6 Deploying Artificial Intelligence ^Top it be operationalized and which risks can be best mitigated? Do managers even have sufficient AI and data literacy? How do we weave in an effective and robust data strategy that accounts for the necessary legal and ethical issues? This gets tricky very quickly for multinational firms since privacy laws differ from country to country. • leadership vision Organizational leadership that cannot envision and define a transformed end state could not only slow down adoption of AI, but also potentially be a barrier to improving solutions. Given the technology's potential for wide-ranging impact, a lack of vision can lead to amplified failures. Further, the hierarchy of decision-making and information flow can be complex in large orga- nizations, leading to a lack of align- ment on the technology’s potential or organizational capabilities. If risk appetite is unequal among functional leadership, conviction levels toward leveraging AI will also be unequal. • talent gap Designing AI solutions requires bilinguals: those who can speak both tech- nology and business languages. Proliferating data science courses are quickly filling in this talent gap, so the challenge now is helping the data scientists understand business parlance. On the other hand, subject matter experts (SMEs) in business functions don’t necessarily understand how AI can trans- form their businesses. When there is a gap in perception between technology and business functions, managers, instead of championing the technology, could end up posing a barrier to adoption. Managers, instead of championing the technology, could end up posing a barrier to adoption.