What is Intelligent Automation?

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. Still, the enterprise requires humans to choose and apply automation techniques to specific tasks — for now. One area currently under development is the ability for machines to autonomously discover and optimize processes within the enterprise. Some automation tools have started to combine automation and cognitive technologies to figure out how processes are configured or actually operating. And they are automatically able to suggest and modify processes to improve overall flow, learn from itself to figure out better ways to handle process flow and conduct automatic orchestration of multiple bots to optimize processes.

Given its potential, companies are starting to embrace this new technology in their processes. According to a 2019 global business survey by Statista, around 39 percent of respondents confirmed that they have already integrated cognitive automation at a functional level in their businesses. Also, 32 percent of respondents said they will be implementing it in some form by the end of 2020. 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%.

Top 25 Use Cases / Examples of Intelligent Automation in 2024

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. By transforming work systems through cognitive automation, organizations are provided with vast strategic opportunities to gain business value. However, research lacks a unified conceptual lens on cognitive automation, which hinders scientific progress.

Automated processes can only function effectively as long as the decisions follow an “if/then” logic without needing any human judgment in between. However, this rigidity leads RPAs to fail to retrieve meaning cognitive process automation tools and process forward unstructured data. Employee onboarding is another example of a complex, multistep, manual process that requires a lot of HR bandwidth and can be streamlined with cognitive automation.

What are the determining factors when considering Cognitive Robotic Process Automation?

Therefore, companies rely on AI focused companies like IBM and niche tech consultancy firms to build more sophisticated automation services. You can check our article where we discuss the differences between RPA and intelligent / cognitive automation. The emerging trend we are highlighting here is the growing use of cognitive technologies in conjunction with RPA. But before describing that trend, let’s take a closer look at these software robots, or bots. The integration of these three components creates a transformative solution that streamlines processes and simplifies workflows to ultimately improve the customer experience.

Down the road, these kinds of improvements could lead to autonomous operations that combine process intelligence and tribal knowledge with AI to improve over time, said Nagarajan Chakravarthy, chief digital officer at IOpex, a business solutions provider. 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. However, there are times when information is incomplete, requires additional enhancement or combines with multiple sources to complete a particular task.

Banking – Processing trade finance transactions

RPA imitates manual effort through keystrokes, such as data entry, based on the rules it’s assigned. But combined with cognitive automation, RPA has the potential to automate entire end-to-end processes and aid in decision-making from both structured and unstructured data. Cognitive Automation is the conversion of manual business processes to automated processes by identifying network performance issues and their impact on a business, answering with cognitive input and finding optimal solutions. Addressing the challenges most often faced by network operators empowers predictive operations over reactive solutions. Over time, these pre-trained systems can form their own connections automatically to continuously learn and adapt to incoming data. For enterprises to achieve increasing levels of operational efficiency at higher levels of scale, organizations have to rely on automation.