Challenges and Best Practices in Corporate AI Governance:Lessons from the Biopharmaceutical Industry

TL;DR

This paper discusses the challenges and best practices in implementing AI governance in the biopharmaceutical industry, based on AstraZeneca's experience, offering practical guidance for organizations.

Contribution

It provides a detailed case study of AstraZeneca's approach to operationalizing AI governance, highlighting common challenges and effective strategies.

Findings

Defining the scope of AI governance is complex.

Harmonizing standards across decentralized units is challenging.

Measuring the impact of governance initiatives is essential.

Abstract

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.