As executives, who are already undergoing digital transformation deal with pandemic-related recovery problems, they rely on established and creative technology to remain on track
Most leaders see artificial intelligence as critical and define a “top-down sense of urgency” to incorporate it. Nonetheless, they are having difficulty integrating company-wide AI initiatives. Seventy-five percent of executives polled feel that if they do not, their businesses will collapse within five years.
AI in business will most likely rise steadily over the next five years, then skyrocket. It is projected that by 2030, the majority of businesses will use AI to help and accelerate upper-level guidance and decision-making. The majority of use cases would include a powerful form of AI known as machine learning, in which computers evaluate massive datasets to help answer questions. However, AI alone is insufficient. Executives are already recognizing that machine learning systems need real-world background to link AI with the physical world, and location intelligence serves as that bridge (LI).
Machine learning algorithms identify clusters and hotspots in large datasets. When applied to consumer data, AI and LI will reveal patterns and trends that help companies better understand their markets.
The question of where to locate a store, for example, necessitates determining how accessible it is from different sections of the city. In the meantime, demographic data will reveal hotspots of specific customer activity. Retailers will better understand which consumers would prefer those sites by evaluating all datasets—potential site reachability and nearby demographics.
People have always been able to answer questions easily using GIS technology, showing reachability and demographics on maps and dashboards. They can now incorporate AI workflows by using data sources such as customer versatility and purchasing trends.
This exposes previously unknown customer patterns and provides answers to critical questions. What would be the average age of customers? How many people will bring their families? What are the patterns of mobility that influence store visit patterns? How many people will use public transportation? Can a store stay open later on those days?
As businesses increasingly tailor goods and services to particular geographic areas, this type of data assists in anticipating and meeting consumer needs. Machine learning algorithms may perform advanced analytics on sales data, often in real-time, to recognize trends and relate those patterns to location. Software, for example, could assist a business in identifying commonalities among regional populations such as urban versus suburban or areas with young families—spotting trends that would otherwise go unnoticed.