What is Robotic Process Automation RPA?

This form of automation uses rule-based software to perform business process activities at a high-volume, freeing up human resources to prioritize more complex tasks. RPA enables CIOs and other decision makers to accelerate their digital transformation efforts and generate a higher return on investment (ROI) from their staff. RPA tools were initially used to perform repetitive tasks with greater precision and accuracy, which has helped organizations reduce back-office costs and increase productivity. While basic tasks can be automated using RPA, subsequent tasks require context, judgment and an ability to learn. Cognitive automation can use AI techniques in places where document processing, vision, natural language and sound are required, taking automation to the next level. Our member firms apply robotic process automation (RPA) and cognitive technologies to achieve enhanced business productivity, process accuracy, and customer service by augmenting or replicating human actions and judgment.

“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. 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. Driven by accelerating connectivity, new talent models, and cognitive tools, work is changing. As robotics, AI, the gig economy and crowds grow, jobs are being reinvented, creating the “augmented workforce.” We must reconsider how jobs are designed and work to adapt and learn for future growth. The Fourth Industrial Revolution is driven by the convergence of computing, data and AI.

Intelligent automation (IA) trends and future direction

R&CA technology is now poised to unlock a world of possibilities through the synergistic combination of its key components. Intelligent automation (IA) — an end-to-end intelligent automation solution that combines robotic process automation (RPA) and artificial intelligence (AI) — can provide many benefits that aid in the digital transformation of an organization. Robotic process automation (RPA), also known as software robotics, uses automation technologies to mimic back-office tasks of human workers, such as extracting data, filling in forms, moving files, et cetera. It combines APIs and user interface (UI) interactions to integrate and perform repetitive tasks between enterprise and productivity applications.

These are just some of the ways cognitive technologies and robotic process automation in insurance can act as key enablers for customer centricity. Leveraging the plethora of capabilities offered by such tools, insurers can now design customer journeys from scratch rather than simply replicating existing journeys that are at best yesterday’s stories with merely a bit more processing efficiency. The resulting combination—termed robotics and cognitive automation (R&CA)—encompasses a potent mix of automated skills with application across the insurance value chain. R&CA is expected to foster greater collaboration between human and machine by both automating repetitive tasks and enhancing the quality of jobs.

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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. Middle management can also support these transitions in a way that mitigates anxiety to ensure that employees remain resilient through these periods of change.

As a Director in the U.S. firm’s Strategy Development team, he worked closely with executive, business, industry, and service leaders to drive and enhance growth, positioning, and performance. Craig received a Master of International affairs from Columbia University’s School of International and Public Affairs, and a Bachelor of Arts from NYU’s College of Arts and Science. This means that processes that require human judgment within complex scenarios—for example, complex claims processing—cannot be automated through RPA alone. Roles requiring perceptual human skills, such as handwriting recognition or facial identification, and other cognitive abilities—planning and reasoning, for example—could also be considered. Based on Deloitte’s analysis of some of the key jobs in the RE sector, many property appraisal, budget analysis, accounting, bookkeeping, and auditing and property management tasks appear ripe for RPA application. RE companies may even consider using R&CA technology for future cash-flow projections, billing, payables processing and payroll applications.

By augmenting RPA with cognitive capacities, we can create dynamic and adaptable virtual workers that execute tasks supplied by cognitive components, thus unlocking the true potential of knowledge work automation. R&CA essentially replicates human actions and judgment at tremendous speed, scale and quality, all at a relatively lower cost. Robotic process automation (RPA) uses software to automate many manual, repetitive and often rules-based processes and tasks.¹ Cognitive automation uses machine learning capabilities for judgment-based processes and predictive decisions. Natural language processing, natural language generation, machine learning, cognitive analytics and sensing are some of the cognitive capabilities that can revolutionize RE ecosystems. The IBM Cloud Pak® for Automation include a single, expert system and library of purpose-built automations – pre-trained by experts – and draws on the extensive IBM domain knowledge and depth of industry expertise from 14,000+ automation practitioners.

The continuous technology advancement is creating and enabling more structured and unstructured data and analyses, respectively. The real estate (RE) sector has the opportunity to leverage one such technology, R&CA, to potentially drive operational efficiency, augment productivity, and gain insights from its large swathes of data. Currently, organizations usually start with RPA and eventually work up towards implementing cognitive automation. Considering factors like technology cost and data type helps find the optimal mix of automation technologies to be implemented. Essentially, organizations that leverage both technologies can provide the best outcomes for customers and the overall business.

It requires deep knowledge of different products, frequent market assessments as well as communications with the market-leading providers to make a well-founded choice. In a production environment, RPA streamlines business operations and reduces the risk of error by automating repetitive tasks and processes, including anything from back-office parts inventory management to the assembly line. RPA can also be used to anticipate inventory using data analytics to evaluate existing inventory usage rates and collate that information to generate a recommendation.

• 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.
• There is growing need for robots that can interact safely with people in everyday situations.
• Automation will expose skills gaps within the workforce, and employees will need to adapt to their continuously changing work environments.

A key feature of cognitive robotics is its focus on predictive capabilities to augment immediate sensory-motor experience. Being able to view the world from someone else’s perspective, a cognitive robot can anticipate that person’s intended actions and needs. This applies both during direct interaction (e.g. a robot assisting a surgeon in theatre) and indirect interaction (e.g. a robot stacking shelves in a busy supermarket). According to the 2017 Deloitte state of cognitive survey, 76 percent of companies surveyed across a wide range of industries believe cognitive technologies will “substantially transform” their companies within three years.

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RPA was able to deploy bots that extracted text or data from various information sources and automatically generated relevant forms, such as invoices, proposals, or digital records. Each of those meant individuals did not have to manually transfer data across forms, saving many work hours. Set up for industrial-era operations, many public sector organizations are hierarchical, function on command and control principles, are labor intensive, and do not sufficiently employ digital tools for handling routine processes.

RPA tools interact with existing legacy systems at the presentation layer, with each bot assigned a login ID and password enabling it to work alongside human operations employees. Business analysts can work with business operations specialists to “train” and to configure the software. Because of its non-invasive nature, the software can be deployed without programming or disruption of the core technology platform. CIOs are now relying on cognitive automation and RPA to improve business processes more than ever before.

Insight three: Good news! Vendor and tool selection is not a make-or-break decision

Our German and English speaking advisors leverage their market experience to tailor automation solutions to your business demands and guide you through the establishment of your automation center of excellence. 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. Robotic Process Automation (RPA) is the use of software to automate high-volume, repetitive tasks.

RE companies should consider evaluating processes and tasks that can be automated and the technology implementation approach. Technical staff need to be up to date on the latest digital tools such as AI, ML, NLP, and data analytics. Each of those things is part of RPA and IA, so keeping abreast of important developments in these areas is crucial for federal employees.

This decision will largely depend on their budgets, estimated return on investment and the sense of urgency to automate existing tasks. To tap the potential benefits of R&CA technology, consider evaluating processes and tasks that can be automated, the technology implementation approach and possible adjustments to their talent strategy. Both federal employees and the customers/clients being served must understand how bots operate and where they are drawing information. Being transparent about choices and processes aids the user experience and builds confidence in the applications. Examples of IA include analyzing agency hearing texts to discern topics, handling complaint logs, and managing customer satisfaction. IA technologies also encompass data analytics that can track agency performance, a subset of tools that represent a way to interpret information in an increasingly sophisticated and efficient manner.

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• Comparing robotics to cognitive automation becomes essential when trying to decide which technology to adopt or whether to adopt both if needed.
• According to the 2017 Deloitte state of cognitive survey, 76 percent of companies surveyed across a wide range of industries believe cognitive technologies will “substantially transform” their companies within three years.
• Because the capabilities of various solutions may be similar, you can make a decision and experiment, gain experience and familiarity with the technology and capabilities, and redirect toward a different vendor if you are unsatisfied or as your needs evolve.
• A key feature of cognitive robotics is its focus on predictive capabilities to augment immediate sensory-motor experience.
• With the automation of repetitive tasks through IA, businesses can reduce their costs as well as establish more consistency within their workflows.
• While RPA software can help an enterprise grow, there are some obstacles, such as organizational culture, technical issues and scaling.