Predictive analytics, optimisation, enhanced capabilities
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Business workflows commonly suffer from ineffective or inappropriate, non-optimal, and reactive utilisation of business resources.
Due to inconsistent and partially unpredictable performance of human operators, business processes suffer, as common human error introduces issues with the quality of service, in addition to being an unreliable production resource and potentially ineffective in comparison to algorithmic approaches to task performance.
Furthermore, there is generally a reactive management or operational mechanism in place, where problems are dealt with as they occur, potentially ignoring signals that would have indicated imminent issues or process breakdown.
Solution and benefits
When incorporating automated information processing, businesses can take advantage of cost-effective, fast, and highly consistent quality of delivery on product or service. By doing so a firm can optimize the reliability and accuracy of service. Through automated information processing, the workforce is partially relieved from repetitive work, thereby decreasing cost for the firm. In addition, such resource-optimized delivery through automated information processing is faster, more reliable, and more consistent.
In addition, when dealing proactively with impending business process breakdown by detecting the appropriate signals, delivery can be kept on target and without interruption of service. Not just detection of known signals, but also new signals can be discovered through machine learning, where detection automatically becomes a learned part of the solution. By dealing proactively with impending issues, resource allocation is optimized, by ensuring that support processes are performed under circumstances of the organisation’s choosing: When and where is convenient rather than under duress. This implies that business process maintenance is performed more cost-effectively and accomplishing higher service quality through decreased downtime and service issues.