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Artificial Intelligence for the Real world

Artificial Intelligence is still a vague blank space in the minds of many people. As part of revolutionizing the way executives understand artificial intelligence, it is important for them to see AI from the lens of its business capabilities rather than as a mere added technology.


This would mean, conveying the different needs of business that artificial intelligence can be integrated to solve. The three key areas AI can be used in business are to automate business processes, big-data analysis for deeper insights, and efficient customer and employee engagement.


However, what’s more important to understand is that integrating AI for these business needs is not an added cost, rather, it's a cost-effective efficient means of conducting business operations to the full potential.



How might AI help businesses to fulfil these needs?


Automating business processes:

Having providing process automation services to many of our clients, we have catered to back-office/administrative tasks, alongside key business operations. Replacing the human intervention in these tasks, we have promoted a more efficient, time-saving and cost effective environment for task completion. Tasks include:

  • Transferring call and email data to records, or information repositories.

  • Analysis of resumes, or other profiles for apt filtration of job applicants.

  • Academic essay analysis, and feedback.

  • Reading, vetting, and extracting data from legal documents or contracts.

  • Helping e-commerce companies compare prices of competition.


These technologies, also referred to as robotic process automation (RPA) are the least expensive and easiest to integrate for companies through our end-to-end plug-in solutions. It is also important to note that people believe implementing RPA would quickly put people out of jobs. However, that is not the case. All our clients using RPA models have declared these models to work well with existing employees helping them enhance their performance by taking out the menial work. We noticed that employee turnover was not a common objective nor a common outcome of our integrations.



Delivering insights and market sentiment:

Another popular use of our models is seen in understanding volume trends, shifts in demand, or studying client behavior. Some of the tasks we have engaged in are:

  • Personalizing marketing campaigns for each individual cohort.

  • Identify risk of fraudulent activity, or cyber attacks.

  • Providing hotels with insights of geographical forecasted demand.

  • Helping companies understand popularly demanded products.

These models use machine learning algorithms, used to analyze big-data contextually. With this said, data is very important to run these models efficiently, and so, we provide data curation and training. This data is taken into account and then analyzed to draw underlying patterns and trends for mass populations. Generally, these models are used to make machine executed processes more efficient. Hence, there are no risks to jobs, and are cost-effective.




Engagement:

NLP (natural language processing) is a widely used name nowadays. These algorithms are used to make chatbots, interactive agents, and even intelligent conversationalists. Some of the models that we have included are as follows:

  • Interactive agent to turn negative sentiment into positive conversation using cognitive behavioral therapy.

  • Education, career and college counselling advisors.

  • Chatbot to guide customers through website navigation and answering basic questions.

  • Anxiety and depression analysis through text.


We saw an average of 25% increase in customer retention with the use of such technologies on websites. These engagements make customer interactions more fruitful and interactive. Additionally, these engagements can be executed in multiple mediums and languages. Whether you want speech in Mandarin, or free flowing text in Spanish, it is all possible.



We accept that each enormous organization should investigate technological advances. There will be a few obstructions, and there is no space for smugness on issues of labor force uprooting and the morals of savvy machines. In any case, with the correct arranging and improvement, intellectual innovation could introduce a brilliant time of efficiency, work fulfillment, and success.



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