How AI Solutions Address 5 Persistent Business Challenges?

Businesses have to face several challenges at varying phases of operation. Even if their area of operation and nature differ, every business, at some point will have to face some long-standing troubles. The application of AI as a tool to solve pestering business troubles has increased in recent years.

Let us take a look at 5 key business challenges and how AI-powered solutions address them.

Digital Fraud

The increasing influence of digital and mobile transactions increases the access of a customer to services and information. But this also means that criminals are getting what they want; the opportunity to access confidential personal and financial data. Companies are struggling to find the right balance between meeting ever-increasing customer expectations and keep their guard up against fraud.

AI is the only technology that has evolved enough to offer the expected speed to transactions. Data can be assessed within seconds by using AI and machine learning algorithms. Companies like Feedzai and Sift Science make use of this functionality to accelerate their data processing operations.  The decrease in fraud, spammers, and financial crimes are the added benefits of making AI and machine learning a part of business operations. Companies including Door dash and Poshmark have reported that the use of AI solutions has helped reduce chargebacks, fraudulent transactions, and customer spamming.

Customer Service

Customer service has become an integral part of business operations that decide the success of businesses. Even when companies succeed in delivering faster transactions, they often fall behind in customer service.

AI helps companies offer responsive customer support across various channels, that too without even requiring a human being to handle the queries. Solutions powered by AI reacts to customer queries while simultaneously checking the complex software grid of the company in order to offer assistance to operators in real-time.

Some companies use virtual assistants and chatbots. The approach of companies using AI in customer service may be different. Some use virtual agents to communicate with the clients; the approach of Verint next IT is an example. However, some like Agara hire operators and give them AI tools.

Personalization

Customers prefer the convenience of shopping at the comfort of their homes but they also want the brands to see them as individuals and not lag behind in personalization. Online selling businesses have a quite large customer base and there is hardly any face-to-face interaction happening. These features make personalization a struggle for online businesses.

Amazon was one of the first enterprises that used AI featured personalized recommendations made by studying the data related to past orders. That was just the beginning of the journey of AI solutions to the stage they are now at. Persado uses the continual learning process of AI by which it decides on formatting, word choice, and word positioning of personalized marketing messages.

Dynamic yield takes AI-powered personalization a step further by exploring ways to add personalization throughout the whole customer journey. Case studies show that the use of AI has significantly increased conversion rates and revenue of companies.

Analysis Of Data

A higher volume of data is beneficial but structuring and mining all this information is not an easy task. There is no denying that AI has become an important part of data analysis in the last decade but organizing that data is still a complex procedure.

DataRobot has successfully figured out ways to automate a part of the process of developing AI and machine learning applications including those developed for data analysis. Analytics experts and data engineers can build efficient data analysis models that improve their data analysis processes powered by AI.

H20.ai created an open-source platform to improve the ability of AI to analyze data with desired accuracy and transparency. The platform developed by the company has offered great help to industries operating in the financial, healthcare, marketing, and manufacturing sectors by assisting them in improvising data analytics and business decisions.

Productivity

To enhance productivity and get the best out of their workforce, companies have to adopt smarter solutions. AI contributes immensely to increasing productivity by helping devise smarter solutions.

Appnomic is an enterprise that is famous for the proactive approach it takes to solve issues that affect the continuity of business applications.  They use AI to predict IT issues and prevent them before they can cause any potential damage to the enterprise. In this way, productivity issues are prevented before they even occur. Several industries from financial to manufacturing to retail have made use of the solutions offered by the company.

If it was not for AI’s prediction capabilities, businesses would be left with the burden of solving the issue, as well as the damage it has caused. The IT department gets more time to focus on their core activities, as they are free of the burden of having to handle the job of checking damages.

Issues related to legacy processes are major concerns for industries like financial services, insurance, and healthcare, as they affect the productivity of the industries in the digital age. AI-driven Vidado primarily plans to fix these issues. With the power of AI, the company helps these industries transform paper processes to automated digital processes, thereby speeding up their much needed digital transformation. Higher efficiency indicates increased productivity and lower expenses.

AI Solutions

AI-enabled solutions make it possible for businesses to address age-old business challenges in the right way. Secure transactions, customer satisfaction, more advanced data management, improved customer interaction, and increased productivity are the results you can expect by employing AI to address business challenges.

Merely deploying AI solutions does not do all the work. The success of AI solutions depends on the user’s knowledge about when and how to use these solutions.