Challenges business facing to implement AI

Everyone, there’re pros and cons of every technology in the world— and artificial intelligence or AI is of no difference to the rule. But, it becomes important as the technology producers and consumers to identify such challenges and reduce minimize the risks associated and also ensure that we will take complete benefit of the technology. So what is standing in a way of Artificial Intelligence realizing its potential? Given are some biggest challenges that many companies have to address in case they are looking to start making some effective use of AI powered tools, which are available.

Data Access

Data is a main part of any digital economy, so for the companies who want to apply AI for many areas, data access will be the biggest challenges that they will have to face. Data will be one biggest challenge that companies face. To train the machine learning algorithms you need clean data sets, and with very minimum biases. One has to remember data privacy issues while it comes about harvesting personal data, mainly for General Data Protection Rule that is in effect from 2018.”

No emotional intelligence

Increasingly, many companies want to use AI technology for supporting their client service efforts. Example, most of them are building the AI powered chatbots, which customers may interact with on the platforms such as Facebook Messenger.

Whereas early incarnations of the chatbots for such platforms left much to be desired, NLP technology is fast improving and the AI-driven bots also are getting better in knowing what humans they are interacting have to say.

One way that brands can address such challenge is limiting the AI application to the customer service and where empathy is not necessary. The chatbots can be made to serve as the front line service, and responding to FAQ and handling easy and low-emotion requests. If requests are complex and sensitive, then AI-powered chatbots must connect the customers to the human representatives.

Specialization

AI powered technologies currently are much better at certain specialized tasks than others. For example, take AI based content creation, the dream of marketers all over. In 2018, around 20% of the business content is going to be produced by the machines. Whereas there’s the evidence that AI can create some types of content, which is indistinguishable virtually from the human content in accuracy and clarity the machine formed content is more boring and not very pleasant to read.

Simple to collaborate

AI can take over the complete marketing campaign and may need require cooperation of several AIs.” But, in theory, it is not a deal-breaker. However, reality and theory is not the same thing.

• It is fun and simple – to envision the complex collection of the AI-performed components collaborating to make fully automated and perfectly modified customer experiences. However, that system can be prone to the frequent failures as some component finds facing conditions that it was not trained to handle it. Suppose systems are well made, then components can shut themselves when it happens.