Four Ways AI And Location Intelligence Are Guiding The Future Of Business
Businesses are working through digital transformation and are handling pandemic-related recovery issues, they’re dependent on technologies—both proven and innovative—to stay on track.
Most entrepreneurs see artificial intelligence and location intelligence as crucial and describe a “sense of urgency at the top” to implement it. Yet, it is not easy for them to integrate company-wide AI initiatives.
In the simplest manner, Location intelligence is a practice of collecting and analyzing various sources of geospatial data from large datasets – by its GPS data, footfall traffic, transactional data, or sociodemographic data – and transform these into strategic insights that businesses can use to address critical challenges. Machine learning manages complex data while location intelligence gives that data the crucial context of where. Here Data Sutram a location intelligence startup highlights examples of how AI And Location Intelligence Are Guiding The Future Of Business
1. Market Analysis, Growth Planning, Advanced Analytics
Machine learning programs find clusters and hotspots in complex datasets. Applying that capability to customer data, AI and LI can unlock patterns and trends that help businesses understand their markets. The question of where to site a store, for example, involves a determination of how reachable it is from various parts of the community.
2. Monitoring and Tracking Assets
Machine learning algorithms can be taught to recognize objects and to sort them accordingly. When there is a location component—as there is with most objects—this capability can pay enormous dividends in time and money, especially in an age of drones and satellite imagery.
This concept works for even larger geographic areas. The operator of an oil pipeline uses deep learning to detect any structures built too close to it.
3. Risk Management and Operational Efficiency
It’s important to remember that sophisticated non-AI prediction tools have existed for some time. The crucial difference is that AI can combine many more variables to make accurate predictions. It’s the difference between forecasting tomorrow’s weather and modeling the complex effects of climate change.
4. Putting It All Together
By using location intelligence with AI machine learning, businesses get the benefit of pattern recognition, classification, and prediction. By rooting decisions in an approach that prioritized location intelligence, the company was able to contextualize them, to visualize how they would play out in the real world. The combination of AI and location intelligence brought these decisions to life.
