use rule-based RPA tools for AML transaction monitoring, it’s typically limited to flagging only known scenarios. Such systems require continuous fine-tuning and updates and fall short of connecting the dots between any previously unknown combination of factors. For example, one of the essentials of claims processing is first notice of loss (FNOL).

We can now automate repetitive tasks requiring manual labor using artificial intelligence and machine learning. It’s a step beyond basic automation, typically following predefined rules or instructions. When determining what tasks to automate, enterprises should start by looking at whether the process workflows, tasks and processes can be improved or even eliminated prior to automation. There are some obvious things to automate within an enterprise that provide short-term ROI — repetitive, boring, low-value busywork, like reporting tasks or data management or cleanup, that can easily be passed on to a robot for process automation.

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. In contrast, Modi sees intelligent automation as the automation of more rote tasks and processes by combining RPA and AI. 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.

These systems require proper setup of the right data sets, training and consistent monitoring of the performance over time to adjust as needed. One organization he has been working with predicted nearly 35% of its workforce will retire in the next five years. They are looking at cognitive automation to help address the brain drain that they are experiencing. “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. These areas include data and systems architecture, infrastructure accessibility and operational connectivity to the business.

Cognitive automation tools are relatively new, but experts say they offer a substantial upgrade over earlier generations of automation software. Now, IT leaders are looking to expand the range of cognitive automation use cases they support in the enterprise. This integration leads to a transformative solution that streamlines processes and simplifies workflows to ultimately improve the customer experience. “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.

This task involves assessing the creditworthiness of customers by carefully inspecting tax reports, business plans, and mortgage applications. Given that the majority of today’s banks have an online application process, cognitive bots can source relevant data from submitted documents and make an informed prediction, which will be further passed to a human agent to verify. A self-driving enterprise is one where the cognitive automation platform acts as a digital brain that sits atop and interconnects all transactional systems within that organization.

This “brain” is able to comprehend all of the company’s operations and replicate them at scale. 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. 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.

Companies looking for automation functionality will likely consider both Robotic Process Automation (RPA) and cognitive automation systems. While both traditional RPA and cognitive automation provide smart and efficient process automation tools, there are many differences in scope, methodology, processing capabilities, and overall benefits for the business. Adopting a digital operating model enables companies to scale and grow in an increasingly competitive environment while exceeding market expectations. For example, a cognitive automation application might use a machine learning algorithm to determine an interest rate as part of a loan request. These skills, tools and processes can make more types of unstructured data available in structured format, which enables more complex decision-making, reasoning and predictive analytics.

We provide an overview of the major BPA approaches such as workflow management, robotic process automation, and Machine Learning-facilitated BPA while emphasizing their complementary relationships. Furthermore, we show how the phenomenon of cognitive automation can be instantiated by Machine Learning-facilitated BPA systems that operate along the spectrum of lightweight and heavyweight IT implementations in larger IS ecosystems. Based on this, we describe the relevance and opportunities of cognitive automation in Information Systems research. He suggested CIOs start to think about how to break up their service delivery experience into the appropriate pieces to automate using existing technology. The automation footprint could scale up with improvements in cognitive automation components. As CIOs embrace more automation tools like RPA, they should also consider utilizing cognitive automation for higher-level tasks to further improve business processes.

Newer technologies live side-by-side with the end users or intelligent agents observing data streams — seeking opportunities for automation and surfacing those to domain experts. RPA is best for straight through processing activities that follow a more deterministic logic. In contrast, cognitive automation excels at automating more complex and less rules-based tasks.

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 cognitive automation tools into an ERP when processing invoices. “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.

“Cognitive automation multiplies the value delivered by traditional automation, with little additional, and perhaps in some cases, a lower, cost,” said Jerry Cuomo, IBM fellow, vice president and CTO at IBM Automation. “Cognitive automation can be the differentiator and value-add CIOs need to meet and even exceed heightened expectations in today’s enterprise environment,” said Ali Siddiqui, chief product officer at BMC.

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. “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. Employee time would be better spent caring for people rather than tending to processes and paperwork. UK telecom company Vodafone was dealing with frustrated customers and extended call times in their service center, where there is a high volume of expertise needed but also a high rate of employee churn.