Your AI won’t work without Smart Data – 5 Advanced Data Strategies for Enterprise AI Success

Executive Summary
The enterprise AI landscape faces a sobering reality: despite unprecedented investment and enthusiasm, 80% of AI projects fail to achieve production scale. This comprehensive analysis reveals that data quality issues represent the primary barrier to successful AI implementation, with McKinsey research identifying inaccurate AI responses as the leading organizational risk in AI adoption. The stark warning that” just 1-2 bad answers can kill GenAI usage” underscores the critical importance of establishing robust data foundations before deploying AI systems at scale.
Discover:
Industry best practices for scaling AI across support channels Lessons learned from failed contact center AI deployments—and how to avoid them Real-world use cases where AI delivers the highest ROI in customer support Five practical smart data strategies that enable fast, accurate, and context-aware responses How high-quality data gives your support organization a measurable edge in 2025












