Future ambitions of AI impact In the short term, organizations have primarily focused on implementing AI proof of concepts to evaluate the technology, demonstrate feasibility and identify future use cases. However, as AI investments continue to grow, it is crucial to look beyond these short-term gains and explore applications of AI that could drive transformative change for both businesses and society, addressing some of the world’s most pressing challenges. While the trends mentioned are expected to deliver value in the coming years, predicting the exact timeline remains challenging. However, organizations that take an enterprise-wide approach and pursue broader AI ambitions will be better positioned to harness genAI effectively, leading the way in innovation and progress. The following are examples of AI’s potential future impact:1 Speeding up drug discovery and disease detection in healthcare. As technology advances, AI is set to accelerate both drug discovery and the early detection of diseases like cancer,59 potentially enabling breakthroughs that could address some of the world’s most pressing health challenges. AI-driven simulations could eventually predict treatment outcomes, optimize therapies and identify early indicators of diseases – paving the way for more proactive healthcare. These potential advances also hold promise for creating personalized healthcare solutions tailored to each patient’s unique needs. To make these goals a reality, there are, of course, various challenges to overcome, including data-sharing restrictions, biases, privacy concerns and transparency issues. Additionally, the impact of AI on the patient- doctor relationship is an important area to monitor as AI tools become more integrated into healthcare settings.60 Early cancer detection with synthetic biopsy: Earli is pioneering in oncology by differentiating between healthy and cancerous cells using programmable genetic constructs. Their method, termed “synthetic biopsy”, reacts to the presence of active cancer cells at an early stage, facilitating rapid development of personalized treatments by pharmaceutical and biotech firms, potentially reducing both time and costs associated with cancer care.61 Human and AI collaboration: Apollo Hospitals, in collaboration with a tech partner, has harnessed AI to revolutionize cardiovascular risk assessment. By mining data from over 500,000 patient records, this tool not only accounts for genetic variances unique to Indian populations but also achieves nearly 90% accuracy in predicting 10-year cardiovascular risk, aiding clinicians in preventive care strategies.62 Sophisticated healthcare delivery: CVS Health has implemented a data intelligence platform that uses AI to refine customer interactions across its network of over 10,000 stores. By analysing customer behaviour, CVS optimizes the timing and channels for prescription reminders, which has led to a significant improvement in medication adherence by 1.6%, ultimately enhancing patient health outcomes and reducing healthcare expenditures.63CASE STUDY 15 AI transforming healthcare 2 Expanding access to tailored education content: AI can help make personalized learning experiences widely accessible, creating customized content that meets the diverse needs of students, employees and lifelong learners. This means enabling tailored learning paths and resources for every student, supporting inclusive learning environments and improving outcomes across age groups and abilities. For industry professionals, AI-driven training and upskilling programmes can adapt in real time to individual performance, continuously improving to maximize learning impact. This approach also aligns with the concept of a “two-sigma shift” in education outcomes, where each student or employee benefits from a personal (AI) tutor – potentially transforming classrooms, workplace training and much more.64 3 Predicting and responding to climate disasters: By analysing massive amounts of data quickly and accurately, the latest AI models could greatly improve disaster preparedness and resource planning. This progress would significantly impact three areas: 1) reducing emissions, 2) building adaptation and resilience to climate impact, and 3) advancing climate modelling, economics, education and related research. Together, this comprehensive approach would help to better manage and respond to climate challenges.65 AI Governance Alliance: Transformation of Industries in the Age of AI 22

AI in Action Beyond Experimentation to Transform Industry 2025