How AI is boosting profits through revolutionary data analysis techniques
ILLUSTRATION BY RUTH MACAPAGAL
In today’s business world, “data is king.” From customer insights to market trends, businesses must rely on data to make informed decisions and stay ahead of the competition. As the “mentor of the giants,” as Fortune magazine has dubbed me, I always emphasize to our clients how important it is to analyze trends. This includes predicting changes in markets and consumer behavior.
Most companies are only one or two major strategic decisions away from failure. It only takes a small hole to sink an entire ship. The long list of famous corporate failures is a testament to that. However, with the sheer volume of data available, making sense of it all can be challenging.
That’s where business intelligence (BI) comes in. BI tools allow businesses to analyze data and extract valuable insights to inform strategic decisions. But what if there was a way to take BI to the next level? Enter artificial intelligence (AI).
AI and BI: The perfect match
AI is transforming many industries, and BI is no exception. By leveraging machine learning and natural language processing, AI can analyze vast amounts of data faster and more accurately than any human could. AI-powered BI tools can sift through data to identify patterns, detect anomalies and make predictions to help businesses stay ahead of the curve.
One example of AI-powered BI is predictive analytics. AI algorithms can identify trends and predict future events by analyzing past data. For example, an e-commerce company could use predictive analytics to forecast demand for a particular product and adjust its inventory accordingly.
One example of an e-commerce company using AI predictive analysis is Amazon. They use AI algorithms to analyze consumer behavior, search patterns, purchase history and other data to make personalized product recommendations to individual shoppers. Amazon’s AI-powered recommendation engine has been shown to increase sales and customer satisfaction, as it suggests products that shoppers are more likely to be interested in purchasing.
More practical examples
Netflix: One of the best examples of AI-powered BI is Netflix. The streaming giant uses machine learning algorithms to personalize the user experience. By analyzing viewing patterns, Netflix can recommend new shows or movies a user will likely enjoy. This not only keeps users engaged but also helps Netflix retain subscribers.
General Electric (GE): It uses AI-powered BI to improve its maintenance processes. GE’s Predix platform uses machine learning algorithms to analyze data from sensors on industrial equipment. By analyzing such data, Predix can detect anomalies and predict when equipment will likely fail. This allows GE to schedule maintenance before a breakdown occurs, reducing downtime and saving money.
UPS: It uses AI-powered BI to optimize delivery routes. The company’s Orion (On-Road Integrated Optimization and Navigation) platform uses machine learning algorithms to analyze data from thousands of delivery routes. Orion can suggest the most efficient route for each driver, saving time and fuel.
The benefits of AI-powered BI
AI-powered BI has several benefits over traditional BI tools. First and foremost, it can analyze data faster and more accurately, saving businesses time and money. AI-powered BI tools can also analyze unstructured data, such as images or video, which traditional BI tools cannot.
AI-powered BI can also provide more accurate predictions and insights. By analyzing vast amounts of data, AI algorithms can identify patterns humans might miss. This can help businesses make better-informed decisions and stay ahead of the competition.
Finally, AI-powered BI can reduce the risk of human error. Humans can make mistakes when analyzing data, such as misinterpreting results or overlooking important information. AI algorithms, on the other hand, are programmed to examine data objectively and without bias.
The internet of things and edge AI
Edge AI refers to deploying AI algorithms and models directly on edge devices, such as smartphones, sensors and other Internet of Things (IoT) devices, instead of sending data to a centralized server for processing. This allows for faster and more efficient data processing and improved privacy and security by keeping sensitive data local to the device.
Edge AI is becoming increasingly popular in applications such as smart homes, autonomous vehicles and industrial automation, where real-time analysis of data is critical.
One practical example of a company using edge AI is Tesla. Tesla uses edge AI to power its Autopilot feature, which enables its vehicles to perform advanced driver assistance functions, such as lane departure warning, automatic emergency braking and adaptive cruise control.
Tesla on the cutting ‘edge’Tesla’s vehicles are equipped with multiple cameras, radar sensors and ultrasonic sensors, which capture much data in real time and process them in real-time using edge AI algorithms that are built into the onboard computer of the vehicle.
By using edge AI, Tesla can process data quickly and efficiently, without relying on a cloud-based AI system. This ensures that the Autopilot feature is responsive and reliable, even in areas with limited or no internet connectivity.
In addition, by processing data locally on the vehicle, Tesla can protect the privacy of its customers. The data collected by the cameras and sensors are not sent to a central server for processing, which reduces the risk of data breaches or privacy violations.
Overall, edge AI has enabled Tesla to provide advanced driver assistance features that are reliable, responsive and secure. This has helped Tesla to establish itself as a leader in the electric vehicle market and has paved the way for further innovation in the field of automotive AI.
Federated learning and the Mayo Clinics
Another area of development is federated learning, which allows AI algorithms to learn from data across multiple devices without compromising privacy. This is particularly important in industries such as health care, where data privacy is a top concern. Federated learning can help health-care organizations analyze patient data to identify trends and make better-informed decisions, without compromising patient privacy.
One example of a health-care company using AI federated learning is the Mayo Clinic. They are using this technology to improve their diagnostic accuracy for heart disease. By combining data from multiple hospitals, the AI system can learn from a much larger and more diverse patient population, leading to more accurate predictions and improved patient outcomes.
In conclusion, AI is rapidly transforming the way businesses operate and make decisions, providing valuable insights and increasing efficiency. As technology continues to advance, businesses that invest in AI and keep up with its developments are likely to see significant benefits and stay ahead of the competition. INQ
Tom Oliver, a “global management guru” (Bloomberg), is the chair of The Tom Oliver Group, the trusted advisor and counselor to many of the world’s most influential family businesses, medium-sized enterprises, market leaders and global conglomerates. For more information and inquiries: www.TomOliverGroup.com or email [email protected].
