Leading organizations expect to double the number of artificial intelligence (AI) projects in place within the next year, and over 40% of them plan to actually deploy AI solutions by the end of 2020, according to the Gartner 2020 CIO Agenda Survey. But the reality is that most organizations struggle to scale the AI pilots into enterprise wide production, which limits the ability to realize AI's potential business value. Launching pilots is deceptively easy but deploying them into production is notoriously challenging. Although the potential for success is enormous, delivering business impact from AI initiatives takes much longer than anticipated. IT leaders responsible for AI are discovering "AI pilot paradox,"' where launching pilots is deceptively easy but deploying them into production is notoriously challenging.

If you've noticed an uptick in product recommendations based on your Amazon purchases, or GPS services that are increasingly accurate in displaying congested traffic areas, it's because artificial intelligence (AI) is everywhere. AI adoption in organizations has tripled in the past year, and AI is a top priority for CIOs. Yet early AI initiatives have a high probability of failure due to misalignment with business requirements and lack of agility. "Although the potential for success is enormous, delivering business impact from AI initiatives takes much longer than anticipated," says Chirag Dekate, senior director analyst at Gartner. "IT leaders should plan early and use agile techniques to increase relevance and success rates."

If you've noticed an uptick in product recommendations based on your Amazon purchases, or GPS services that are increasingly accurate in displaying congested traffic areas, it's because artificial intelligence (AI) is everywhere. AI adoption in organizations has tripled in the past year, and AI is a top priority for CIOs. Yet early AI initiatives have a high probability of failure due to misalignment with business requirements and lack of agility. "Although the potential for success is enormous, delivering business impact from AI initiatives takes much longer than anticipated," says Chirag Dekate, senior director analyst at Gartner. "IT leaders should plan early and use agile techniques to increase relevance and success rates."

Here's a scene from our digital future: You sit down to dinner at a restaurant where your server was selected by a "robo-boss" based on an optimized match of personality and interaction profile, and the angle at which he presents your plate, or how quickly he smiles can be evaluated for further review. Or, perhaps you walk into a store to try on clothes and ask the digital customer assistant embedded in the mirror to recommend an outfit in your size, in stock and on sale. Afterwards, you simply tell it to bill you from your mobile and skip the checkout line. These scenarios describe two predictions in what will be an algorithmic and smart machine driven world where people and machines must define harmonious relationships. In his session at Gartner Symposium/ITxpo 2016 in Orlando, Daryl Plummer, vice president, distinguished analyst and Gartner Fellow, discussed how Gartner's Top Predictions begin to separate us from the mere notion of technology adoption and draw us more deeply into issues surrounding what it means to be human in a digital world.