How to Leverage Cognitive Computing for Effective Intelligent Process Automation

Business is moving faster than ever, and organizations are looking for ways to increase efficiency and speed decision-making. That is where cognitive computing and intelligent process automation (IPA) come in—two forces of change that are transforming business operations. By combining cognitive technologies with automation Robot Process Automation companies are able to reduce their operational costs significantly while offering outstanding customer experiences. In this blog, learn how you can maximize the potential of cognitive computing in intelligent processing automation-with some relevant use cases and compelling facts.

Cognitive Computing and Intelligent Process Automation in a Nutshell

Cognitive Computing

Cognitive computing involves leveraging the power of advanced technology to emulate sub-set human thought processes. It covers a host of technologies from artificial intelligence (AI), to machine learning, natural language processing and computer vision. With the help of these technologies, systems can understand and respond to data which work similar like human thinking hence improve decision making,problem solving feature.

Intelligent Automation

Intelligent process automation builds on the existing traditional robotic process automation (RPA), but adds cognitive technologies. RPA is known for rule-based task automation, but IPA can handle any variety of data — structured or unstructured and with decisions made on those findings. As a result, enterprises can successfully automate complex end-to-end processes which in turn enhances the operational efficiencies.

Key Technologies Enabling Cognitive Computing in IPA

• Natural Language Processing (NLP): As a domain, it enables machines to interpret human language styles making applications such as sentiment analysis and intent classification possible. For example, it can be useful in customer service; putting context around inquiries that achieved by effectively processing what the user wrote.
• Computer Vision: Which involves teaching computers how to interpret visual data. Computer vision, for example — insurers can use this technology to run images of damaged vehicles through an algorithm that will assess the damage immediately and automate a claim approval.
• Machine Learning: This learns from historical data to be able to identify patterns and make future predictions using those learned patterns. These algorithms learn over time and get better at making decisions with IPA.
• OCR (Optical Character Recognition): This allows data conversion of different documents into editable and searchable information using OCR technology. This is crucial for automating data entry tasks, particularly in the case of environments that deal with piles and piles of papers.

Implementing Cognitive Computing in IPA

Here are the steps that companies should adopt to carry out cognitive computing in intelligent process automation:
• Identify Automation Opportunities: Look for redundant tasks and processes within existing workflows, their origin in the system so that you can automate them. Concentrate on where cognitive technologies can be of real use, such as non-transactional and unstructured data or decision-making activities.
• Select Suitable Tools: Choose cognitive computing platforms and tools that align with your automation goals. Ensure these solutions integrate smoothly with existing systems and are scalable to meet future needs.
• Formulate a Strategy: Develop a comprehensive strategy for implementing IPA. Clearly define objectives, establish key performance indicators (KPIs), and outline a roadmap for deployment.
• Training and Optimization: Conduct training to make sure employees can use cognitive technologies efficiently To meet changing business needs, processes get continuously optimized and this is where the real value of any technology happens.
• Monitor and Evaluate: Keep track of how automated processes perform and their impact on business operations on frequent intervals. Analyze the data to learn and make informed decisions for future improvements.

Cognitive Computing Case Studies That Will Make You Believe in Intelligent Process Automation

• Insurance: A leading insurance company harnessed cognitive automation to transform its claims processing. By employing computer vision and NLP, the firm could analyze images of damaged vehicles and extract relevant information from claims documents. This innovation halved the claims processing time and significantly boosted customer satisfaction.
• Banking Industry: A major global bank used intelligent process automation to modernize its customer service operations. The bank combined NLP and machine learning, built its own bot that did transaction processing more naturally, personalized the interaction medium for users and facilitated taking actions by making smart recommendations only. The result of this initiative was 30% decrease in customer service costs and more effective customer interactions.
• Healthcare: A health care service delivery organization used cognitive computing to optimize patient data management. Utilizing OCR and Machine Learning, he was able to automate the data extraction from all kinds of medical documents which enabled a faster patient intakes process. In turn, this brought about enhanced accuracy of the data as well as reduced administrative pressure on healthcare providers.

Benefits of Cognitive Computing for IPA

• Amplified Efficiency: Automating repetitive tasks allows organizations to save time and resources, enabling employees to focus on more strategic activities.
• Better Decision-Making: Cognitive technologies facilitate the analysis of large data sets, leading to quicker and more informed decisions, thus enhancing business agility.
• Cost Reduction: Intelligent process automation reduces reliance on manual labor, resulting in significant cost savings and optimized resource allocation.
• Enhanced Customer Experience: By leveraging cognitive computing, businesses can provide faster, more personalized services, leading to higher customer satisfaction and loyalty.
• Scalability: Cognitive solutions can easily scale with business growth, allowing organizations to adapt to changing market conditions.

Challenges and Questions to Consider

As powerful as cognitive computing can be, there are numerous impediments organizations will face with automation:
• Integration Complexity: Despite the fall in the top-line, Cisco continued to enjoy strong margins. GAAP gross margin for the fourth quarter was 64.4%, slightly higher as compared to 64.1% for the similar period a year ago. The non-GAAP gross margin was even better, 67.9%.
• Data Privacy and Security:Operating margin was 19.2% on a GAAP basis, compared to 28.0% last year — impacted by low revenue along with an increase in operating expenses. The operating margin at 32.5% on a non-GAAP.
• Change Management Employees might be not comfortable with the changes automation is introducing. It is an important aspect requiring effective change management strategies for smooth transitions.

Conclusion

At the core, Cognitive computing offers breakthrough to Intelligent Process Automation allowing organizations automate multifaceted workflows while augmenting decision-making prowess. Using technology such as NLP, computer vision and machine learning to deliver more efficient outcomes that are in turn less cost-prohibitive — contributing directly back into the top-line through a better customer experience. More importantly, as organizations adopt these advancements across the organization, they ready themselves to win in an future defined by automation and cognitive capabilities. Businesses that implement these transformative technologies will innovate faster and adapt better to a world where most work is being automated.