Businesses today are harnessing the power of AI in many ways, from call centers using AI-powered chatbots to banks using deep learning to analyze countless data points and detect fraud in seconds.
Factories use artificial intelligence to automate complex physical tasks that require adaptability and dexterity. Marketers use artificial intelligence to generate personalized recommendations and automatic order fulfillment. The list is practically endless. A range of services taken for granted today, from credit card fraud detection to email spam filters and predictive traffic alerts to personalized reminders, would not be possible without artificial intelligence.
One area where AI is widely used is business intelligence. Companies use deep learning algorithms to identify behavioral patterns that can lead to sales, use IoT sensor suggestions for predictive maintenance and inventory optimization, and more.
However, what companies are doing now is just the tip of the iceberg of possibilities.
Artificial intelligence enables direct decision-making
With data distribution, many companies run the risk of overloading data. The unprecedented growth of big data and the obsession with analyzing this data can easily embarrass the core businesses of the company. Business intelligence software enables artificial intelligence to break down data into manageable information and make big data meaningful.
Artificial intelligence can also change the dynamics of analysis. Conventional data analysis focuses on descriptive analysis or data analysis to report on what happened. The current generation of AI-based analytics tools enables predictive analytics or the use of data to decipher future insights. However, it is based on “best guesses” with behavioral and historical data used to estimate the probabilities.
The recipe analysis expects to take over in the near future. Artificial intelligence-based prescription analysis tools will split through the vast amount of data, allowing users to prescribe different possible actions and recommend feasible solutions. Prescriptive analysis not only predicts but also gives good advice and explains why things will go the way they go or happen.
The transition from reactive predictive analytics to proactive prescriptive analytics increases the power and relevance of business decisions. Real-time and real-time information enables companies to make better use of industry data and make decisions based on what is happening today rather than what happened in the past. Many of the recommendations can also be automated, with the best course of action determined by the smart machine based on available input.
Artificial intelligence brings voice and facial recognition to the center of the stage
Voice-activated personal digital assistants with AI technology fascinated Generation Y with style. The emergence of deep learning applications such as speech recognition interfaces, their widespread adoption by companies and the huge popularity of digital voice assistants such as Apple Siri, Amazon Alexa, and Google Assistant are signs of the future. Voice is replacing keyboard and touch interfaces as the standard for people who interact with brands across all industries.
Likewise, facial recognition techniques for adults are poised to make leaps and bounds with current levels in the near future. AI-powered facial recognition technology can only make the password very annoying.
AI drives hyper-personalization
AI-driven intelligence learns from experience and improves with every experience or transaction. Since the next decision is automatically prescribed better than the previous one, the stage where the AI model is very mature and includes all events is not far off.
It’s even better. Systems powered by the artificial intelligence of the future can automatically decipher the emotions of the user and even the users from voice commands that will soon become the order of the day, to make highly accurate recommendations or communicate with them on a truly personal level. The next wave of AI technology assistants will be able to analyze large amounts of data contextually, in real-time, to quickly understand needs and priorities.