How AI Is Changing Corporate Decision Making Until the introduction of AI and ML, major decisions were taken based on months of researching and data collection

Artificial intelligence (AI) has slowly made its way into everything we see around us. From basic shopping tips and movie recommendations on OTT platforms to the use of AI in healthcare, technology will change the way businesses function. Apart from assisting in operating businesses, predictive analytics will be the key in revolutionizing corporate governance. Corporate governance refers to the decisions made by the top brass of any business.

Until the introduction of AI and machine learning (ML), major decisions were taken based on months of researching and data collection. However, with the advancements in the field of analytics, it is now much simpler to make those decisions. Predictive analytics provides information and helps with possible outcomes through the use of various models and techniques. Not to mention the billions of dollars saved by preventing misinformed decisions and subsequently generating more revenue with the help of it. Here is how predictive analytics is the way forward in strategic discussions.

A shift in decision-making methods

With more and more businesses understanding the potential of predictive analytics, a shift from traditional decision-making methods to newer, well-informed ones is observed. Business leaders are making use of these methods in order to not only improve operations but also enable their employees. Those in the healthcare sector have already benefitted hugely. It helps hospitals and clinics to sift through a vast collection of medical records, find patterns, and further assists in planning the next steps for patients. A good example is provided by the American company Salesforce which uses a proprietary AI named Einstein to get predictive performance information for their board meetings.

Data science and its advantages

Data-driven decision making is the way ahead for any business, irrespective of the sector. The basic idea of predictive analytics is to combine past and current data to obtain actionable insights about the future. In terms of the hospitality industry, businesses can rely on the use of AI and ML to forecast the number of guests during a specific period. This is possible by using previous trends along with current data to help hotels in dealing with customers. Since these predictive methods are heavy on data, the strategic imperative is clear for early adopters. They develop proprietary data sets that put them above competition.

Models of predictive analytics

In order to fully analyze the data at hand, various models are used. For instance, a manufacturing business will pay more attention to the quality assurance model. Under this, data collected over time helps in identifying defective products. As for those in the retail/wholesale business, predictive analytics assists in inventory management by forecasting past and current trends. It may also help in managing and assuring timely deliveries to customers/retailers. Service providers related to insurance companies are making use of predictive analytics to give personalized quotes within seconds. A health startup called Plasmit is using AI imaging to perform real-time diagnosis.

Importance of understanding the value of predictive analysis

However, to fully implement predictive analytics into any business, one must learn the basics of it. While the businesses right now may not fully incorporate AI and ML into their decision-making process, the future will be highly dependent on it. Complex decisions require the help of complex computing. The ability to seamlessly use both instincts and experience along with the use of predictive technology will prove fruitful in the future. It is therefore imperative for future business leaders to fully understand the potential of it. Predictive analytics can provide trends of competition, facilitate the dilemma of capital allocation to various lines of business and cut down the due diligence time for new product launches.

What is required from a business?

To harness the power of predictive analytics, a business must fully commit to understanding and implementing the same. High levels of investments are required in terms of money as well as time. While it may provide short-term or instant solutions, predictive analytics works effectively in coming up with long-term plans. For example, businesses which heavily rely on customer purchasing their product/service, the customer lifetime value model is insightful. It provides those businesses with the ability to pinpoint customers which are likely to buy their products/services based on various data points. Evidence gathered by Mckinsey Global Institute suggests that predictive analytics and Artificial intelligence can deliver real value to the companies willing to use it across operations and in their core functions. The report suggests, based on a survey of more than 3000 AI aware companies, that they use analytics to increase revenues as well as reduce costs. The businesses need to tune up with both training and hiring. Mike Baccala, the PwC's Assurance innovation Leader believes that companies can begin this path only when they have people who properly understand data.

Predictive analytics functions on the principles of decision trees and neural networks. Decision trees help in mapping out the various options and provide a clear picture. In terms of neural networks, numerous algorithms are used in order to identify relationships between data sets by mimicking the human brain.

From airlines using predictive analytics to set ticket prices to security agencies in identifying threats, the applications are endless. While the use of artificial intelligence may never replace or replicate decisions made by humans, it is certainly the future in corporate governance, irrespective of any sector. In order for today's youth to be well equipped, it is vital to at least have a basic understanding of it. As EY puts it, "The true value lies in embedding analytics deeply into business processes at the point where decisions are made—by human beings."