2. New capabilities and business model expansion-
deploy data and analytics into the enterprise, it opens up new opportunities for businesses to participate in different areas. For example, autonomous vehicle companies, with the reams of data they’re collecting, could identify new revenue streams related to insurance, while an insurance company could apply AI to its vast data stores to get into fleet management.

3. Improved monitoring-
AI’s capacity to take in and process massive amounts of data in real-time means organizations can implement near-instantaneous monitoring capabilities that can alert them to issues, recommend action, and, in some cases, even initiate a response.
For example, AI can take the information gathered by devices on factory equipment to identify problems in those machines as well as predict what maintenance will be needed when thereby preventing costly and disruptive breakdowns, as well as the cost of maintenance work, performed because it’s scheduled rather than because it’s needed.

4. Better quality and reduction of human error-
Organizations can expect a reduction of errors as well as stronger adherence to established standards when they add AI technologies to processes. When AI and machine learning are integrated with technology like RPA, which automates repetitive, rules-based tasks, the combination not only speeds up processes and reduces errors but can also be trained to improve upon itself and take on broader tasks.

5. Minimize operational costs-
Errors can not only postpone the release date of your product, for instance — they can also cost your company a lot. However, you can use AI to minimize the number of errors and improve the efficiency of your company. Here is an example for you.
Most of the company solution is allows plant managers to make better use of the sensor data they already have.
Predict asset failures with enough lead time to take corrective actions before the equipment has a chance to break down while leaving assets that do not need maintenance to continue running.

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