Explainable AI enables a better adoption of AI by increasing the transparency and trustworthiness of AI solutions and outcomes. Explainable AI also reduces the risks associated with regulatory and reputational accountability for safety and fairness. Increasingly, these solutions are not only showing data scientists the input and the output of a model, but are also explaining the reasons the system selected particular models and the techniques applied by augmented data science and ML. Bias has been a long-standing risk in training AI models. Bias could be based on race, gender, age or location.

Data science and machine learning teams are now starting to be measured on business results rather than production metrics (the number of models produced, or projects started, for example). Consequently, the required disciplined approach brought about by commercial platforms is becoming a required condition to achieving business value and data science team sustainability.

Meaningful artificial intelligence (AI) deployments are just beginning to take place, according to Gartner, Inc. Gartner's 2018 CIO Agenda Survey shows that four percent of CIOs have implemented AI, while a further 46 percent have developed plans to do so. "Despite huge levels of interest in AI technologies, current implementations remain at quite low levels," said Whit Andrews, research vice president and distinguished analyst at Gartner. "However, there is potential for strong growth as CIOs begin piloting AI programs through a combination of buy, build and outsource efforts." As with most emerging or unfamiliar technologies, early adopters are facing many obstacles to the progress of AI in their organizations. Gartner analysts have identified the following four lessons that have emerged from these early AI projects.

IT organizations are adjusting their plans about how and when to implement artificial intelligence and machine learning initiatives. Moving into production is taking longer than IT leaders may have expected. CIOs have identified artificial intelligence and machine learning as the number one way to achieve "game-changing transformation." That's according to Gartner research VP Svetlana Sicular, who provided a perspective on where the industry is right now in terms of implementation, where we are going, and how soon we might be getting there. Sicular offered her take during a session, The Future of Data Science, Machine Learning, and AI, during the recent Gartner Data and Analytics Summit in Orlando, Florida.

Augmented analytics, continuous intelligence and explainable artificial intelligence (AI) are among the top trends in data and analytics technology that have significant disruptive potential over the next three to five years, according to Gartner, Inc. Speaking at the Gartner Data & Analytics Summit in Sydney today, Rita Sallam, research vice president at Gartner, said data and analytics leaders must examine the potential business impact of these trends and adjust business models and operations accordingly, or risk losing competitive advantage to those who do. "The story of data and analytics keeps evolving, from supporting internal decision making to continuous intelligence, information products and appointing chief data officers," she said. "It's critical to gain a deeper understanding of the technology trends fueling that evolving story and prioritize them based on business value." According to Donald Feinberg, vice president and distinguished analyst at Gartner, the very challenge created by digital disruption -- too much data -- has also created an unprecedented opportunity. The vast amount of data, together with increasingly powerful processing capabilities enabled by the cloud, means it is now possible to train and execute algorithms at the large scale necessary to finally realize the full potential of AI. "The size, complexity, distributed nature of data, speed of action and the continuous intelligence required by digital business means that rigid and centralized architectures and tools break down," Mr. Feinberg said.

Meaningful artificial intelligence (AI) deployments are just beginning to take place, according to Gartner, Inc. Gartner's 2018 CIO Agenda Survey shows that four percent of CIOs have implemented AI, while a further 46 percent have developed plans to do so. "Despite huge levels of interest in AI technologies, current implementations remain at quite low levels," said Whit Andrews, research vice president and distinguished analyst at Gartner. "However, there is potential for strong growth as CIOs begin piloting AI programs through a combination of buy, build and outsource efforts." As with most emerging or unfamiliar technologies, early adopters are facing many obstacles to the progress of AI in their organizations. Gartner analysts have identified the following four lessons that have emerged from these early AI projects.

Data and analytics leaders face a dilemma. Without data scientists, venturing into machine learning and data science is difficult. Without any successful pilots, convincing the business to hire data scientists is equally challenging. Enterprises don't have to have a large data science lab in order to take advantage of machine learning. "Many organizations are still in the early phases of their data science journey and struggle to understand what machine learning and data science can do for them," says Cindi Howson, research vice president at Gartner.