AI in Action Beyond Experimentation to Transform Industry 2025

3. Physical and digital infrastructure Technologies like 5G, cloud computing (remote data processing) and edge computing (local data processing) allow organizations to handle data efficiently while supporting genAI. Since most organizational data currently resides outside the cloud, transferring it to cloud-based AI systems can be costly and impractical. Deploying AI models directly where the data exists – known as edge AI – enables organizations to use their existing infrastructure, reducing expenses and improving efficiency. This approach lowers latency, strengthens data security, and meets compliance needs, making it particularly valuable in sectors like finance and healthcare.55 AI impact programme (The Frontier MINDS) BOX 1 The Forum’s AI Governance Alliance, in collaboration with Accenture, aims to collect and recognize innovative AI use cases that inspire community engagement and facilitate “combinatorial” thinking around high-value AI applications. As part of this effort, the Forum will use the aforementioned criteria to assess promising AI applications. As genAI becomes more widespread, addressing its risks is crucial for responsible and successful adoption. Key challenges include ensuring genAI’s security and reliability to prevent issues like data breaches, privacy violations and unauthorized model usage. Accountability and oversight are also essential to maintaining transparency and managing complex responsibilities within AI ecosystems.For the ethical use of AI, for example, mitigating bias and discrimination is necessary to avoid amplifying inequalities. Meanwhile, managing risks like misinformation, environmental impact and job displacement are key to building societal trust. It is critical for organizations to steer the technology towards applications that contribute to positive change for society at large. Framework for transformational and responsible AI adoption To make the most of the recent surge in AI investments, it is critical for organizations to steer the technology towards applications that look beyond narrow productivity improvements and contribute to positive change for society at large, as well as for the bottom line. To help guide these  efforts, the community of the AI Transformation of Industries initiative established the following imperatives that AI applications should follow: –Impact: Measures how effectively AI applications contribute to a company’s core objectives like profitability, efficiency and market competitiveness. Key indicators include quantifiable benefits such as revenue growth, cost reduction, risk mitigation, market expansion and customer satisfaction. “Impact” also evaluates the positive social impact of AI use cases, such as advancements in healthcare, education, sustainability and employment. –Novelty: Focuses on how AI applications offer innovative ways to tackle persistent challenges. “Novelty” considers the use of new ecosystems, tools and/or methodologies that provide fresh, effective solutions. –Scalability: Assesses the adaptability and resilience of AI solutions, ensuring they can expand to different regions or industries without losing performance quality. “Scalability” includes evaluating the technology’s reliability, as well as its ability to handle increased workloads when demand grows. –Responsible AI: Evaluates AI design and operational practices to ensure alignment with ethical AI standards, such as the Forum’s Digital Trust Framework (which emphasizes principles like security, accountability, oversight, inclusivity, ethics and reliability). AI Governance Alliance: Transformation of Industries in the Age of AI 20