AUGMENTED WORKFORCE

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 AUGMENTED WORKFORCE




What is Augmented Workforce ?

An augmented workforce is a blend of human employees and technology working on tasks together . By utilizing AI and cognitive technologies , companies are already learning how they can streamline processes and create more efficient workforces . Machine Learning is a key element of this , where computer programs access data and learn for themselves .

Journey of Augmented Workforce :

Various Data Engineers studied the idea of building a machine that had the intelligence of a human , basically an electronic brain . Of course , the breakthrough for this was the introduction of the first digital computer in the 1940s . Since then , the capabilities of digital computers have evolved massively and the digital revolution has shaped the world’s working processes .

Traditional manual jobs are becoming obsolete or changing considerably because new technology capabilities can drive great efficiencies . The automation of processes that were typically performed by a human is resulting in reduced jobs of this nature . For businesses , efficiencies leverage profits so it is easy to see why so many companies are investing in AI .

41% of companies have fully implemented or have made significant progress in adopting cognitive and AI technologies within their workforce . This means AI is not a futuristic vision but is being implemented in workplaces around the world right now .

AI in Business as Augmented Workforce :

There are many different ways in which AI is being used , varying by industry and influenced by budget and in some cases company culture or ethos . Car manufacturers are working on driverless cars and  driverless trains have been introduced in some countries , including Japan . Progress in this area will obviously have a major affect upon the number of driving jobs in the future with potential benefits being cost savings and increased safety .

In many businesses , AI is being used to for marketing automation , support and service , for example , the use of chat bots . They can answer questions based on keywords that the user types into the conversation and provide quicker customer service compared to a single employee manually managing multiple conversations .

Personal assistants like Siri do the same thing ; they pick up keywords to find the most relevant option they can find . Whilst this can work very well in some cases , users can become frustrated if they are not getting the answer to their question because the technology is unable to interpret the context .

The rise of intelligent personal assistants in the home is the most recognized example of this with Amazon’s Alexa and Google Home pioneering the way for voice activated search .

Categories of Augmented Workforce :

  1. Robotic Process Automation ( RPA ) : The simplest form of automation , RPA technology automates repetitive rule - based processes . This technology cannot learn , adapt or make decisions ; an RPA bot simply applies a consistent set of rules to a process to deliver quick and efficient outcomes . Many manual administrative processes can be streamlined in this way .
  2. Machine learning : At the next level is machine learning, where a computer is able to use large volumes of data to understand and predict the desired course of action , with performance improving over time . Chat - bots are a good example of machine learning being used today in the financial sector . These bots use technologies such as natural language processing ( NLP ) to communicate in real time with human customers , use data from past interactions to understand the nature of the customer's query , and provide the desired information or response .
  3. Cognitive augmentation : Cognitive augmentation is the closest we currently have to true artificial intelligence . Cognitive computers , such as IBM's Watson , are able to handle unstructured data and provide answers to complex queries , enabling them to complete tasks that could once only be performed by humans .

Though these categories denote levels of complexity , it is better not to think of automation technologies as stages through which an organization must progress . Instead , each technology is best suited to particular types of work and may be used in concert to achieve larger goals .

Intelligent automation ( IA ) is a term increasingly applied to this concept of combining multiple automation technologies to solve complex business issues . For example , organizations are looking to use RPA with machine learning , NPL and digital character recognition to help address regulatory compliance challenges and process high - volume , low - complexity insurance claims .


Written By - Ritesh Pandita  ©

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