Cognitive Automation: Augmenting Bots with Intelligence

Our previously modeled adoption scenarios suggested that 50 percent of time spent on 2016 work activities would be automated sometime between 2035 and 2070, with a midpoint scenario around 2053. Generative AI tools can draw on existing documents https://chat.openai.com/ and data sets to substantially streamline content generation. These tools can create personalized marketing and sales content tailored to specific client profiles and histories as well as a multitude of alternatives for A/B testing.

To help customers to achieve value quickly, Automation Anywhere is also delivering a suite of AI-powered solutions to help accelerate business outcomes across all key business functions. «AI Agent Studio has truly streamlined my workflow, boosted my productivity, and has been a game-changer,» said Khaled Mostafa, Intelligent Automation Services Delivery Manager, Magnoos Information Systems. Cognitive automation tools are relatively new, but experts say they offer a substantial upgrade over earlier generations of automation software.

• But one of the useful things you can do with these agents, whether it’s ChatGPT-4, Bing Chat, or any other, is to ask them to explain the paper in terms relevant to you and your industry.
• 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.
• 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.

By automating cognitive tasks, organizations can reduce labor costs and optimize resource allocation. Automated systems can handle tasks more efficiently, requiring fewer human resources and allowing employees to focus on higher-value activities. Task mining and process mining analyze your current business processes to determine which are the best automation candidates. They can also identify bottlenecks and inefficiencies in your processes so you can make improvements before implementing further technology. 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.

Straight through processing vs. exceptions

One of the useful things in prompting these agents is learning how to sharpen your prompt directing. If you’re a brand marketing expert trying to figure out a branding question for a portfolio company, one of the things you can do is go to Bing Chat, GPT-4, Pi, et cetera. QuantumBlack, McKinsey’s AI arm, helps companies transform using the power of technology, technical expertise, and industry experts.

«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. 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. Another important use case is attended automation bots that have the intelligence to guide agents in real time.

The “earliest” scenario flexes all parameters to the extremes of plausible assumptions, resulting in faster automation development and adoption, and the “latest” scenario flexes all parameters in the opposite direction. We also surveyed experts in the automation of each of these capabilities to estimate automation technologies’ current performance level against each of these capabilities, as well as how the technology’s performance might advance over time. Specifically, this year, we updated our assessments of technology’s performance in cognitive, language, and social and emotional capabilities based on a survey of generative AI experts. These examples illustrate how technology can augment work through the automation of individual activities that workers would have otherwise had to do themselves.

Although the COVID-19 pandemic was a significant factor, long-term structural challenges—including declining birth rates and aging populations—are ongoing obstacles to growth. Interestingly, the range of times between the early and late scenarios has compressed compared with the expert assessments in 2017, reflecting a greater confidence that higher levels of technological capabilities will arrive by certain time periods (Exhibit 7). “With Cognigy’s AI Agents, we help our valued consumers and at the same time are building direct relationships and enhancing our brand experience. Cognigy.AI is flexible enough to cover various use cases while providing a common framework for global scaling and governance,” said Christian Hohmann, Head of New Technologies (AI) & IT-Management at Henkel.

«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. RPA primarily deals with structured data and predefined rules, whereas cognitive automation can handle unstructured data, making sense of it through natural language processing and machine learning. Cognitive automation works by combining the power of artificial intelligence (AI) and automation to enable systems to perform tasks that typically require human intelligence.

It’s simply not economically feasible to maintain a large team at all times just in case such situations occur. This is why it’s common to employ intermediaries to deal with complex claim flow processes. These technologies allow cognitive automation tools to find patterns, discover relationships between a myriad of different data points, make predictions, and enable self-correction.

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. RPA tools are ideal for carrying out repetitive tasks inside of a process that require the use of a UI while BPM platforms are designed to manage and orchestrate complex end-to-end business processes. However, as the RPA category matured, vendors started bundling BPM services to RPA tools and vice versa, blurring the line between the two sets of tools.

AI Agents take automation to the next level with the ability to learn from enterprise data, make informed decisions, and take action responsibly across any enterprise system, speeding processes by up to 90 percent. AI Agent Studio features low-code tools, making it easy for developers of all skill levels to quickly create specialized AI Agents to help with their specific use cases – no data scientist required. These AI Agents combine AI and action to tackle more complex cognitive work, like identifying and automatically replacing a product in the case of a stock shortage. They are adaptive, capable of learning from complex enterprise data, and able to take swift action for quick resolution and higher ROI.

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For example, in an accounts payable workflow, cognitive automation could transform PDF documents into machine-readable structure data that would then be handed to RPA to perform rules-based data input into the ERP. “We’re aiming to enable the creation of more sophisticated customer service solutions and the acceleration of AI-first technologies that deliver return on investment,” Heltewig said. Philipp Heltewig, who was CIO at marketing firm Sitecore before it was sold to private equity group EQT in 2016, joined forces with Sascha Poggemann and Benjamin Mayr eight years ago to found Cognigy, a customer service automation startup.

“We are on a mission to redefine the standards of customer service by leveraging the transformative power of Conversational AI and GenAI. Our collaboration with Cognigy is a testament to our commitment to pioneering solutions that are at the forefront of technology and meet our members’ needs. The integration of Cognigy’s technology into our service framework is a game-changer, setting a new benchmark for excellence in the industry,” said Jack Roberts, Director of GMS Technology & Applications at TechStyleOS. We’ve also incorporated retrieval-augmented generation (RAG) to mitigate hallucinations and enhance the accuracy and reliability of generative AI models using enterprise data like knowledge articles and product catalogs. And, we’re also including security, data, privacy, and responsible use guard rails in AI Agents with real-time monitoring, traceability, auditability, and PII masking. AlphaChat is a cognitive bot tool allowing companies to build Conversational AI bots for their website.

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. Previous generations of automation technology were particularly effective at automating data management tasks related to collecting and processing data. Generative AI’s natural-language capabilities increase the automation potential of these types of activities somewhat. But its impact on more physical work activities shifted much less, which isn’t surprising because its capabilities are fundamentally engineered to do cognitive tasks. The potential of technological capabilities in a lab does not necessarily mean they can be immediately integrated into a solution that automates a specific work activity—developing such solutions takes time.

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. Cognitive automation describes diverse ways of combining artificial intelligence Chat GPT (AI) and process automation capabilities to improve business outcomes. Middle managers will need to shift their focus on the more human elements of their job to sustain motivation within the workforce. Automation will expose skills gaps within the workforce and employees will need to adapt to their continuously changing work environments.

For lower-wage occupations, making a case for work automation is more difficult because the potential benefits of automation compete against a lower cost of human labor. Additionally, some of the tasks performed in lower-wage occupations are technically difficult to automate—for example, manipulating fabric or picking delicate fruits. Some labor economists have observed a “hollowing out of the middle,” and our previous models have suggested that work automation would likely have the biggest midterm impact on lower-middle-income quintiles. For example, the life sciences and chemical industries have begun using generative AI foundation models in their R&D for what is known as generative design. Foundation models can generate candidate molecules, accelerating the process of developing new drugs and materials.

Considering other RPA benefits like error reduction and increased customer satisfaction, RPA tools offer a compelling amount of ROI for your business. 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.

IBM Consulting’s extreme automation consulting services enable enterprises to move beyond simple task automations to handling high-profile, customer-facing and revenue-producing processes with built-in adoption and scale. This integration leads to a transformative solution that streamlines processes and simplifies workflows to ultimately improve the customer experience. Since 1989, Automated Control Logic, Inc. has been the source clients have relied on for quality and expertise with building automation controls, installation, and service.

This research is the latest in our efforts to assess the impact of this new era of AI. It suggests that generative AI is poised to transform roles and boost performance across functions such as sales and marketing, customer operations, and software development. In the process, it could unlock trillions of dollars in value across sectors from banking to life sciences. Available today is the Service Operations Solution Accelerator, with capabilities to automate order management, returns processing, ticket management, and service Q&A processes for enhanced worker productivity and improved customer experience. Cognitive automation creates new efficiencies and improves the quality of business at the same time.

Possible indications for a given drug are based on a patient group’s clinical history and medical records, and they are then prioritized based on their similarities to established and evidence-backed indications. AI has permeated our lives incrementally, through everything from the tech powering our smartphones to autonomous-driving features on cars to the tools retailers use to surprise and delight consumers. Clear milestones, such as when AlphaGo, an AI-based program developed by DeepMind, defeated a world champion Go player in 2016, were celebrated but then quickly faded from the public’s consciousness. With Cognigy’s AI Agents, we stay ahead and provide real value to customers and car dealerships,” said Peter-Pascal Meik, Manager Innovation & Projects at Toyota. This means that you can ask a question and the bot understands what you are saying.

Cognitive automation accelerates the decision-making process and makes automation applicable to wider enterprise processes, beyond simply planning and negotiating. Humans can rely on unbiased and objective data and dedicate attention to building connections, which is more creative and strategic work. Also, tribal knowledge built on tenured experience isn’t always helpful as organizations experience numerous unforeseen shocks, including pandemics, market fluctuations, and other “black swan” events. Cognitive automation is about transforming, not eliminating, the relationship between technology and people. Cognitive automation has proven to be effective in addressing those key challenges by supporting companies in optimizing their day-to-day activities as well as their entire business. This step involves combining information with past trends and rules to decide on a course of action.

Across the 63 use cases we analyzed, generative AI has the potential to generate $2.6 trillion to $4.4 trillion in value across industries. Its precise impact will depend on a variety of factors, such as the mix and importance of different functions, as well as the scale of an industry’s revenue (Exhibit 4). Software engineering is a significant function in most companies, and it continues to grow as all large companies, not just tech titans, embed software in a wide array of products and services. For example, much of the value of new vehicles comes from digital features such as adaptive cruise control, parking assistance, and IoT connectivity.

Sometimes it’s OK to say, ‘We’re going to be a deliberate follower and let other people do the experimentation.’ But sometimes, being a follower means you lose. So you have to think about where you need to lead, where you need to match, and where you can follow. Sometimes it’s OK to say, “We’re going to be a deliberate follower and let other people do the experimentation.” But sometimes, being a follower means you lose. And in those cases where you determine you should be leading, then you obviously have to pick up your game quite a lot. And what makes it even bolder than the Industrial Revolution or the printing press is obviously the speed at which it will be moving.

What is cognitive automation and why does it matter?

By augmenting human cognitive capabilities with AI-powered analysis and recommendations, cognitive automation drives more informed and data-driven decisions. You can foun additiona information about ai customer service and artificial intelligence and NLP. Its systems can analyze large datasets, extract relevant insights and provide decision support. It mimics human behavior and intelligence to facilitate decision-making, combining the cognitive ‘thinking’ aspects of artificial intelligence (AI) with the ‘doing’ task functions of robotic process automation (RPA). While there are clear benefits of cognitive automation, it is not easy to do right, Taulli said.

“The principles of competition enforcement apply whether an innovation is powered by steam, by transistors or by reorganizing human thought through machine learning,” Assistant Attorney General Jonathan Kanter said in a speech last month. RPA is best deployed in a stable environment with standardized and structured data. Cognitive automation is most valuable when applied in a complex IT environment with non-standardized and unstructured data. «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. Quosphere is a global advanced analytics solutions provider, helping clients gain a competitive edge by leveraging innovative technologies.

Over the years, machines have given human workers various “superpowers”; for instance, industrial-age machines enabled workers to accomplish physical tasks beyond the capabilities of their own bodies. More recently, computers have enabled knowledge workers to perform calculations that would have taken years to do manually. Researchers start by mapping the patient cohort’s clinical events and medical histories—including potential diagnoses, prescribed medications, and performed procedures—from real-world data. Using foundation models, researchers can quantify clinical events, establish relationships, and measure the similarity between the patient cohort and evidence-backed indications. The result is a short list of indications that have a better probability of success in clinical trials because they can be more accurately matched to appropriate patient groups.

We’re confident that once workers see the power of AI-powered automations, they’ll want to expand and scale to automate more and more use cases. We also announced “bring your own LLM” capabilities to connect to, govern, and control third-party LLMs, and, coming soon, connectors to custom models built on AWS. These enhanced AI capabilities allow business users to automate more processes across any system and support developers of all skill levels as they build custom AI tools grounded in your business data. Facilitated by AI technology, the phenomenon of cognitive automation extends the scope of deterministic business process automation (BPA) through the probabilistic automation of knowledge and service work.

Technological and digital advancement are the primary drivers in the modern enterprise, which must confront the hurdles of ever-increasing scale, complexity, and pace in practically every industry. Dive into a huge catalog of learning opportunities to help you make an even bigger impact in your business. Discover the future of industrial operations with over 10,000 problem-solvers, makers and leaders. As you strive to achieve more, to simplify complex production challenges and to be more resilient, agile and sustainable, Automation Fair will empower you to make your mark. Join all Cisco U. Theater sessions live and direct from Cisco Live or replay them, access learning promos, and more.

But the embrace of generative AI shows that the technology trend is too powerful for even Apple to ignore. «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. RPA usage has primarily focused on the manual activities of processes and was largely used to drive a degree of process efficiency and reduction of routine manual processing. CIOs also need to address different considerations when working with each of the technologies.

Our analysis finds that generative AI could have a significant impact on the pharmaceutical and medical-product industries—from 2.6 to 4.5 percent of annual revenues across the pharmaceutical and medical-product industries, or $60 billion to $110 billion annually. This big potential reflects the resource-intensive process of discovering new drug compounds. Pharma companies typically spend approximately 20 percent of revenues on R&D,1Research and development in the pharmaceutical industry, Congressional Budget Office, April 2021. With this level of spending and timeline, improving the speed and quality of R&D can generate substantial value. For example, lead identification—a step in the drug discovery process in which researchers identify a molecule that would best address the target for a potential new drug—can take several months even with “traditional” deep learning techniques.

By comparison, the bulk of potential value in high tech comes from generative AI’s ability to increase the speed and efficiency of software development (Exhibit 5). In other cases, generative AI can drive value by working in partnership with workers, augmenting their work in ways that accelerate their productivity. Its ability to rapidly digest mountains of data and draw conclusions from it enables the technology to offer insights and options that can dramatically enhance knowledge work.

But when complex data is involved it can be very challenging and may ask for human intervention. Key distinctions between robotic process automation (RPA) vs. cognitive automation include how they complement human workers, the types of data they work with, the timeline for projects and how they are programmed. Generative AI can substantially increase labor productivity across the economy, but that will require investments to support workers as they shift work activities or change jobs. Generative AI could enable labor productivity growth of 0.1 to 0.6 percent annually through 2040, depending on the rate of technology adoption and redeployment of worker time into other activities. Combining generative AI with all other technologies, work automation could add 0.5 to 3.4 percentage points annually to productivity growth.

So when we’re doing human-feedback learning, we teach Pi to give an answer with EQ. Because it depends on the purpose of work you’re about to find at the end of your cognitive automation journey. Today, companies need access to numerous sources as making assumptions is based on zettabytes of raw data. The promise of automation seems to be focused on making processes touchless — devoid of human interaction and input.

Last, the tools can review code to identify defects and inefficiencies in computing. Generative AI could have a significant impact on the banking industry, generating value from increased productivity of 2.8 to 4.7 percent of the industry’s annual revenues, or an additional $200 billion to $340 billion. On top of that impact, the use of generative AI tools could also enhance customer satisfaction, improve decision making and employee experience, and decrease risks through better monitoring of fraud and risk. Our analysis of the potential use of generative AI in marketing doesn’t account for knock-on effects beyond the direct impacts on productivity. Generative AI–enabled synthesis could provide higher-quality data insights, leading to new ideas for marketing campaigns and better-targeted customer segments.

We’ve always worked to safeguard your sensitive data, ensure compliance, and enhance security with comprehensive governance, privacy, and security capabilities. We are beyond thrilled to share more of the benefits and capabilities of our suite of new AI-powered automation solutions announced today at Imagine Austin 2024. These innovations, built on the generative AI capabilities announced at Imagine 2023, will expand the limits of what’s possible with automation and accelerate the pace at which automation ideas turn into secure actions. Activechat is an automated intelligent bot platform for customer interaction automation through building smart AI chatbots that are bundled with a live chat tool and a conversational intelligence module.

In particular, our estimates of the primary value the technology could unlock do not include use cases for which the sole benefit would be its ability to use natural language. For example, natural-language capabilities would be the key driver of value in a customer service use case but not in a use case optimizing a logistics network, where value primarily arises from quantitative analysis. All of us are at the beginning of a journey to understand generative AI’s power, reach, and capabilities.

Traditional advanced-analytics and machine learning algorithms are highly effective at performing numerical and optimization tasks such as predictive modeling, and they continue to find new applications in a wide range of industries. However, as generative AI continues to develop and mature, it has the potential to open wholly new frontiers in creativity and innovation. It has already expanded the possibilities of what AI overall can achieve (see sidebar “How we estimated the value potential of generative AI use cases”). As companies rush to adapt and implement it, understanding the technology’s potential to deliver value to the economy and society at large will help shape critical decisions. We have used two complementary lenses to determine where generative AI, with its current capabilities, could deliver the biggest value and how big that value could be (Exhibit 1). Cognigy’s AI Agents deliver instant and personalized service at scale, which has been critical to our operations and is helping millions of our customers every year.

Banks have started to grasp the potential of generative AI in their front lines and in their software activities. Early adopters are harnessing solutions such as ChatGPT as well as industry-specific solutions, primarily for software and knowledge applications. Generative AI’s potential in R&D is perhaps less well recognized than its potential in other business functions. Still, our research indicates the technology could deliver cognitive automation company productivity with a value ranging from 10 to 15 percent of overall R&D costs. For one thing, mathematical models trained on publicly available data without sufficient safeguards against plagiarism, copyright violations, and branding recognition risks infringing on intellectual property rights. A virtual try-on application may produce biased representations of certain demographics because of limited or biased training data.

Let’s break down how cognitive automation bridges the gaps where other approaches to automation, most notably Robotic Process Automation (RPA) and integration tools (iPaaS) fall short. 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.

Many organizations have also successfully automated their KYC processes with RPA. KYC compliance requires organizations to inspect vast amounts of documents that verify customers’ identities and check the legitimacy of their financial operations. 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 breakthrough new feature is the ability to build custom AI Agents with the new AI Agent Studio.

Individuals focused on low-level work will be reallocated to implement and scale these solutions as well as other higher-level tasks. The deployment of generative AI and other technologies could help accelerate productivity growth, partially compensating for declining employment growth and enabling overall economic growth. In some cases, workers will stay in the same occupations, but their mix of activities will shift; in others, workers will need to shift occupations.

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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. Digital process automation (DPA) software, similar to low-code development and business process management tools, helps businesses to automate, manage and optimize their workflows and processes. Document Automation, which has seen tremendous customer response and 9x customer growth year on year, leverages generative AI enhancements for real-time processing of any document type, including unstructured documents and achieves more than 90 percent accuracy. Companies now can rapidly capture data in the flow of work from any document type with the new ability to extract data from complex tables, more than 30 supported languages, and expanded model options.

Generative AI could still be described as skill-biased technological change, but with a different, perhaps more granular, description of skills that are more likely to be replaced than complemented by the activities that machines can do. Labor economists have often noted that the deployment of automation technologies tends to have the most impact on workers with the lowest skill levels, as measured by educational attainment, or what is called skill biased. We find that generative AI has the opposite pattern—it is likely to have the most incremental impact through automating some of the activities of more-educated workers (Exhibit 12). Based on developments in generative AI, technology performance is now expected to match median human performance and reach top-quartile human performance earlier than previously estimated across a wide range of capabilities (Exhibit 6).

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. The hotel guest management technology company’s platform digitizes the hotel guest journey from post-booking through checkout. AccountsIQ, a Dublin-founded accounting technology company, has raised $65 million to build “the finance function of the future” for mid-sized companies. Cognigy GmbH, the developer of an artificial intelligence platform designed to make contact centers more efficient, today announced that it has closed a $100 million funding round.

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. «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. Basic cognitive services are often customized, rather than designed from scratch.