前沿者的AI扩展指南：来自行业领导者的经验教训（英）

The front-runners’ guide to scaling AILessons from industry leadersAuthorsSenthil Ramani Global Lead for Data & AI, AccenturePhilippe RoussiereGlobal Lead for Innovation and AI, Accenture ResearchLan Guan Chief AI Officer, AccentureThe front-runners’ guide to scaling AI: Lessons from industry leaders2About the researchWe surveyed 2,000 C-suite and data-science executives, who lead 1,998 of the world’s largest companies (revenues greater than $1 billion), which are headquartered in 15 countries (Australia, Brazil, Canada, China, Germany, France, India, Italy, Japan, Saudi Arabia, Singapore, Spain, United Arab Emirates, United Kingdom and United States) and operate in nine industries (banking, insurance, energy, consumer goods and services, life sciences, utilities, retail, public services and communications and media). The survey, fielded from June to July 2024, aimed to shed light on how companies develop and deploy AI models to create financial and non-financial value. The survey covered topics such as organizations’ data and AI strategy, data and AI architecture, budgets for—and investments in—strategic bets, talent strategy, ecosystem strategy, responsible AI, AI-related challenges and AI adoption rates.To identify the most important strategic bets (see “Get strategic,” below), we also interviewed numerous C-suite experts within and outside Accenture. In addition, we deployed machine learning to identify both the key capabilities associated with scaling strategic bets and companies’ progress in developing those capabilities. The research was further enriched with insights from our extensive experience helping clients scale AI solutions. By drawing on these diverse inputs, our findings thus capture both strategic perspectives on AI and real-world execution challenges.For the purposes of this report, “scaling AI” refers to the process of expanding AI implementation across an enterprise to achieve broader, more impactful outcomes. Scaling includes integrating AI into diverse business processes and workflows; ensuring widespread adoption across assets and employees; seamlessly integrating AI with existing systems; driving innovation to gain a competitive edge in the market; and otherwise improving key performance metrics. “Generative AI” is used as an umbrella term for artificial intelligence that can produce brand-new output—such as text, images, videos, audio and code.The front-runners’ guide to scaling AI: Lessons from industry leaders3Executive summaryFor businesses, securing a sustained advantage over competitors was long the Holy Grail—a coveted, yet elusive prize. Today, however, generative artificial intelligence and other forms of AI have flipped the script, bringing the previously unattainable within reach. That’s why the world’s largest companies are investing heavily in data and AI.But reinventing the enterprise with generative AI (gen AI) isn’t simply a matter of deploying a few chatbots. Reinvention is about building advanced AI cap