Prospects of universal AI ethics seem slim. However the five design pillars will serve organizations well beyond the social fads and fears. The goal – build controls that will identify biases, show attribution, and enable course correction as needed. Ready to roll out your plans for AI? Do you understand the business model implications? Who will you partner with for AI? Add your comments to the blog or reach me via email: R (at) ConstellationR (dot) com or R (at) SoftwareInsider (dot) org. Please let us know if you need help with your Digital Business transformation efforts. Here's how we can assist: Reprints can be purchased through Constellation Research, Inc. To request official reprints in PDF format, please contact Sales .

Over the past four decades, valiant attempts at personalization have failed due to the lack of relevant and intelligent automation. Moreover, expectations of consumers and prospects have only grown. Currently, the fashionable approach is predictive. Prediction does a great job of using past history to foretell future patterns. If one knows a person always gets a specific type of coffee at the same time every day, that's predictive.

In an age of digital disruption, customers, partners, suppliers, and employees have grown accustomed to massive market choice, a plethora of pricing and policy options, and convenient delivery. The rise in expectations creates an insatiable cycle of satisfaction and disappointment that an omni-channel approach alone cannot deliver. Today, omni- channel plays only a temporary role as organizations must progress forward. Lack of relevance leads to lack of engagement. Contextual relevancy can be correlated to an immediate effect on the top line. Constellation estimates that lack of content relevancy often results in 83 percent lower response rates in the average marketing campaign.

The market need for mass personalization at scale and the technology advances in artificial intelligence (AI) enable brands and enterprises to finally deliver on the promises of digital transformation. As new algorithm-driven intelligence improves, these AI-driven smart services have the capacity to deliver immersive experiences, mass personalization, and value exchange across different modes and cadences. Further, these systems can apply machine learning to improve their capabilities in future interactions. This report will show how AI-driven smart services deliver on the promises of mass personalization at scale, how organizations and brands can design their own AI-driven smart services, and highlight 10 recommendations to accelerate personalization success.