Artificial Intelligence is gradually but slowly becoming a part of our daily lives. It doesn’t matter if it’s as simple as removing the background of the image or intricate functions like autonomous automobiles; we’re preparing to have a reliable and error-free future.
Table of Contents
7 advantages of AI
Let’s look at each benefit individually, as well as the problems they address, as well as real-world examples.
1. Automation
At the beginning of the 90s, companies were heavily dependent on manually entering data and documents to run their business. This usually required a huge amount of human effort as well as time to finish.
Today, a lot of firms are relying on software robots to perform these tasks in order to increase efficiency and workflow and stay competitive. Automation is among the most widely publicized benefits of AI and is extensively used in every industry from manufacturing to transportation.
Simply put, it’s the process of executing easy, repeatable tasks in accordance with the instructions of individuals. While automation does require some human involvement, it’s certainly not as labor-intensive as its predecessor.
For instance, insurance companies go through the tedious process of evaluating, validating and approving claims. This process can take weeks, and sometimes even months, from beginning to end. This results in unhappy customers, an enormous backlog of claims and a reduction in revenues.
BNP Paribas Cardiff improved their workflow by automating the most important processes, which reduced the time required to process claims from 4 weeks to just 10 minutes. This allowed them to use the time saved on more productive tasks like increasing their marketing and customer-facing activities.
2. Smarter decisions
There’s a reason that companies are putting more emphasis on the ability to feel emotions than before. It aids in removing subjectiveness and bias from the decision-making process. If you’re working with businesses with high asset risk in their accounts, you simply cannot afford to make a mistake.
Artificial intelligence can analyse patterns, establish data consistency, forecast, and detect irregularities – all of which assist in making better independent, data-driven choices. It’s not surprising that 86% of business leaders are employing AI to manage their business.
For instance, recruiters’ talent scouts are charged with finding an ideal person for the job. They are usually stifled with logging, categorising and evaluating the applicant’s information and time is taken away from what they are supposed to do.
AI can help by helping in two different ways. The first is that it takes the responsibilities off of Scouts (automation), which frees the time and space for them to travel and search for candidates. The second benefit lies in the process of selecting candidates.
AI can evaluate thousands of CVs in just a few seconds, free of bias, resulting in an efficient selection process that could increase the accuracy of matching and longer employee retention.
3. Better customer service
From long wait times to unresolved tickets, bad client service has become a frequent complaint across the globe, leading to an endless amount of frustration, inefficiency, and stress
However, when customer service representatives receive email after email and calls after call, you’ll be hard-pressed to criticise them for giving poor customer service.
There are a myriad of ways in which AI has benefited customer service over the past few years, and especially through the introduction of intelligent chatbots. They’re proactive, instead of responsive (like normal bots) and can take the inputs of users and then algorithmically come up with answers that are free of (much) human intervention. Although there’s a need to connect to an agent for more specific cases, the advancements in augmented chat mean it’s just a matter of time before chatbots are able to efficiently handle customer requests all the way through.
For instance Watson Watson-powered “digital concierge” 1-800-Flowers uses Natural Language Understanding and Natural Language Generation to process orders from customers. Instead of filling in a form and having someone call you, you’ll just type in your query into a chatbot, and 1-800-Flowers can answer your question and assist you during the purchase.
4. Accurate Medical diagnosis
In 2020/2021, the number of medical claims filed to the NHS for medical malpractice increased by 133% from 2006/2007, highlighting the urgent necessity for innovation in the healthcare industry.
In the past 50 years, doctors have achieved significant advances in the use of AI to make more precise diagnoses and treatment of diseases. Basing their systems on earlier rules-based systems, they’ve overcome integration challenges to create an AI model that is able to fulfil this critical task with accuracy that is comparable to or even more than humans.
Initial signs point to significant improvement, with experts declaring that the application of AI for analysing and evaluating images from radiology and mammograms could increase the speed of analysis by at least 30 times, with an accuracy of 99.
For example, Google’s DeepMind is engaged in developing Deep Learning technology to fill the gaps in various sectors. A key component of this project, which is a collaboration with Moorfields Eye Hospital, is developing a neural network that can analyse 3D retinal scans in order to identify more than 50 kinds of eye diseases.
5. Faster data analysis
Processing and analysis of data is always at the core of any successful business strategy. With the increasing volume of data available, it’s becoming a daunting task for analysts who must manipulate information in a manner that provides meaningful insights without a lot of time and money.
AI in analytics is classified into predictive analytics, in which the AI utilises historical data to predict future events and prescriptive analytics, in which the AI does not just predict and recommends the best course of action and augmented analytics, in which the AI is able to draw valuable insights from data.
For instance, arguably the most trusted tool for data analytics, Google Analytics uses a robust AI which continuously scans your data for anomalies, which are the result of significant changes that could affect your business. Google Analytics straightforwardly provides this data to enhance the quality of your decisions.
6. Reduce human error
Human error has historically resulted in some very tragic outcomes. Millions of dollars lost due to property damage. Cyber attacks. Aeroplane crashes. Data loss. It even happened to Titanic “unsinkable” Titanic.
Human error reduction is a priority that crosses all disciplines, which is essential to cut down on time, lessen the financial burden following losses or damage and increase efficiency. Additionally, AI is being used as a viable option for automation, forecasting, and more.
For instance, Suntory Pepsico in Vietnam had to deal with production delays and costly stops when the Quality Assurance agents failed to scan expiration date codes on labels due to printing problems or other problems.
To solve this problem, the company came up with an AI solution that incorporates cameras, dubbed “Machine Vision,” which could read labels instantly and determine if that code is valid. If a label is damaged or unreadable, it would be removed by an ejector without stopping the production line, thus making the entire process more efficient and fluid.
7. More precise forecasting
In the past, analysts used data from the past to predict the future value of metrics like the performance of assets or revenues. But the past does not always reflect the future. Moreover, the heavy reliance on historical data made it more difficult to determine the gap between forecasts and actual values.
However, AI-driven forecasting that is automated utilises real-time data constantly identifying new patterns to predict changes in the market, allowing companies to react quickly and in a swift way, reducing the risk of losing margins and ensuring that they are protected.
For instance, in 2017, when many of the traditional retailers struggled to report positive figures for revenue, Walmart saw a 63 per cent increase in sales online over the course of the year. AI and prescriptive analytics were the key to this increase.
Walmart’s AI immediately took the sales data of its point-of-sale systems and integrated this into its forecasts to figure out the products that were most likely to sell out. The system was in a position to suggest alternatives immediately to shoppers who downloaded the app, thus improving the purchasing experience.
