How will AI shape the future for human commerce?
AI is a branch of computer science referring to the ability of machines to perform tasks that generally require human intelligence. The world of AI comprises of machine learning (supervised and un-supervised), natural language processing (NLP), neural networks, and deep learning. Each of these segments plays a vital role in making AI capable of whatever it has to offer today.
AI allows machines to model, train, and iterate on data sets inclusive of historical facts, geographical facts, human responses, and task-oriented scenarios. It can take on jobs associated with human cognitive functions such as identifying logic and patterns, interpreting speech, conversing, and answering queries on certain topics based on acquired data. These AI capabilities have and will continue to help humans run a business successfully in various ways such as:
- Automating routine tasks so that human resources can be engaged with strategic and creative aspects of an enterprise.
- Identifying new trends and opportunities so that new business owners can actualize them for accelerated growth.
- Reducing recurring costs by identifying areas that need improvement and optimizing present operations.
- Assisting with detecting and preventing fraudulent practices by identifying patterns and discrepancies in data.
It is important to note that AI optimizes itself through learning algorithms. These algorithms are data centric and thus, each time some new information is fed to the machine, it will produce a new outcome. Also, like humans, Artificial Intelligence is adept at recognizing patterns and structures. This will lead to it making routine decisions on someone’s behalf – allowing for additional time for other important tasks. For instance, contact centers are assisted by smartbots to answer repetitive or similar kind of questions, which in turn, helps with customer service.
Additionally, AI can achieve a greater percentage of accuracy through deep learning. Software agents like Alexa, Siri, and Cortana are good examples of deep learning. The way they provide incredible insights to a wide range of questions and requests, has been achieved through diving deep into neural networks. With more data on the way, Alexa and Siri are destined to offer more useful and effective content for your problems in the future.
What is predictive analytics?
Predictive analytics is a data-driven applied science that focuses on how an organization and its various departments can work together as a profitable enterprise. It presents logical and believable predictions based on past and on-going patterns and data.
Once data is collected, it is organized to suit the convenience of the human eye. The second step involves analyzing it and steadily modelling it on certain pre-requisites and objectives.
Modern-day tools and tech platforms have made predictive analytics accessible for big as well as mid-sized companies in an effort to better understand consumer behavior, identify potential risks, and prepare with a backup plan to avoid losses.
However, before leveraging the capacity of predictive analytics, organizations should have a clear picture of their business goals and objectives. Whether it is empowering customer engagement or improving operational duties, the analytics produced by any reliable data analytics software will help make important business decisions.
What future benefits do predictive analytics offer businesses?
In the past few years, businesses have leveraged the power of predictive analytics to retain old customers as well as acquire new ones. Any AI-backed analytical software is one data report away to foresee hits and misses for most kinds of businesses.
- Forecast future demands and trends;
- Foreshadow potential risks and competition;
- Analyze customer behavior through past and present data;
- Suggest strategic allocation of tasks;
- Emphasize relevant and personalized customer experience content creation;
AI-led strategy preferred over human-led strategy
Predictive analytics existed before AI in the business world. Though, most of it was guesswork and the rest of it was based on finite observations and the previous sales. There was also a time when market leaders, innovators, and scientists made claims based on their individual studies. Some of these predictions came true and some didn’t.
Today, predictive analytics has adopted the nuances of AI and machine-learning almost completely.
Artificial Intelligence can identify and catalog broader patterns that humans easily miss. Also, human beings are naturally biased creatures and have the tendency of opting notions or theories that human brains find easiest to accept. But AI tags other variables and performs permutations and combinations to predict all possible outcomes rather than a select few. Therefore, it is safe to say that the brilliance of present-day AI-driven bots and tools can predict the future of all sectors.
Machine learning algorithms come in handy while generating predictive analytical reports. This is because they can process large amounts of data and make predictions based on their calculations.
This means that as enterprises continue to collect more data, their predictive models will become more exact and relevant. For instance, based on a customer’s past purchases, location, gender, and allergy history, a nearby supermarket using an AI tool can easily make a list of things that the customer would need in my next visit.
This is also why many FMCG giants are keen on getting their hands on AI-based predictive analysis to develop marketing strategies. For example, retailers in U.S.A have registered a significant rise in online shopping compared to consumers visiting stores. U.S. retail sales will grow by 4.2% in 2023 to $5.08 trillion, while online retail sales will increase 5.8% to $1.09 trillion.
The future looks promising..
Although AI-based predictive analytics seems inevitable in the future, a lot of components are yet to be considered. Factors like data quality, data privacy, accountability and management of data have yet to be explored completely and could make implementation more difficult in different use cases and industries. But the advent of smart bots like ChatGPT and other AI-backed tools such as Scikit Learn, TensorFlow, Theano etc. has given us hope for a better future which is free of repetitive and mundane tasks. It is only a matter of time until scientists develop something that factors-in all the above-mentioned elements. Creating a machine with human-level intelligence that is capable of being pragmatic and ethical at the same time is the ultimate goal for AI scientists.