In an attempt to tackle the UN SDGs, AI for Social Impact (AI4SI) projects focus on harnessing AI to address societal issues in areas such as healthcare, social justice, etc. Unfortunately, despite growing interest in AI4SI, achieving tangible, on-the-ground impact remains a significant challenge. For example, identifying and engaging motivated collaborators who are willing to co-design and deploy AI based solutions in real-world settings is often difficult. Even when such partnerships are established, many AI4SI projects "fail" to progress beyond the proof-of-concept stage, and hence, are unable to transition to at-scale production-level solutions. Furthermore, the unique challenges faced by AI4SI researchers are not always fully recognized within the broader AI community, where such work is sometimes viewed as primarily applied and not aligning with the traditional criteria for novelty emphasized in core AI venues. This paper attempts to shine a light on the diverse challenges faced in AI4SI research by diagnosing a multitude of factors that prevent AI4SI partnerships from achieving real-world impact on the ground. Drawing on semi-structured interviews with six leading AI4SI researchers - complemented by the authors' own lived experiences in conducting AI4SI research - this paper attempts to understand the day-to-day difficulties faced in developing and deploying socially impactful AI solutions. Through thematic analysis, we identify structural and organizational, communication, collaboration, and operational challenges as key barriers to deployment. While there are no easy fixes, we synthesize best practices and actionable strategies drawn from these interviews and our own work in this space. In doing so, we hope this paper serves as a practical reference guide for AI4SI researchers and partner organizations seeking to engage more effectively in socially impactful AI collaborations.

AI continues to transform industries across the globe, and business decision makers of all kinds are taking notice. But there's a problem: although 80% of today's enterprises recognize that AI is critical to their future, only 14% succeed in harnessing it (source). In other words, a gap remains between the potential of AI and its ease of deployment. At Google Cloud AI, closing this gap is perhaps my most important responsibility. After years of breakthroughs, AI has stabilized with the emergence of sophisticated tools, best practices, and a rapidly growing community of builders.

The evolution of AI has been a rich tale of exploration since its origins in the 1950's, with the last decade providing an especially dramatic chapter of breakthrough innovations. But I believe the real story is what comes next -- when the disruption stabilizes and machine learning transitions from a staple of Silicon Valley headlines to an everyday technology. It'll be a far longer chapter -- perhaps decades -- in which developers all over the world use a mature set of tools to transform their industries. In 2019, we find ourselves at the start of this new chapter. AI has undergone a remarkable refinement in recent years, as barriers to entry have fallen and a wide range of products, services, resources, and best practices have emerged. As our focus shifts -- finally -- from AI itself to the impact that AI can have on your business, the question is no longer how this technology works, but what it can do for you.