of companies reporting a positive business impact of digital transformation also reported higher net revenue growth.
Source: Deloitte
increase in customer satisfaction can be achieved through digital transformation with a focus on customer.
Source: Mckinsey
of companies expect that digital transformation will disrupt their industry, but only 44% are prepared for digital disruption.
Source: Deloitte
Conversational AI combines natural language processing (NLP) with machine learning. These NLP processes flow into a constant feedback loop with machine learning processes to continuously improve the AI algorithms. Conversational AI has principle components that allow it to process, understand, and generate a response in a natural way.
Machine Learning (ML) is a sub-field of artificial intelligence, made up of a set of algorithms, features, and data sets that continuously improve themselves with experience. As the input grows, the AI platform machine gets better at recognizing patterns and uses it to make predictions.
Natural Language Processing is the current method of analyzing language with the help of machine learning used in conversational AI. Before machine learning, the evolution of language processing methodologies went from linguistics to computational linguistics to statistical natural language processing. In the future, deep learning will advance the natural language processing capabilities of conversational AI even further.
NLP consists of four steps:
Unstructured data transformed into a format that can be read by a computer, which is then analyzed to generate an appropriate response. Underlying ML algorithms improve response quality over time as it learns.
These four NLP steps can be broken down further below:
Users provide input through a website or an app; the format of the input can either be voice or text.
If the input is text-based, the conversational AI solution app will use natural language understanding (NLU) to decipher the meaning of the input and derive its intention. However, if the input is speech-based, it’ll leverage a combination of automatic speech recognition (ASR) and NLU to analyze the data
During this stage, Natural Language Generation (NLG), a component of NLP, formulates a response.
Finally, machine learning algorithms refine responses over time to ensure accuracy.
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Human Error – No matter how qualified and equipped your human workforce is, they will always be prone to distraction in some form which will lead to errors in jobs being processed and this can completely be eliminated by deploying BOTS saving businesses millions of dollars annually.
Scale – For most businesses that want to scale, RPA is the greatest invention ever as it allows firms to focus on growth and utilize their human workforce to think and act differently rather than spend time on training and developing human resources to perform mundane administrative work.
High Performance – In order to achieve robotic efficiency and performance, BOTS needs to be deployed across the organization as they can process the most complex tasks in the quickest manner and ensure accuracy which enables the human workforce to review and deliver high performance through quality outputs.
Costs – By implementing RPA, firms can drastically reduce their spending on various business functions such as F&A, HR, administration, IT, contact centers, and much more. Due to the speed and delivery with RPA, organizations are reshaping their HR and business strategies and increasing their spending on RPA technology. You no longer need 10 resources in the same process. You could do with 3 while the rest are deployed in other core business functions.
