  While the use of artificial intelligence (AI) systems promises to bring
significant economic and social benefits, it is also coupled with ethical,
legal, and technical challenges. Business leaders thus face the question of how
to best reap the benefits of automation whilst managing the associated risks.
As a first step, many companies have committed themselves to various sets of
ethics principles aimed at guiding the design and use of AI systems. So far so
good. But how can well-intentioned ethical principles be translated into
effective practice? And what challenges await companies that attempt to
operationalize AI governance? In this article, we address these questions by
drawing on our first-hand experience of shaping and driving the roll-out of AI
governance within AstraZeneca, a biopharmaceutical company. The examples we
discuss highlight challenges that any organization attempting to operationalize
AI governance will have to face. These include questions concerning how to
define the material scope of AI governance, how to harmonize standards across
decentralized organizations, and how to measure the impact of specific AI
governance initiatives. By showcasing how AstraZeneca managed these operational
questions, we hope to provide project managers, CIOs, AI practitioners, and
data privacy officers responsible for designing and implementing AI governance
frameworks within other organizations with generalizable best practices. In
essence, companies seeking to operationalize AI governance are encouraged to
build on existing policies and governance structures, use pragmatic and
action-oriented terminology, focus on risk management in development and
procurement, and empower employees through continuous education and change
management.
