Business applications are evolving in various ways to make business processes much easier and profit-oriented. Decision-making based on big data analytics, artificial intelligence for customer fronting and stock planning, goals-oriented machine, deep learning projects, etc., have helped modern-day businesses be more productive. However, these technologies put forth many overblown prospects of what humans and businesses can achieve to make their lives easier on the other side of the spectrum. Artificial intelligence-based applications are in vogue, and at one end, it also creates a fear of job loss. For example, bot-revolution poses a threat to millions of chats and customer support jobs.
The most fundamental piece of artificial intelligence, which was known as an artificial neuron, was first developed in 1943 by William McCulloch and Walter Pitts. From that point, we have now come a long way with numerous developments in that field to the current AI models capable of analysis, comprehension, and prediction beyond the scope of human processing. Among all these evolving technologies, artificial intelligence or AI has now become the backbone of various business applications across the globe, ranging from China to Silicon Valley.
As we said, AI is already used in many real-time business applications. The business specialties like data analytics, automation, natural language processing, etc., have the elements of artificial intelligence and machine learning elements. Across various industries, NLP, analytics, and automation are the major fields where a lot of AI R&D is happening to streamline operations and improve efficiencies.
Automation can help to reduce the overhead of repetitive tasks and the risks in dangerous tasks. Proper data analytics will provide businesses with easy and actionable insights, which was not possible ever before. The NLP approach paved the way to intelligent engines for search, chatbots for realistic support, and better accessibility to take technological benefits to the challenged individuals.
Other major uses of AI in business applications are:
– Transfer and cross-reference of data.
– Updating the files.
– Analyzing consumer behavior.
– Forecast of product demand and product recommendations.
– Early detection of fraudulence.
– Personalized ads.
– Customized and timely delivery of marketing messages.
– Automated customer support through chatbots and IVR systems.
May business experts out their testify that their applications with AI capabilities have advanced features. In fact, when you interact with many modern-day businesses, the customers tend to come across different AI applications used; customers come across many intelligent AI applications even without realizing the same. From an expert view, we can see that many AI-based business applications are now very advanced to the extent they can do many activities far better than an actual human. For achieving this goal, a good database with clean and valid data is essential to be maintained. RemoteDBA.com offers database consulting to enterprise clients with custom-tailored solutions for business database management.
Back in 2018, a Harvard Business Review report showed that AI would be the future technology to have the greatest impact on marketing services, manufacturing, and supply chain management. Three years from there, we are now witnessing that these predictions are actualizing in real-time. There is an unprecedented growth in AI-powered marketing. Brands are now able to use social media marketing in a more personalized way to connect with the customers and track the success of their campaigns.
The supply chain is consistently making AI-based advancements on a larger scale lately. There is increasing use of process intelligence-based technologies to make some high-level advancements in the coming years. It is expected that these advanced process intelligence business applications will provide a better insight into monitoring and improving business operations in real-time.
Anther real-time examples of AI-based technology include healthcare, security, and data management sectors. Considering the patient side of the healthcare sector, we can expect AI to help with more effective caregiving in terms of early and accurately diagnosing and offering timely medical care. On the provider side, we can AI-based applications may assist with everything from error-free diagnosing and data analysis to give actionable insights for physician decision-making. Ai is expected to play a larger role in streamlining the healthcare scheduling and also to help secure all the patient records.
Security and data transparency are some other areas where Ai is making a huge difference. As the customers are now more aware of how the companies gather their data for various purposes, they are becoming increasingly concerned about the privacy and security of their critical info. For businesses, it is essential to maintain optimum data transparency as to what data is collected and how it is used. Building trust on the customers bout data security and confidentiality is a key measure if any enterprise wants to grow and leverage the benefits of big-data applications.
While cybersecurity has been an all-time concern for the information technology sector, many businesses also now consider the physical threats posed to the public. In industries like automotive and transportation, this is the biggest concern. As a real-time example, we can take the case of autonomous vehicles, which must respond to real-road scenarios when an accident is imminent. Even though driverless vehicles have started their trial runs in various cities, this topic is still controversial and being debated largely. There are many tools like the Moral Machine of MIT, which are meant to gauge the public opening about self-driving cars as to what harm they may cause to the public and how it can be avoided.
However, this ethical question about technology goes far beyond the topic of mitigating damage. It may pose more moral questions to the developers. For example, while mitigating the damages, there could be a choice in the future about the moral practices to be followed by the AI applications as to whether different demographical factors like age, sex, occupation, or the criminal history of the people determine whether a person to be spared in an accident or not.
So, while dealing with AI-based business applications and solutions, the developers should have a more insightful approach to it as to develop probabilistically opposed to deterministic solutions. In the former, Ai may be able to predict how a person is likely to pay back an approved loan based on their credit history, whereas, in the deterministic model, the decision is made directly by ignoring the uncertainties. So, enterprises may be careful and diligent about making Ai applications for their business processes.
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