A new AI maturity audit framework for industrial enterprises uses a 30-question system to replace subjective self-reporting with evidence-based performance metrics.
Month-over-month growth reports are often misleading due to calendar inconsistencies, requiring analysts to normalize data and account for seasonality to avoid false conclusions.
Product managers often fail when implementing AI by ignoring user needs, operational costs, and data quality, leading to ineffective features and budget overruns.
TheCUBE Research will host a summit on September 11 to address how enterprises can measure the business value and return on investment of AI in contact center operations.
