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. Additionally, while robotic process automation provides effective solutions for simpler automations, it is limited on its own to meet the needs of today’s fast-paced world. “RPA handles task automations such as copy and paste, moving and opening documents, and transferring data, very effectively. It also allows organizations to set up a good foundation for automation.
Cognitive computing systems are typically used to accomplish tasks that require parsing large amounts of data. For example, in computer science, cognitive computing aids in big data analytics, identifying trends and patterns, understanding human language and interacting with customers. Systems used in the cognitive sciences combine data from various sources while weighing context and conflicting evidence to suggest the best possible answers. To achieve this, cognitive systems include self-learning technologies that use data mining, pattern recognition and NLP to mimic human intelligence. Automation technology, like RPA, can also access information through legacy systems, integrating well with other applications through front-end integrations. This allows the automation platform to behave similarly to a human worker, performing routine tasks, such as logging in and copying and pasting from one system to another.
The majority of market participants are developing cognitive services that application developers or end users can access and deploy on their servers and systems. Use cases for cognitive automation have been observed in a variety of industries, including finance, retail, and healthcare. In contrast, Modi sees intelligent automation as the automation of more rote tasks and combining RPA and AI. These are complemented by other technologies such as analytics, process orchestration, BPM, and process mining to support intelligent automation initiatives.
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Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised enterprises on their technology decisions at McKinsey & Company and Altman Solon for more than a decade. He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem’s work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.
“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. AI is still at its infancy, it learns by example, most technologies like NLP, OCR or ML has not yet been perfected or matured, this leaves room for error and require close attention. It gives businesses a competitive advantage by enhancing their operations in numerous areas.
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While RPA automations can dramatically speed up a business process previously handled by humans, bots can break when application interfaces or process workflows change. 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. 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. 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.
“The governance of cognitive automation systems is different, and CIOs need to consequently pay closer attention to how workflows are adapted,” said Jean-François Gagné, co-founder and CEO of Element AI. 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. RPA uses technologies like screen scraping, workflow automation whereas Cognitive automation relies on technologies like OCR, ML and NLP.
Seetharamiah added that the real choice is between deterministic and cognitive. “Go for cognitive automation, if a given task needs to make decisions that require learning and data analytics, for example, the next best action in the case of the customer service agent,” he told Spiceworks. According to experts, cognitive automation falls under the second category of tasks where systems can learn and make decisions independently or with support from humans. In 2020, Gartner reportedOpens a new window that 80% of executives expect to increase spending on digital business initiatives in 2022. In fact, spending on cognitive and AI systems will reach $77.6 billion in 2022, according to a report by IDCOpens a new window . Findings from both reports testify that the pace of cognitive automation and RPA is accelerating business processes more than ever before.
Organizations are understanding the tremendous value of automation and expect to see significant growth and investment in these initiatives moving forward. As the lines between real and fake blur, Americans increasingly chase the idea of authenticity. The first step may be to consider self-knowledge, truthfulness, and other building blocks on the road to personal growth. Our “automatic mind” indeed can determine not only the way we pay attention and process environmental inputs and the world around us, but also how we perceive and interact with other people. This test is called the Stroop task and it illustrates how the automaticity of the reading can interfere with our performance.
“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. It’s also important to plan for the new types of failure modes of cognitive analytics applications. “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 shift of models will improve the adoption of new types of automation across rapidly evolving business functions.
Check out our RPA guide or our guide on RPA vendor comparison for more info. You can also learn about other innovations in RPA such as no code RPA from our future of RPA article. Make automated decisions about claims based on policy and claim data and notify payment systems. While these are efforts by major RPA vendors to augment their bots, RPA companies can not build custom AI solutions for each process. Therefore, companies rely on AI focused companies like IBM and niche tech consultancy firms to build more sophisticated automation services.
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