No strategic planning exercise is complete nowadays without prioritizing digital transformation initiatives to improve cost efficiency and unlock new value. Leaders in growth-focused organizations strive to integrate digital technology into mundane operations to streamline them and deliver more value to customers. This way, employees also get to focus on higher value-added tasks and decision making, to a large extent.
RPA is enabled by rule-based deterministic automation tools that delegate repeatable, predictable tasks to bots. The worldwide RPA software market grew 63% in 2018 and is forecast to reach $1.3 billion in 2019, according to a Gartner study, with banks, telecom firms, insurance companies, and utilities alike embracing this change.
Just look around and you can find plenty of examples in action.
RPA adoption is growing, but you must have noticed several real challenges that have emerged over the recent years.
Most RPA solutions work efficiently in a linear way. They, however, depend on human intervention to correct complex issues that are outside their scope of functioning. They cannot judge how to use specific information contextually, work with exceptions or variants, or work with unstructured data. And in general, 60-80% of organizational data happens to be unstructured. Also, while RPA may automate tasks efficiently, the time taken for it to integrate into overall processes is often underestimated – limiting its success. Most importantly, it doesn’t lead you to real process transformation by redesigning already existing inefficiencies in end-to-end processes.
Recent industry research suggests that RPA-optimized companies with conventional enterprise ops centers only achieve about 30% of their targeted automation goals. You should, in fact, only see RPA as a step towards intelligent automation that enables robots to automate processes with increased context-awareness.
This is where IPA or Cognitive Automation, comes into the picture.
It refers to the combined applications of RPA, Artificial Intelligence (AI), and other advanced technologies such as Computer Vision Technology (CVT), Optical Character Recognition (OCR), and Machine Learning (ML). It uses human-like bots creatively to mimic human activities, think and learn on their own, providing reliable support to the workforce using rich data and insights.
Here, you can augment rule-based automation with decision-making capabilities through an intelligent platform capable of processing structured data to generate deep insights. It can support employees by removing routine, repetitive tasks, and can transform customer journeys by making interactions simpler, smoother and faster.
This intelligent automation will enhance your bots with as many human characteristics as possible, including the capabilities to see, sense, learn, communicate, collaborate, empathize, and self-heal.
Recent advances in CVT, OCR, and ICR enable bots to See – read documents, identify images, and recognize objects. Your machines can easily download and analyze complex information contained in scanned images, including text, numbers, logos, and even other objects. Imagine a seeing bot that can scan thousands of invoices, purchase orders, or KYC documents per minute and also deciphers powerful information.
Bots enhanced with AI can capture key attributes of a use case, identify trends and then take the required action. Statistical models like Artificial Neural Networks, Bayesian Networks, and Support Vector Networks can provide the sensing capabilities for applications specific to your processes. Some common examples of Sense and Learn can be seen in the dynamic load balancing of process volumes and bots, business exception management, and triggering of automation based on specified event patterns.
As you know, human-human interaction is required for optimized customer service and employee engagement processes. Speech recognition software, Natural Language Processing (NLP), and Semantic Analysis now let bots understand spoken and written language, and the successful automation of such processes. For instance, chatbots connected to RPA-enhanced bots can now Communicate effectively. They can interpret, understand, and respond to your client or employee queries while also executing transitions on their behalf.
Collaboration between your machines is possible nowadays through Advanced Knowledge Management Systems (KMS), which collate, tag, store, and process data through powerful search engines. A live example of this is your chatbots that answer queries and navigate the customer or employee through a series of preprogrammed solutions.
Empathy is a key factor in customer-facing processes and in processes where customer interactions and customer satisfaction levels are the key performance indicators. Experts now predict that AI will be able to Empathize by helping bots assess human emotions. For instance, sentiment analysis algorithms can detect your customers’ emotions based on their speech, text or handwriting. Once the sentiment is determined, the human worker can be directed to the proper solution much quicker, or the chatbot can adjust its responses and solutions accordingly.
In order for your IPA program to be successful, you will need a strategic approach avoiding random installations of discreet applications. Your C-level executives must definitely be involved throughout the adoption process.
To start with, your entire organization needs to be clear about the potential benefits of IPA, and how best to maximize the related data analytics and decision-making capabilities. There must be clarity on what the goals of the program are, and where it fits within your existing operations.
Next, prepare a wish list of tasks and problems to address using IPA. Prioritize these tasks based on potential value and feasibility and define realistic ROIs for each.
It is now important to identify the strengths and limitations of your IT team and establish a well-defined governance structure. Seek professional consultation and set up a pilot program to set the ball rolling. Also set up a smart in-house team for data clean-up and integration.
Remember to start small, collect feedback, learn and adjust all along the way, and expand slowly. Plan additional storage requirements as your organization expands its IPA scope. Also, set up a feedback mechanism encouraging your employees to provide feedback.
As your IPA program builds up over time, you will have to address quality and transparency concerns. Pay close attention to protocols like bandwidth requirements, storage capabilities, safety, and security.
Combining AI with your human workforce is a critical component of IPA adoption especially during the first steps of automating complex processes. You will need to find innovative ways to audit the decisions made by these machines. In many cases, only humans will be able to recognize and analyze complex patterns and explain them. In the initial stages, you may even allow your employees to override AI-generated decisions until they gather enough data to validate the results.
IPA is in its infancy stage of adoption, and there are few success stories to substantiate it yet. At the moment, it is only on the draft board of many organizations, but its arrival is near and imminent.
As an innovative organization, you must have already automated a majority of your routine processes and are learning from the resulting data sets and outcomes. Now is the time to identify the right partners to strategically embed AI and ML into your systems, software and hardware infrastructure.
When configured and managed properly, IPA will integrate with predictive analysis technologies, automate even your complex processes, and fundamentally redesign your business processes. Ultimately, with the resulting employee productivity improvements, and enhanced value provided to customers, you stand to gain a significant competitive edge over the others.