According to Deloitte, “RPA continues to meet and exceed expectations across multiple dimensions including improved compliance (92%), improved quality/accuracy (90%), improved productivity (86%) and cost reduction (59%).” RPA is being used in many ways across multiple business sectors such as banking, retail, transportation, government and so on. RPA has advanced considerably in the last couple of years and is expected to manage customer-oriented processes along with internal processes, in the coming years.
According to Gartner, “The Robotic Process Automation software market will grow by 41% year over year to 2020”. The big tech companies like Google, Amazon, Apple and Microsoft are investing heavily into machine learning and RPA. Thus, for the last couple of years, robotic process automation has been deployed extensively across many industries such as retail, utilities, finance, government etc. Despite of this growth, RPA is still shrouded with many myths around its potential and working. But before busting these myths, let’s first understand-
Hospitality industry relies heavily on the front line service delivery staff. And that’s why it is more prone to human errors. A small mistake in placing a zero in the customer’s bill, miscommunication in noting down the customer preferences, etc. can lead to loss of money as well as customer satisfaction.
Human care and technology play a key role in the Healthcare Industry. While technology can’t replace the human touch and emotions, which is the soul of this industry, it can surely help in eliminating the limitations of human capabilities.
25% of customer service and support operations will integrate virtual customer assistant (VCA) or chatbot technology across engagement channels by 2020, according to Gartner.
The digital landscape has changed remarkably over the past decade. Almost every business, irrespective of the industry is realizing the digitization benefits and embracing it as well. With this, digital data is generated, faster than ever. In fact, some of the industries are at a point where manual data analysis is a next to impossible job. This has driven the rise of Machine Learning (ML), which is the ability of machines to analyze big data and extract information, just like humans do.
The accelerated market hype around Artificial Intelligence has made it a buzzword of almost every industry. Businesses, irrespective of their industry are interested to invest in the potential of AI to automate, assist, and augment various value-based tasks.
The ever growing interest of businesses and the market hype for Artificial Intelligence (AI) is making it one of the predominant element of tech-industry. AI, essentially is a broad term that defines the ability of machines to exhibit human intelligence. While real-AI is a near future, the current AI technologies are still offering a level playing field to almost every industry today, including healthcare, manufacturing, transportation, and many others.
Artificial Intelligence (AI) remained the driving force of various industries in 2017. With so many tech giants and startups already delving into the AI ecosystem, it is expected to grow with better use cases in the year 2018. Considering the acceptance, development, and applications of AI, here we are with significant opportunities and perils that this ingenious technology will put forth in 2018.
Can you imagine a mobile app automating and controlling a number of tasks for the users, without being explicitly programmed for it. Well, that might sound fanatic, but it’s a practicable model today. Thanks to Machine Learning!
Machine Learning (ML) is subset of Artificial Intelligence (AI) that analyses a set of data, builds an analytical model, and then predicts accordingly. By implementing ML to mobile apps, businesses can ensure better experience to the existing and anticipated users.