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.

More organizations are adopting artificial intelligence (AI). Fourteen percent of global CIOs have already deployed AI and 48% will deploy it in 2019 or by 2020, according to Gartner's 2019 CIO Agenda survey. "While adoption is increasing, some organizations are still questioning the business impact and benefits. Today, we witness three barriers to the adoption of AI," says Brian Manusama, Senior Director Analyst, Gartner.