In today’s economic landscape, business functions are expected to deliver more with less – whether it be improved KPIs, reduced cost, or increased quality. Intelligent automation can address this challenge by leveraging advanced technologies such as robotic process automation (RPA), artificial intelligence (AI), and machine learning to handle an increased percentage of transactions. However, positioning intelligent automation among the business leader community has, to date, been somewhat of a challenge.
Businesses around the globe are at different stages of implementing automation, with varying levels of maturity and understanding – and many organizations start their automation journey with a mindset of reaping benefits from the “low hanging fruit.” While this can often create that eureka moment, the truth is many automation projects just fizzle out – priorities shift, interest wanes, stakeholders change, and the RPA return on investment story simply fails to scale beyond that initial success.
Process vs. task
Key to understanding how intelligent automation can take you beyond RPA is the difference between the concept of a “process” and a “task.” Although the latter works well to enjoy initial success with RPA technology, to automate multiple tasks that form multi-departmental end-to-end process, transformation and automation that leverages multiple technologies is needed. This is where intelligent automation comes into play.
Intelligent automation is about approaching process transformation intelligently – which means knowing what to automate, when to automate, and how to automate with what kind of tools and technologies. Note that tools and technologies appear at the end of the sentence.
The intelligent automation toolbox
Having advanced technologies such as RPA, AI, and machine learning in your toolbox is certainly important, but they are only as good as the workman who owns the toolbox. Here’s a quick summary of the most important intelligent automation technologies:
- RPA – simple, medium, and complex process task automation technologies – such as product suites from UiPath, Automation Anywhere, and Blue Prism – embed advanced AI algorithms into processes to make traditional RPA more flexible
- AI – using algorithms that leverage deep learning technologies to simulate how the human mind senses its surrounding environment, AI can be applied to unstructured document recognition, communications, and image sensing. Natural language processing (NLP) is a subset of AI through which machine learning models extract information from unstructured human speech or written language. Chatbots are built on NLP frameworks, and email and social media communication can be analyzed for sentiment, tone, and intent using NLP. While back-office supplier desk and vendor management can be automated largely with RPA and AI, the strategic application of NLP can be in any proactive contact center operations where organizations monitor customer sentiments in advance to address customer grievances.
- Machine learning – with transaction processing creating a huge amount of data, machine learning can be leveraged to study patterns and make various associations, predictions, and recommendations for the next best action. In procurement, machine learning can be used to analyze sourcing patterns and behaviors, and provide real-time monitoring of supplier performance against the contract. In collections, machine learning models can predict “propensity to pay” and improve cost to collect and DSO (days sales outstanding).
Transforming the world of automation
I started this article with how business is expected to deliver more with less. My advice is this:
- Automate your processes intelligently with a comprehensive set of technologies including AI and RPA
- Use machine learning models to predict your performance targets, understand current state and make appropriate adjustments to the process.
Intelligent automation is the art of applying AI technology to aid process transformation, and it is revolutionizing the world of automation.
To learn how Capgemini’s Intelligent Process Automation has helped enterprises adopt the tools, methodologies, and practices to set up intelligent automation projects that accelerate benefits to your business, contact: email@example.com
Read more about how Capgemini’s Cash Collections Assistant powered by Artificial Intelligence recently won the “best virtual agent solution” by AI Breakthrough.
Prasanna Velayudham is responsible for advising clients on RPA and business process automation, as well as bringing accelerated and sustainable benefits to enterprises.