With deep support from the C-suite and the right mix of skillsets and strategies, enterprises can move to the next stage of AI development. Decades ago, artificial intelligence arrived with huge expectations for significant increases in efficiency and productivity. However, despite billions spent on technology, project after project stalled--mainly because challenges with company strategies, technical hurdles, and cultures kept the potential power of AI unrealized. Over the last decade, enterprises have migrated en masse to online platforms and cloud providers. This evolution has paved the way for computing capabilities to handle much more data while simultaneously generating troves of new data that these systems can now analyze.

Two decades after businesses first started deploying AI solutions, one can argue that they've made little progress in achieving significant gains in efficiency and profitability relative to the hype that drove initial expectations. On the surface, recent data supports AI skeptics. Almost 90% of data science projects never make it to production; only 20% of analytics insights through 2022 will achieve business outcomes; and even companies that have developed an enterprisewide AI strategy are seeing failure rates of up to 50%. But the past 25 years have only been the first phase in the evolution of enterprise AI -- or what we might call Enterprise AI 1.0. That's where many businesses remain today.