The rush to exponential technologies in new business models has placed artificial intelligence (AI) in the forefront of boardroom priorities for 2018. This report provides a framework on how to realize the path to full AI, design for infinite ambient orchestration, build any AI-driven smart service and identify when to automate with AI. Scientists have long aspired to recreate humans in machine form. While early attempts focused on the physical aspects of humanity, today's computer scientists have aspired to recreate the intelligence aspects in computer form. From vision to hearing, speech, touch and smell, scientists have come closer to replicating our five senses in the pursuit of artificial intelligence (AI).

The combination of machine learning, deep learning, natural language processing, and cognitive computing will soon change the ways that we interact with our environments. AI-driven smart services will sense what we're doing, know what our preferences are from our past behavior, and subtly guide us through our daily lives in ways that will feel truly seamless. Perhaps the best way to explore how such systems might work is by looking at an example: a visit to a hospital. You walk into the hospital and its AI-driven smart service searches to see the patient you may be there to see. The system uses facial recognition and matches you with the photos and the family's list of approved visitors. At the reception kiosk, the system notices you came empty-handed and that you are out of breath.

CXOs: Participate in Constellation's digital transformation survey by Aug.18, 2017 and receive a summary of the results. AI has become one of the great, meaningless buzzwords of our time. In this video, the Chief Data Scientist of Dun and Bradstreet explains AI in clear business terms. The combination of machine learning, deep learning, natural language processing, and cognitive computing will change the ways that humans and machines interact. AI-driven smart services will sense one's surroundings, learn one's preferences are from past behavior, and subtly guide people and machines through their daily lives in ways that will truly feel frictionless.

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.

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.

The combination of machine learning, deep learning, natural language processing, and cognitive computing will change the ways that humans and machines interact with our environments. AI-driven smart services will sense one's surroundings, know one's preferences are from past behavior, and subtly guide people and machines through their daily lives in ways that will truly feel seamless. This quest to deliver AI driven smart services across all industries and business processes will usher the most significant shift in computing and business this decade and beyond. Organizations can expect AI driven smart services to impact future of work flows, IOT services, customer experience journeys, and block chain distributed ledgers. Success requires the establishment of AI outcomes (see Figure 1).