Global Big Data Conference

AI teams not only need to have cutting-edge skillsets to build, test and refine AI models and applications, but they also need to step up as transformational leaders, a new study finds. Artificial intelligence and machine learning have come a long way in recent years, with solid business cases, powerful algorithms, vast compute resources, and rich data sets now the norm for many enterprises. However, AI managers and specialists are still grappling with seemingly insurmountable organizational and ethical issues that are hamstringing their efforts, or even sending things down the wrong path. That's the conclusion of a recent in-depth analysis that looked at the pressures and compromises faced by today's AI teams. The researchers, Bogdana Rakova (Accenture and Partnership on AI), Jingying Yang, (Partnership on AI), Henriette Cramer (Spotify) and Rumman Chowdhury (Accenture), found that most commonly, "practitioners have to grapple with lack of accountability, ill-informed performance trade-offs and misalignment of incentives within decision-making structures that are only reactive to external pressure."