What is cognitive automation and when should you use it?

Understand the difference between process and cognitive automation before deciding which to implement in your organisation

Cognitive automation is rapidly transforming business operations by integrating AI capabilities into traditional process automation. Leveraging cognitive automation to enhance decision-making and operational efficiency starts by learning the differences between process automation and cognitive automation:

What is process automation?

Process automation could be better described as ‘traditional’ RPA. It allows for the automation of repetitive, structured, and logical tasks quickly and effectively. Some other key characteristics include:

• Structured data
• High stability and consistency, low variability
• Simple and rules-based, low complexity
• High volumes
• Digital inputs

Essentially, process automation brings speed, accuracy and precision to time-consuming, ‘mundane’ tasks that typically don’t require the human user’s specialised expertise and decision-making.

Use case examples for process automation

• Finance: invoice and expense processing
• HR: joiners, movers, leavers
• Technology: data migration, data cleansing, password unlock and reset
• Contact centres: one customer view, updating customer profiles, customer complaints
• Operations: customer onboarding, account opening, renewals, account closing, change of address

Understanding cognitive automation and its business benefits

Cognitive automation refers to the use of elements of artificial intelligence (AI) to enhance process RPA, allowing the tools to make ‘judgements’ and increasing the ability to automate business process steps that require specialised expertise and decision-making.

Characteristics of cognitive automation include:

• Situational and decision-oriented tasks
• Unstructured data
• Machine learning – training based on analysis of historical and ongoing data
• Decisions made by Bots or escalated for human input (and further training)

Use case examples for cognitive process automation

• Mismatches between contracts and invoices
• Chatbots
• Banking: customer screening
• Insurance: assess the impact of policy changes
• Insurance: make automated claims decisions

How is AI used in cognitive process automation?

When we refer to AI in the context of cognitive automation, we are referring to the three elements that allow RPA tools to understand data and extract actionable information. These are:

• Natural language processing (NLP): The ability to understand text and spoken words in a way that mimics human understanding, then reply in a similar fashion. A clear example of this in action is an organisation’s chatbot that analyses customer questions and requests then serves appropriate replies.
• Intelligent optical character recognition (OCR): Industries subject to heavy regulation, such as legal, finance, and healthcare, require accurate character recognition when paper documents are processed. This is a key element of automating historically manual processes such as contract review.
• Machine Learning (ML): As the previous two elements take care of ‘understanding’ unstructured data, Machine Learning is the decision-making element that puts that data into action. This ‘machine judgement’ replaces human judgement when processes require decisions to be made.

Whilst cognitive automation will never replace the need for human input in all scenarios to resolve highly complex conditions, it allows for the scope of automation in organisations to take a few steps further.

You can see a direct comparison between process and cognitive automation in the flowcharts below:

When should you use cognitive process automation?

The hype around cognitive automation can unfortunately often inspire too much initial confidence and scope in some organisations’ RPA pilot projects. Often, this is not down to failures in the cognitive technology itself, but rather the failure of organisations to fully appreciate the complexities and risks of implementing it.

The phrase ‘don’t run before you can walk’ is appropriate in the context of cognitive automation.

The ultimate success of RPA in many organisations is based on early-stage successes and confidence, providing the levels of adoption, interest (crucially across both business and IT stakeholders) and budgets that require further investment at each stage. Many organisations rush into using cognitive automation and experience limited success with significant cost. This means that adoption is low, funding for ongoing maintenance and support isn’t forthcoming, and confidence investing further quickly erodes. RPA therefore often ends up ‘on the shelf.’

When you are exploring automation opportunities in your organisation, clear ‘quick wins’ will be available in the form of process automation. If these are implemented well and with appropriate stakeholder buy-in, they can have significant cost, speed, and efficiency benefits. This in turn establishes confidence and allows the business case to move to the next stages and levels of adoption, during which cognitive automation will become increasingly relevant.

In most scenarios, an effective combination of process and cognitive automation, often referred to as ‘Intelligent Automation’ will lead to the most effective optimisation of manual processes and thus yield the highest return on investment.

Emerging use cases in various industries

Cognitive automation is reshaping industries by enhancing processes, improving decision-making, and driving efficiency. Let’s explore how it’s making a difference across various sectors:

Healthcare

In Healthcare, cognitive automation is revolutionising patient care and administrative tasks. By using AI-driven bots, hospitals can manage patient records more accurately and swiftly. For instance, automated systems can scan and analyse medical histories to assist doctors in diagnosing conditions or suggesting treatment plans. This not only speeds up the process but also reduces human error. Additionally, chatbots are being used to handle patient inquiries and appointment scheduling, freeing up valuable time for medical staff to focus on direct patient care.

Finance

The Financial Services industry is leveraging cognitive automation to enhance customer service and streamline operations. Banks and financial institutions use AI-powered systems to conduct customer screenings, detect fraud, and manage compliance tasks. For example, cognitive tools can analyse transaction patterns to identify and flag suspicious activities. This helps mitigate fraud risks and ensure regulatory compliance. Cognitive automation also aids in processing large volumes of data quickly, allowing financial analysts to make informed investment decisions.

Manufacturing

Manufacturing plants are adopting cognitive automation for predictive maintenance and quality control. By integrating AI with IoT devices, manufacturers can monitor equipment performance in real-time, predicting failures before they occur and reducing downtime. Cognitive tools also help in quality assurance by analysing product standards and deviations, ensuring consistently high-quality output. This not only boosts efficiency but also enhances product reliability and customer satisfaction.

Cognitive automation is unlocking new possibilities across these industries, driving operational excellence and innovation. By automating complex, decision-oriented tasks, businesses are experiencing increased productivity, better resource management, and a significant return on investment.

Latest trends in cognitive automation

Cognitive automation is rapidly evolving, with new trends reshaping how businesses operate and interact with technology. These trends are not only shaping the current landscape of cognitive automation but are also paving the way for future innovations. By staying ahead of these developments, businesses can harness the power of cognitive automation to drive growth, enhance productivity, and deliver exceptional value to customers.

Here are some of the most significant trends driving this transformation:

AI integration

One of the most prominent trends is the seamless integration of artificial intelligence (AI) into cognitive automation systems. By embedding AI, businesses can automate complex decision-making processes, enhancing accuracy and efficiency. AI algorithms can analyse vast amounts of data to predict outcomes and provide actionable insights, enabling companies to make informed decisions quickly. This integration is particularly beneficial in sectors like finance and healthcare, where rapid and precise decision-making is crucial.

Real-time data processing

The ability to process data in real-time is another crucial trend in cognitive automation. With advancements in technology, businesses can now gather and analyse data as it is generated. This real-time processing allows for immediate responses to changing conditions, improving operational agility.

Enhancing customer experiences

Cognitive automation is increasingly being used to enhance customer experiences. By utilising AI-driven chatbots and virtual assistants, companies can provide personalised and efficient customer service around the clock. These tools can handle routine inquiries, provide product recommendations, and even troubleshoot technical issues, all while learning from interactions to improve future engagements. This not only enhances customer satisfaction but also frees up human resources for more complex tasks.

Automation-as-a-Service (AaaS)

The rise of Automation-as-a-Service (AaaS) platforms is making cognitive automation more accessible to businesses of all sizes. These cloud-based services offer scalable and customisable automation solutions without the need for significant upfront investment. Companies can leverage these platforms to automate specific processes or entire workflows, achieving greater flexibility and cost-effectiveness.

Focus on security and compliance

With the increasing adoption of cognitive automation, there is a growing emphasis on security and compliance. Organisations are implementing advanced security measures to protect sensitive data and ensure compliance with regulatory standards. Cognitive automation tools are being designed with built-in security features, such as encryption and access controls, to safeguard information and maintain trust.