Digital giants dominate the cloud and ecommerce markets, and part of the reason for their dominance is artificial intelligence and advanced analytics. The good news is that mainstream enterprises can learn from their experiences and employ cutting-edge technologies.
That’s the word from R. “Ray” Wang who provides a roadmap for AI success in his latest book, Everybody Wants to Rule the World. Wang calls the capability needed to move forward “AI Smart Services” that help automate precision decisions. “To fine-tune precision decisions at scale-that is, to develop decision velocity, [data-driven enterprises] must automate the process of turning signal intelligence into a decision or action. And the way to do this is by creating AI smart services-automated processes powered by AI.”
The catch is, of course, “AI smart services are not easy to master,” Wang cautions. “They require more than just great algorithms.” He outlines six requirements for advancing in the AI era:
Computing power. To develop AI smart services, data-driven enterprises “must have access to or own cheap computing power,” Wang says. “The ultimate metric for AI rests in pricing not in terms of computing power, but in terms of potential cost per kilowatt hour. The cheapest rate of computing power may determine the cost structure for AI smart services. The most efficient code for finding signal intelligence will provide a cost advantage for each decision made.”
Time. Time waits for no one, Wang says. “The ability to compress time, or take tasks that would normally take weeks and complete them in minutes, provides [data-driven enterprises] an inherent advantage over their competitors.” However, “AI smart services need more time to identify new patterns. That’s why early adopters who train their AI smart services to process the massive petabytes of data coming in to them gain an advantage. The earlier and the quicker the AI smart services learns, the more precision they put back into their algorithms.”
Math talent. Algorithms are only as good as the math talent behind it, Wang says. “Success requires hiring digital artisans — those who can balance right brain and left brain expertise. Digital giants typically have armies of data scientists and a brain trust on hand to fine tune AI smart services for their data-driven enterprises.”
Vertical specific expertise. To make precision decisions, AI smart services “must understand nuances of the various verticals in which they operate — such as size of company, industries, and cultural regions.”
Natural user interfaces and user experiences. Data-driven enterprises must develop AI smart services “that engage users in a variety of human computer interfaces that mimic human interaction in terms of their sensory, visualization, voice, and gesture
capabilities. The interfaces might range from chat bots to virtual assistants, and from augmented reality to brain wave mind readers and computer vision.”
Contextually relevant recommendations. “Once users are confident about how the system arrives at a recommendation, these AI driven smart services start automating decisions-augmenting humanity, accelerating decision making, and ultimately providing filters that deliver situational awareness (the ability to perceive one’s surroundings, events in a timeline, and the potential future state.”
