How AI Can Boost Productivity
Despite the circumstances of the past year, the artificial intelligence market is expected to keep growing. The latest figures show a five-year compound annual growth rate (CAGR) of 17.1%, meaning global revenues will exceed $300 billion in 2024.
There are many theories and speculations about how our world will look after fully embracing artificial technology. There will always be the contingency that claims a robotic uprising will outsource all our jobs. Others see artificial intelligence as a way to enable humans to be more productive.
Robotics and automation are task-focused, not job-focused. They are meant to perform one or more tasks with maximum efficiency. However, humans and machines will be more like a team. A human will still carry out jobs with a high degree of complexity since it wil be too complicated for full automation.
Storage and data analysis is where AI really shines. By removing the burden of storage off-site, cloud computing has freed up businesses to handle big data. Machine learning is now able to process and understand this data and seek out patterns. This is done at speeds unfathomable to a human. In addition, machines can make predictions based on the data to determine consumer’s possible behavior. Using this learned information, AI will create more accurate demand forecasting based on multiple computations and simulations of possibilities, enabling suppliers to produce a more precise amount of product. Unwanted inventory will be reduced by 20-50% as a result.
Taking physical inventory will also be sped up and more accurate with the assistance of artificial intelligence. In addition to counting, overhead drones and sensors can scan items and check for misplaced goods. This is a task that would take humans multiple days to accomplish.
One of the most significant expenditures is factory repair. AI-based predictive maintenance can replace the cost of excessive preventative maintenance, as well as reactive maintenance, which may be too late. Using sensors, machines can use data analysis to detect anomalies before the problem escalates, and some machines can perform routine self-diagnoses. Companies have reported a 10% reduction in annual maintenance costs, a 25% reduction in inspection costs, and a 20% downtime reduction.
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