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Elevating Automation: The Era of Intelligent Automation
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. In contrast, cognitive automation or Intelligent Process Automation (IPA) can accommodate both structured and unstructured data to automate more complex processes. Deloitte explains how their team used bots with natural language processing capabilities to solve this issue. You can also check our article on intelligent automation in finance and accounting for more examples. Bolstered by cognitive services, intelligent automation empowers organizations to thoroughly analyze processes and capture valuable knowledge that might otherwise be lost.
- This has made them valuable tools for automating tasks that were previously difficult to automate, such as customer service and support, content creation, and language translation.
- While large language models and other AI technologies could significantly transform our economy and society, policymakers should take a balanced perspective that considers both the promises and perils of cognitive automation.
- You might even have noticed that some RPA software vendors — Automation Anywhere is one of them — are attempting to be more precise with their language.
- Automation will expose skills gaps within the workforce, and employees will need to adapt to their continuously changing work environments.
- For example, a cognitive automation application might use a machine learning algorithm to determine an interest rate as part of a loan request.
However, I believe that the long-term impact of cognitive automation on the labor market is difficult to predict. It is possible that these technologies could create new job opportunities that we can’t even imagine today. As David mentioned earlier, many of the jobs that we work in today didn’t exist decades ago. Therefore, it is important to approach the adoption of these technologies with caution and to consider the potential consequences for the workforce. It also holds a permanent memory of all the decisions made on the platform, along with the context and results of those decisions. The cognitive automation system uses this information to learn and optimize future recommendations.
Future of Decisions: Replace Gut Instinct With Artificial Intelligence
The rapid rise of large language models has stirred extensive debate on how cognitive assistants such as OpenAI’s ChatGPT and Anthropic’s Claude will affect labor markets. I, Anton Korinek, Rubenstein Fellow at Brookings, invited David Autor, Ford Professor in the MIT Department of Economics, to a conversation on large language models and cognitive automation. Large language models, like ChatGPT and Claude, are artificial intelligence tools that can recognize, summarize, translate, predict, and generate text and other content. They generate this content based on knowledge gained from large datasets containing billions of words. Their responses in the transcript below have been copied exactly as written and have not been edited for accuracy.
At this point, human experts still rule when it comes to opining on new developments, whereas today’s generation of large language models may have more to contribute in creative contexts where abstract models of the world are less important. We were fortunate to have David, one of the world’s top experts on the topic, lead the conversation. Most businesses are only scratching the surface of cognitive automation and are yet to uncover their full potential. A cognitive automation solution may just be what it takes to revitalize resources and take operational performance to the next level. In addition, cognitive automation tools can understand and classify different PDF documents. This allows us to automatically trigger different actions based on the type of document received.
VIDEO: Cognitive Automation Summit 2021 Pioneers of Cognitive Automation Panel
Even if it were possible, it may not be desirable for machines to perform all human work. As AI takes over more tasks, it will be important to ensure that human skills, values, and judgment remain involved in applications and decisions that have a significant impact on people and society. Creativity, cultural understanding, and wisdom are also core parts of the human experience, and we would not want to fully automate away activities that tap into these capabilities. An ideal outcome might be to use increasingly capable AI to liberate humans from dangerous, tedious, and undesirable work, while still relying on human skills, values, and judgment for applications critical to society. However, there are valid arguments on multiple sides regarding how AI might ideally integrate with and augment human labor.
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. Also referred to occasionally as “alive” robots, Xenobots possess a few peculiarities that set them apart from any other existing AI and robotics-based applications. For instance, xenobots are created using an amalgamation of robotics, AI and stem cell technology. The creators of the technology used stem cells from the African clawed frog (its scientific name is Xenopus Laevis) to create a self-healing, self-living robot that is minute in size—xenobots are less than a millimeter wide. Like natural animal and plant cells, the cells used to create xenobots also die after completing their life cycle.
Cognitive automation (also called smart or intelligent automation) is an emerging field that augments RPA tools with artificial intelligence (AI) capabilities like optical character recognition (OCR) or natural language processing (NLP). Vendors claim that 70-80% of corporate knowledge tasks can be automated with increased cognitive capabilities. To deal with unstructured data, cognitive bots need to be capable of machine learning and natural language processing. Intelligent automation simplifies processes, frees up resources and improves operational efficiencies, and it has a variety of applications. An insurance provider can use intelligent automation to calculate payments, make predictions used to calculate rates, and address compliance needs.
According to IDC, in 2017, the largest area of AI spending was cognitive applications. This includes applications that automate processes that automatically learn, discover, and make recommendations or predictions. Overall, cognitive software platforms will see investments of nearly $2.5 billion this year. Spending on cognitive-related IT and business services will be more than $3.5 billion and will enjoy a five-year CAGR of nearly 70%.
VIDEO: The Art and Science of Decisions
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. Cognitive automation is a summarizing term for the application of Machine Learning technologies to automation in order to take over tasks that would otherwise require manual labor to be accomplished. Data mining and NLP techniques are used to extract policy data and impacts of policy changes to make automated decisions regarding policy changes. You can check our article where we discuss the differences between RPA and intelligent / cognitive automation. 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.