In recent times, AI has earned reputation of a superhero that can solve business problems that cannot be addressed by traditional technologies. However, according to an MIT SLOAN and BCG report, on the ground only 1 in 20 companies have been able to extensively utilize AI in their business. The huge gap between the expectation and actual delivery is due to a wide variety of challenges and concerns that enterprises face before a model can be deployed into production.
In this webinar, Ashish Khandelwal from EdgeVerve, an Infosys product subsidiary, and Nate Spiegel from Citizens Bank share their thoughts on the need for a Machine Learning based debt collection strategy and practical solutions to problems around auditability of a Machine Learning models, time to value, complex skill requirements and continuous monitoring of value delivered after the model is successfully deployed.
VP, Process Improvement & Change Execution, Citizens Bank
Director – Product Management, EdgeVerve