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AI and ML Boost Productivity, Here’s How

August 7, 2020by admin0

Artificial Intelligence and Machine Learning are increasing organizational productivity and performance. The AI market is expected to expand at a CAGR ( Compound Annual Growth Rate) of 52 per cent by 2025, according to the Research and Markets survey. This lets businessmen make choices that apply to the industry more educated. The difference between AI and ML is that while AI is a larger concept of creating smart machines that can undertake various tasks within an organisation, whereas ML is a subset of this concept that deals with computing data and aiding in the prediction of various decisions. AI has the ability to improve a business’ efficiency through the convergence of knowledge and communication technologies, intelligent computer control, early notice of possible problems and much more.

So have there been any long term effects when companies turn to AI and ML. So what is the primary difference between AI and ML? What kind of companies have used it in the past to help their productivity?

An Example of Artificial Intelligence in practice

Artificial intelligence allows Cable and Wireless customer support representatives to assist customers more efficiently and with better precision. In the past, Alvin Stokes, senior vice president for consumer engagement at Cable and Wireless, says customer support employees will have to memorize all the latest client practices and campaigns of the business. We will need to sift through several files checking for one item of consumer details at a time, sharing that knowledge with the client while on the line. Artificial Intelligence Vs Machine Learning within this organisation? Now the customer support representative interacts with a virtual agent to deliver the responses they need, says Stokes, instead of keeping five to six windows open on their laptop at a time searching for details that might address the consumer’s query. The employee literally types or speaks the search words and the AI can find the right details and transmit it. “The time it takes to get a reply is shortened, and the answer becomes more precise,” says Stokes. This also reduces the tension for staff at call centers, particularly for new hires. “New hires needn’t memorize anything straight out of the gates,” he says.

 An Example of Machine Learning in practice

Personalization is highly valuable to consumers in the modern-day and age, as it means faster service, more relevant options, and better all-around experiences. Big data and customer metrics, including real-time information, have made it possible to deliver more targeted service options. This is where we see the true genius of implementing AI or ML. Starbucks is at the forefront of this, using their mobile app and vast data stores to display preferred orders to baristas before customers even get to the counter. It also improves performance considerably, speeding up order and service times, especially during the busiest hours.

How does it work? Members of the Starbucks rewards program and mobile app often use it to order drinks, call in future orders and take advantage of exclusive benefits. At the same time, the company uses this service to gather a lot of information about their customers’ habits and buying preferences. That is precisely how they can provide preferred order information to baristas. Simply the existence of this much data to process expounds the difference between AI and ML. Machine learning deals with computing large data sets like this and creating a database which makes smart decisions that help the business grow.

But the company also uses this information to build more relevant marketing campaigns and promotions, decide locations for new stores or potential business and even decide future menu updates.

As one can see above, most graphs speak the same fundamental truth. AI and ML are tools that must be utilized to improve productivity in appropriate ways. It’s a nuanced concept that not many understand, here at ITConnectUS we know the exact difference between AI and ML to develop a plan that will help your company’s productivity improve.

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