AI adoption is accelerating, with one in ten enterprises currently using ten or more AI applications and 75% of businesses expected to shift from piloting to operationalizing AI by 2024. As adoption advances, Data Science and AI/ML teams and business leaders must in turn advance their scope of focus to the broader implications of AI systems: are these systems trustworthy, transparent, and responsible? Are outcomes reliable over time? Is there bias built into models? Are models standing up to regulatory and compliance requirements?
From ethical, regulatory, end-user, and model developer perspectives, there is a tremendous need for explainability methods and the ability to continuously monitor models. An overarching question that arises for this variety of stakeholders is: why did the model make this prediction? This question is of importance to developers in debugging (mis-)predictions, regulators in assessing the robustness and fairness of the model, and end-users in deciding whether they can trust the model.
As we considered these far-reaching implications, we endeavored shine light on the state of AI across industries and stakeholders: What is the state of AI adoption within organizations? What are their top AI challenges and priorities moving into the new year? How are organizations thinking about the ability to explain and monitor their models? And what role will regulatory and compliance requirements play in the decision-making and development of AI within these organizations?
To better understand the answers to these questions, we conducted a survey across a range of organizations from publicly-held corporations to privately-held, emerging tech companies, representing industries including software, consulting, and banking & financial services, among others. The majority of respondents came from Data Science functions, including Data Scientists, Chief Data Scientists, and Heads of Data Science.
In our new market report, we present the results of the survey on the state of AI Explainability and Monitoring and share our take on implications for the future of AI adoption within organizations.
