AI, RPA and machine learning – why it matters

Dienstag, 12 März 2019 13:37

Machine learning, Artificial Intelligence, Robotic Process Automation, Cognitive Services, Interactive/chat Bots. These are terms you will come across in literature and conversations almost every day. What do they really mean and more concerning to the average person what will be the role of the human employee in this new digitally transformed world that we are rapidly moving towards?

The ever-expanding landscape of machine learning, with its ability to enable systems to learn and improve from data, opens up a world of possibilities. Artificial intelligence, with its remarkable capability to emulate human intelligence and perform tasks with remarkable precision, continues to reshape industries and redefine what was once thought to be exclusively human endeavors. Robotic process automation steps in, streamlining repetitive tasks and freeing up valuable human resources for more creative and strategic endeavors. Meanwhile, cognitive services offer a glimpse into the realm of enhanced decision-making and problem-solving, harnessing the power of advanced algorithms to extract insights from vast amounts of information. In order for RPA tools in the marketplace to remain competitive, they will need to move beyond task automation and expand their offerings to include intelligent automation (IA).

Machine Learning vs. Artificial Intelligence: What's the Difference?

Understanding the latest advancements in artificial intelligence (AI) can seem overwhelming, but if it's learning the basics that you're interested in, you can boil many AI innovations down to two concepts: machine learning and deep learning. Artificial Intelligence is a technology that enables a machine to stimulate human behavior to help in solving complex problems.

The concept of machine learning has been around for a long time (think of the World War II Enigma Machine, for example). At a high level, machine learning is the ability to adapt to new data independently and through iterations. In other words, machine learning involves computers finding insightful information without being told where to look. Machine learning brings out the power of data in new ways, such as Facebook suggesting articles in your feed. Machine learning and advanced analytics could be a game changers for insurers, for example, in the race to improve compliance, reduce cost structures, and gain a competitive advantage from new insights. In order for RPA tools in the marketplace to remain competitive, they will need to move beyond task automation and expand their offerings to include intelligent automation (IA).

What is Intelligent Automation?

Intelligent Automation (IA) is a combination of Robotic Process Automation (RPA) and artificial intelligence (AI) technologies which together empower rapid end-to-end business process automation and accelerate digital transformation.

To extend the horizons of business process automation by an order of magnitude, Intelligent Automation combines the task execution of RPA with the machine learning and analysis capabilities of automatic process discovery and process analytics as well as cognitive technologies, like computer vision, Natural Language Processing, and fuzzy logic.

Intelligent Automation spans the entire automation journey discovery, automation, optimisation automating any front- or back-office business process, and orchestrating work across combined human-bot teams.

Uses for intelligent automation

There are many uses for IA, all of which ultimately help provide a better customer experience. Some of the uses include the following:

Intelligent document processing (IDP): Forms of business data like images, emails and files often appear in an unstructured format. IDP uses IA tools like RPA, machine learning and natural language processing (NLP) to extract, validate and process that data.
Process discovery: IA can help create a complete guide for automating a process using RPA.
Streamline workflows: IA can use data to automate workflows for faster, more efficient processes
Production and supply-chain management: IA can be used to predict and adjust production to respond to changes in supply and demand.

Combine the power of RPA and AI to empower rapid end-to-end business process automation Intelligent Automation (IA) is a combination of Robotic Process Automation (RPA) and artificial intelligence (AI) technologies which together empower rapid end-to-end business process automation and accelerate digital transformation.

The phenomenon called Big Data (a term coined in 2005 by Roger Mougalas) has allowed us to make more effective use of data, we can see things in a new and better way. It also accelerated the development and convergence of the technologies I have listed above.

In its top 10 strategic technology trends for 2019, Gartner highlighted the impact AI will have over the coming years. The global research and advisory firm expects that by 2022, at least 40% of new application development projects will have AI co-developers on their teams. In 2019, robotic process automation (RPA) and artificial intelligence (AI) will join forces to create digital workers for more than 40% of enterprises, predicts Forrester Research. Also that 25% of leaders will use automation to address the shortage of talent, according to Forrester.

Robotic Process Automation (RPA) is the technology that allows anyone today to configure computer software, or a “robot” to emulate and integrate the actions of a human interacting within digital systems to execute a business process.

What this means is that a so-called digital worker will be able to carry out the basic repetitious routine processes of information work, freeing people up to do more complex, more exceptional and difficult tasks. For people in the workplace distinguishing between data and information, as well as knowledge and meaning, will be a key skill.

According to Barry Devlin (a founder of the data warehousing industry), decision-makers will need to make information-informed evaluations, explore the meaning of the underlying facts and over and above that include other relevant considerations.

This changes the face of the workplace, job descriptions and opens up new opportunities, however it also creates uncertainty regarding job security. By 2021, Forrester estimates that there will be more than 4 million robots doing office and administrative work as well as sales and related tasks.

Enterprise RPA is the disruptive force in digital transformation.

This data-driven technology convergence has allowed us to create insights about ourselves and organisations that range from personalised algorithms in search engines to computational criminology. This intensive use of data has given rise to artificial intelligence. More enterprises have adopted RPA functions to automate rote, repetitive tasks, but sometimes they need more capabilities. Enter machine learning functions and the result is "intelligent automation" which, unlike RPA, can learn and adapt.

AI will challenge the knowledge worker in the same way that factory automation challenged the factory worker. It is a technology we have to master just like past discoveries and inventions. Most importantly, we need to harness it in a way that holistically benefits us all. That will be the first task for the organisation.