Understand the difference between process and cognitive automation before deciding which to implement in your organisation
There is a lot of hype in the RPA world around cognitive automation. This leads to many questions from individuals in organisations that are just starting their RPA journey – the primary one being, ‘should I be using this?’
To answer that question, we need to explore 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
What is cognitive automation?
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 automation
- Mismatches between contracts and invoices
- Banking: customer screening
- Insurance: assess the impact of policy changes
- Insurance: make automated claims decisions
How is AI used in cognitive 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 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.