Hiring AI leaders is critical to achieving an organization's AI goals.

[nusratjahan]

The research shows that one of the biggest challenges for most organizations is the lack of mid- and/or senior-level talent for AI at scale. Over half of leaders implementing AI at scale have appointed a Chief AI Officer who can provide development teams with a vision and establish guidelines for prioritizing use cases, ethics, and security, all while harmonizing the use of AI development platforms and tools.

However, there is currently a significant gap between supply and demand in key areas such as machine learning and data visualization. This makes training and continuous development essential to bridge this gap and ensure the necessary skills are retained within the company.

Ethics in interacting with AI plays a critical role in consumer satisfaction and trust.
Despite strong consumer attention and regulations surrounding ethical AI , the report finds that many organizations are not actively addressing issues such as the need for shop a team trained in ethics. The report finds that less than a third of organizations struggling to implement AI at scale report having a detailed understanding of how and why their AI systems achieve a certain outcome. This understanding is especially important for executives, who need to have confidence in their organization's AI systems. At the same time, it's impossible to earn consumer trust if the employees who report to them are the first to lack it in the models or data used by their organizations.

“In light of the recent COVID-19 crisis, organizations are looking to data and AI to improve the resilience of their operations, but there is an even greater need to connect tactical and strategic business objectives with their implementation to achieve scalability ,” said Marco Perovani , TMT & EUCS Director, Capgemini Business Unit Italy . “The research highlights that the most successful organizations are combining efforts to rationalize and modernize their data estate and data governance processes, focusing on introducing new agile tools from partner ecosystems and approaches such as DataOps and MLOps (machine learning ops) to develop and implement AI solutions, building teams with diverse backgrounds, and establishing balanced operating models.”