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Cognitive Automation with AI

Cognitive automation is a major buzzword these days. It combines AI (artificial intelligence) and process automation abilities for enhancing outcomes in business. It represents a wider array of approaches which boost the capabilities of automation in data capturing and automated decision-making along with enabling scale automation.

Instances of Cognitive Automation

Cognitive automation can be delineated into several examples. These include intelligent process automation (IPA), DPA (digital process automation), intelligent business process automation, cognitive service, and hyperautomation. It may also encompass the following aspects:

• Fusing intelligent data capture methods with process automation, leveraging machine vision, OCR (optical character recognition), natural language understanding, speech recognition. This is mostly classified into the DPA or IPA category.
• Automation of process-based workflows and decision-making with AI-based engines for swapping or adding to conventional business process management and other systems. These autonomous enterprise abilities enable the same autonomous abilities for business systems.
• Usage of AI-based tools and process mining for automating the procedure of identifying opportunities for automation and provisioning the same accordingly. This may be classified as hyperautomation.
• Packaging services which fuse automation and AI abilities which have been provisioned through a private or commercial app store. This is similar to the evolutionary nature of Microsoft Cognitive Services.

Functioning of Cognitive Automation

Cognitive automation indicates abilities which are provided as a part of a customised service or commercial software package. Basic services in this category enable customised offerings instead of relying on those designed from the ground-up. Business users can seamlessly customise and provision cognitive automation. Some uses may be delineated as follows:

• Automatic capturing of product data from multiple sources into a structured data set.
• Automatic retrieval of customer/support data as a response to any ongoing service/support call with the help of natural language understanding and speech processing.
• Information copying from invoices with varying formats into a standard template and loading the same into accounting systems.
• Leveraging AI-based recommendation engines for information capturing on customer intent towards streamlining overall experiences.

Cognitive Automation Advantages

Some of the key advantages of cognitive automation include the following:

• Streamlined IT service and administration tasks along with automated incident response and quicker issue identification.
• Automation for filling up gaps between RPA (robotic process automation) bots and application programming interface tools, in tandem with low-code applications.
• Automated decision-making for lowering manual tasks, combating biases, and accelerating business processes.
• Enhancing customer experiences through automation and RPA, fusing bots with virtual assistants and even conversational AI-based Chatbots.

Watch-Outs for Business

Some of the key watch-outs for companies deploying cognitive automation include the following:

• Higher level of customisation and integration for every enterprise. This comes into play when cognitive autom0ation is not used for machine vision or OCR for interpreting invoice structures and texts, among other simple tasks.
• Complex tasks will require higher customisation, planning, and ongoing monitoring.
• Longer timelines for ROI (return on investment).
• Hard-to-find talent and expertise in autonomous business systems.
• Vetting algorithms for biases.
• New security challenges arising from bots gaining access to more workflows and systems.
• Compliance/privacy breaches arising from new workflows getting data that is identifiable at a personal level.

Cognitive Automation and RPA- What Are the Differences?

A few core differences between cognitive automation and RPA should be understood in order to build context. These include the following:

• RPA covers automation of repetitive tasks, while cognitive automation is more inclined towards automation of processes.
• RPA conventionally functions with structured information, while cognitive automation is about processing unstructured information from phone calls, videos, emails, and other sources.
• RPA offers quicker ROI than cognitive automation which requires more planning and time for establishing workflows and requisite infrastructure.
• Cognitive automation offers a long-term competitive advantage, while RPA is more about harnessing short-term gains.
• RPA is simpler to track and manage, while cognitive automation necessitates extra management duties.
• Cognitive automation is more adaptable towards changing needs, while RPA may not always be as flexible.
• Cognitive automation may swiftly learn about use cases and intentions of businesses, while adapting to the same likewise. RPA bots are programmed more explicitly.

As can be seen, cognitive automation is applicable in the real-world ecosystem throughout various sectors. This includes everything from processing loans and accounts payables for financial institutions to automated onboarding of employees and even payroll. It may also enable improved sentiment analysis or opinion mining as it is called. This helps determine sentiments in various input sources and the emotions/opinions/attitudes/perceptions are classified by ML and AI algorithms. It naturally gives a booster shot to customer engagement and experience for companies. They can provide more personalised and quicker support for improved customer journeys. These are systems functioning on the basis of natural language understanding, which means that they can easily tackle queries of customers, provide recommendations, and help with various tasks.

Hence, with the growing inclination of companies towards unearthing valuable insights, trends, and patterns from multifarious and voluminous datasets, cognitive automation has a bigger role to play in the future. It will also help them adhere to regulatory compliance through the interpretation and analysis of complex policies and other regulations. They can be implemented easily into workflows, helping companies find major risks, track adherence to compliance, and also identify potential errors, missing data, or fraud. From this standpoint, it can be stated that strategic implementation of cognitive automation is the need of the hour.

FAQs

Can cognitive automation be applied to various industries, or is it industry-specific?

Cognitive automation can be leveraged throughout multiple industries. These include all customer-facing sectors including financial services, banking, and even customer support and service at companies in all sectors.

How does cognitive automation impact job roles and workforce dynamics?

Cognitive automation can lead to a major productivity boost while unlocking newer opportunities for employment. It can automate mundane and otherwise time-consuming tasks, while also freeing up employees who can focus on more value-added jobs and complex activities. This may lead to better engagement and job satisfaction alike.

How does cognitive automation leverage natural language processing (NLP) in interactions with users?

Cognitive automation adopts a knowledge-based perspective or mission when integrated into contemporary workflows. It makes use of advanced techniques like natural language processing (NLP) for its user interactions. It can thus offer better advice and recommendations along with guiding users towards the information that they require in order to take better decisions. This is also fused with text analytics, semantic technology, data mining, and machine learning.

How do organizations measure the success and ROI of cognitive automation implementations?

There are several ways of measuring the ROI (return on investment) and success of implementing cognitive automation. ROI may be calculated at the outset through deducting the investment costs upfront from the final value while dividing the new figure by the investment cost. It has to be multiplied by 100 in order to know the final percentage.

Another way is to undertake a thorough comparison of the processes at the company in terms of the future and current states. Companies can measure the success of cognitive automation through measuring cost savings (comparison of manual process costs to automated process costs). They can also track overall productivity on account of employees being freed up to emphasize strategic tasks and duties. It can be monitored through evaluating the time spent by employees on manual tasks after and before automation. Cognitive process information may also enable higher accuracy through lowering the count of errors across manual procedures. It can also be examined through tracking the count of errors prior and after automation. Other options include tracking lower risk incidents and customer satisfaction.

Can cognitive automation work alongside human workers in collaborative environments?

Cognitive automation can function seamlessly alongside human workers in environments that are more collaborative. Humans can deploy cognitive automation for streamlining various tasks and enhancing efficiency and productivity.