Robotic Process Automation (RPA), Artificial Intelligence (AI) and Machine Learning (ML) are all ‘buzz-phrases’ in this era of cognitive computing. Pure RPA has been around for quite some time – interactive Voice Response Systems (IVRS), for example, have been in existence for years. RPA enables organizations to create their own software robots to automate business processes. Bots are non-disruptive, non-invasive, with configurable software controlled by humans. The disruptive technology can create smarter business processes through a flawless performance of repetitive tasks at a fraction of the cost of traditional processes – leading up to 25 to 40% reduction in labor costs. However, there are other more ground-breaking technology capabilities like AI, ML and computer vision and cognitive automation. When converged with RPA, these technologies can produce augmented automation capabilities, with the potential to amplify business value and competitive advantages for organizations.
Transitioning to Intelligent Automation
Today, organizations are increasingly aware that the integration of the execution capabilities of RPA with the cognitive capabilities of ML can take business benefits of automation to the next level. RPA differs from artificial intelligence in that software robots must be provided with a set of instructions as they are not ‘naturally’ intelligent. While robots may be good at executing specifically defined tasks, RPA tools have a limited capability as they cannot learn from experience or adjust to new conditions. This is where artificial intelligence comes into play. AI is an umbrella term used to describe the ability of machines to carry out “smart” tasks and mimic human mental capabilities like cognition and reasoning. ML is the most common way of applying AI by providing machines access to data for self-learning. The advantage is that an AI algorithm can adapt to a new environment, learn from the outcomes of decisions and improve over time.
Organizations are increasingly harnessing the synergy of RPA and machine learning synergy
Examples of use cases include insurance claims and customer service. In the insurance sector, organizations are deploying computer vision applications that can integrate AI capabilities to assess the context of how an accident occurred to enable remote review and minor claims approval.
AI is also key to providing a more seamless experience for customers
An example is improved speech recognition and call routing in contact centers using AI to facilitate a more seamless experience for customers. AI can also help customer service representatives by providing them contextual information to handle complicated challenges that self-service cannot resolve. Today, AI software that can listen to calls and decode their impact on customers is available. This means AI can help understand how the issue was resolved, gauge if the customer’s loyalty would increase in future as a result of the call, and even flag at-risk customers. Unsurprisingly, organizations are increasingly transitioning to Intelligent Automation - a combination of RPA and different forms of machine intelligence.
Intelligent Automation at Firstsource
https://vimeo.com/353743315 At Firstsource, we are continuously innovating and investing in next-generation tools, technologies and frameworks, enabling clients to become future-ready. We understand the importance of harnessing RPA along with AI to drive significant efficiencies and generate new sources of revenue for organizations. We have set up the ‘Firstsource Automation Centre of Excellence' (FACE) that comprises a dedicated team of experienced global automation experts, responsible for roles ranging from automation consultants to scripters. The team determines exact technology, approach and scoping and conducts a detailed due diligence phase with clients to identify automation opportunities.
Mobilizing the power of AI, ML and deep learning
At Firstsource, our DNA of leveraging automation technologies has allowed us to naturally progress to employing AI to address unstructured data inputs and dynamic scenarios with clients. Some of our capabilities include:
- ML Hybrid Data Extractor – ML Hybrid Data Extractor is a machine learning-led data extraction and classification platform that excels at extracting data from structured as well as unstructured documents. Conventional Optical Character Recognition (OCR) technology is usually not powerful enough to accurately extract data from scanned documents. Our data extraction solution features Intelligent Character Recognition (ICR), word recognition and machine learning algorithms to improve the fidelity of results achieved in data extraction. The platform is compatible with different formats like a fax, PDF, email, etc.
- NLP-led chatbots and deep knowledge search – We leverage NLP-powered knowledge management and knowledge automation tools to enable contextual associate guidance and support. We use chatbots to solve end user problems by blending chatbot technology with RPA.
- AI Constellation – AI constellation is our ambitious approach to using deep learning in processes that cannot be conventionally automated. Deep learning algorithms applied to computer vision is opening doors, ranging from facial recognition to home appraisal, in the mortgage back-office space.
- Hybrid chatbots – Our hybrid AI-powered chatbots can be configured to hand over chats to human associates, if a scenario becomes too complicated for the bot or if human intervention is required.
- Knowledge automation – We leverage knowledge automation through on-demand, interactive desktop widgets that eliminate the need for a bloated knowledge base. Our knowledge automation capabilities allow content curation, discovery and search powered by machine learning, allowing associates to rapidly find knowledge articles, templates or relevant customer information.
Assisted RPA
Firstsource leverages Assisted RPA to eliminate redundant process steps. Voice, chat and email processes are difficult to automate with traditional RPA due to the dynamic nature of inputs received by an associate. Our methodology has evolved to enable RPA in dynamic processes through multiple means depending on the scenario:
- Assistance combined with RPA – Associates are provided real-time, interactive guidance in addition Next Best Actions (NBA) for triggering specific tasks.
- Hotkey-based RPA triggers – These help in launching specific RPA tasks via console or defined hotkeys.
- Unified desktop – This combines multiple screens or applications into a single interface by using automation at appropriate stages of the interface. Unified interfaces positively impact associate performance by reducing the number of transfers between screens and redundant copy-pasting of data.
While organizations that leverage RPA implementations will reap significant cost, operational and strategic benefits, it will be the potent combination of AI and other technologies that will be the truly transformative technology of the future.
Featured Resource
Download our research ‘Overcome the human challenges of business transformation’ and learn from personal experiences of 120 senior executives scoring their most significant transformation initiative against the leadership framework.