By Hazel del Rosario-Lee
Digital transformation is today?s biggest buzzword in enterprise technology. Behind the buzz are key elements such as machine learning, artificial intelligence (AI), bots and other forms of automated intelligence. The end result is to change enterprise data into information assets that will fuel the competitive advantages of enterprises and by extension, the national economy.
AI has been around for quite some time. Its recent rise to prominence can be attributed to data and the computing power required to process that data.
Enterprises are drowning in data and IT experts are looking to AI-enabled technology to make processes more efficient, enable faster/improved customer experiences and expedite smarter decisions across the organization. This entails the capture and analysis of staggering amounts of data, which only advanced AI capabilities can provide.
AI can deliver a spectrum of services such as smart analytics, predictive analytics and rigorous risk assessment, all of which can lead to actionable information for management and business-decision makers.
With smart analytics, AI can crunch large amounts of data into useful information to bear on intractable enterprise problems. On the factory floor, AI can apply business intelligence like pattern recognition to streamline time-consuming and repetitive tasks. By learning business patterns and behavior, AI can deal with persistent trouble spots in the production line and suggest the automation of certain processes without the need to program code. It can also assist in schedule management so the right machines and the right tools are available at the right time.
In the purchasing function, AI can help preempt adverse situations so management can respond ahead of time. For example, a projected delay in the purchase of raw materials will lead to delays in the delivery of the final product, buyer dissatisfaction and ultimately penalties, payment delays and reduced repeat orders.
Predictive analytics allows AI-enabled apps to fast-track even more detailed customer profiling and give companies greater and expanded insights into individual buying behavior and other distinct consumer habits. Firms will also be able to conduct high-end data mining to synthesize actual consumer reaction to their products and from the findings, undertake appropriate changes to the offering to make it more acceptable to the marketplace.
? Predictive accounting. AI can facilitate the transition of the existing financial system from historical models to a predictive accounting system in which transactions are monitored as they are posted and patterns may be drawn up straightaway to validate business issues for prompt resolution.
? Predictive auditing. Predictive auditing moves beyond the traditional or continuous auditing model. While continually testing data, predictive auditing would begin to understand the flow of data and identify when things are out of the ordinary.
Predictive risk assessment is among AI?s strongest suits. It can be applied to a host of revenue-generating business activities ranging from financial and economic forecasting to project development strategy, asset portfolio management and uncovering internal irregularities affecting financial matters.
Financial institutions will find the most value in AI?s powerful risk evaluation suite of functionalities, particularly in the areas of risk rating and the prediction of default and bankruptcy. Deploying machine learning, banks, for example, can mine large data sets and unstructured information to identify signs of unwanted breaches such as fraud and money laundering. The same functionality thereby improves on and strengthens the bank?s compliance with industry standards and BSP?s regulatory framework.
The business benefits of AI are no longer confined to large-scale corporations. Even small and medium enterprises, especially those that subcontract jobs with big firms, can reap the benefits.
The author is the managing director for Oracle-NetSuite Philippines