AI and business decision-making
Organizations are using AI-powered technology and tools to help make various business decisions from day-to-day operations to strategy. AI-powered tools can bring new efficiencies to procedures like real-time tracking and improved forecasting that can help businesses with their decision making, Harvard Business Review reports.
While generative AI is quickly creating new possibilities for business operations, machine learning is the power behind much of the faster, more accurate and more efficient decision making across many industries. Through machine learning AI, organizations can augment many critical processes, allowing them to make better decisions in real time. Machine learning technology is accelerating many industries, including financial services, manufacturing, software and IT, retail and healthcare.
Closer look at machine learning
Machine learning is a branch of AI that involves algorithms that are used to identify patterns in large amounts of data. It allows computers to learn from data, relying on specific algorithms to recognize patterns, find anomalies and make predictions.
Specifically, machine learning makes it possible for computers to process substantial amounts of complex data compared with conventional analysis methods. A report from the International Institute of Business Analysis notes, “One of the most significant contributions of machine learning to business analysis is its ability to process vast amounts of data swiftly and accurately.”
Because of its sophisticated pattern recognition and flexibility to alter patterns, machine learning has made advancements in areas of data analysis including predictive analytics, defined as a “branch of advanced analytics that makes predictions about future outcomes using historical data combined with statistical modeling, data mining techniques and machine learning.”
As a result, machine learning allows organizations to use the extensive amounts of data that they can now access in many forms (raw, structured, unstructured) for advanced decision making. Machine learning algorithms help mine large datasets for key insights that can offer real-world benefits for improved business decisions.
Use cases of machine learning span industries
Machine learning AI systems can be employed for decision making and strategic planning in various industries from retail to manufacturing, finance and healthcare. Recently, McKinsey & Company reported that their research found “more than 400 use cases of machine and deep learning across 19 industries and nine business functions.”
Among these industries, some top business use cases that have emerged include:
Fraud detection
Machine learning is adept at looking for anomalies and fraudulent activity in various business transactions. Organizations are increasingly using AI tools for what they consider high-impact tasks such as fraud detection, and a recent study from PYMNTS.com shows that 30% of COOs surveyed are using AI for fraud detection.
Machine learning algorithms can analyze transaction data (e.g., time, location, amount and business) to identify patterns and flag potentially fraudulent transactions in real time. This makes them ideal for detecting credit card fraud, banking and sales fraud and identity theft. These systems can operate at the point of sale (POS) and for mobile payments, e-commerce fraud and invoice fraud.
Other techniques, such as natural language processing (NLP), are also being used to integrate advanced system and network security in AI-powered firewalls for enhanced threat protection.
Sales decision making
Various types of sales analytics can be greatly enhanced by machine learning. Advanced predictive analytics and analysis of historical sales data and real-time data can help inform a variety of decisions such as sales forecasting, sales strategy, inventory planning and supply planning.
Machine learning systems and models make use of all relevant customer and sales data including past and current sales, marketing and financial data. Since machine learning can analyze huge data sets, its prediction capabilities are faster and more accurate, giving businesses the ability to make more precise decisions.
Customer relationship management and customer experiences
Some top use cases of machine learning systems involve both customer relationship management and customer experience through the analysis of enormous amounts of customer data.
Machine learning powers recommendation engines that give businesses the ability to provide highly personalized customer experiences that can help with customer retention. Through precise analysis of customer data, retailers can use machine learning systems to more accurately meet and even predict the wants and needs of their clientele.
With AI machine learning tools, companies can offer a better customer experience by accurately analyzing behavior and shopping patterns. Shopify notes that machine learning ”empowers retailers to make smarter, faster decisions by analyzing vast amounts of data and uncovering patterns that might otherwise go unnoticed. This leads to smarter, data-driven decisions that directly impact performance.”
Finally, customer insights from machine learning systems can also help to minimize customer churn by more accurately assessing patterns in sales, demographics and customer behavior.
Smarter decisions, stronger results
AI and machine learning are transforming how businesses make decisions—faster, smarter and with greater precision. Whether it’s predicting customer needs, detecting fraud or optimizing operations, these technologies help turn complex data into real-time insights. Companies that harness these capabilities effectively are better positioned to adapt, compete and grow.