Samuel Thompson

Amul is deploying AI, personified by virtual assistant Sarlaben, to support millions of Indian dairy farmers. Leveraging vast historical data, the platform offers personalized, multilingual guidance via a mobile app and voice calls. This initiative aims to boost milk yield and farmer income, addressing the productivity paradox in India’s large dairy sector. It represents a significant step in bringing AI benefits to rural communities.

Physical AI development is fragmented. While giants like OpenAI focus on large models, Hitachi and Siemens champion domain expertise. Hitachi’s approach emphasizes foundational understanding of physics and industrial equipment, citing projects with Daikin and JR East as proof of concept. Their R&D also targets accelerating software development and ensuring safety through integrated design. Hitachi Vantara is also leveraging NVIDIA hardware for advanced digital twins, aiming to create robust physical AI systems.

APAC’s retail sector is rapidly integrating AI into daily operations, driven by urban density and competition. Consumers show strong interest in AI recommendations. Computer vision and machine learning are automating stores, like Japan’s cashier-less Lawson Go and South Korea’s Fainders.AI MicroStore. AI optimizes inventory and reduces waste through systems like Coop Sapporo’s Sora-cam, improving promotion efficiency. Agentic AI personalizes shopping by handling complex requests, planning meals, and managing shopping carts, aligning with APAC’s home-cooking culture. Key challenges include data consent, accuracy, and localization.

Executives express cautious optimism about AI’s future, anticipating its transformative impact on markets and business functions. They see AI driving efficiency, innovation, customer experience, and decision-making. However, concerns about talent gaps, data quality, ethics, integration complexity, and regulations temper this optimism. Strategic, ethical, and pragmatic adoption is key to unlocking AI’s value.

Coca-Cola is shifting its marketing strategy from price hikes to AI-driven persuasion to cultivate consumer demand. The company is integrating generative AI for creative development, optimizing campaigns, and streamlining content creation across digital platforms. This evolution moves AI beyond efficiency to fundamentally influence brand engagement and sales, signaling a broader industry trend towards automated marketing pipelines and data-informed strategies.

AI is transforming enterprise treasury management, moving businesses from error-prone spreadsheets to automated data pipelines. Experts highlight the need for digitized, real-time data as a foundation for AI implementation. Integrating treasury management systems with ERP platforms and trading systems is crucial for accurate insights, enabling better liquidity management, risk mitigation, and compliance. This modernization is essential for navigating market volatility and building financial resilience.

DBS Bank and Visa are piloting “Visa Intelligent Commerce,” enabling AI agents to initiate and complete purchases on behalf of customers. This “agent-driven commerce” shifts transactions from human to AI execution, with banks retaining control through tokenization and approval workflows. The pilot focuses on routine purchases, aiming to integrate AI more deeply into financial operations while addressing security and trust concerns.

Financial sector leaders are moving beyond AI experimentation to focus on operational integration for 2026. The shift is towards system-wide AI agents that manage processes within strict governance, requiring architectural and cultural adjustments. Key challenges involve coordinating legacy systems, compliance, and data silos to enable “agents” that run processes, not just assist. This necessitates a “Moments Engine” for signals, decisions, messaging, routing, and action, with governance as a foundational, hard-coded feature. Data architecture must enable restraint in personalization, and generative search optimization is crucial for off-site brand visibility. Agility will be achieved through structured, secure experimentation, paving the way for agent-to-agent interactions.

Integrating AI is a strategic organizational shift, not just a tech upgrade. A six-area framework guides planning and assessment, emphasizing data preparation as foundational. Success requires redesigning workflows, managing legacy systems with AI’s help, and converging physical and digital operations. Robust governance, including risk assessment and security, is vital. Sustainable AI success depends on leadership alignment, investment, and a realistic view of capabilities, addressing all aspects holistically.

SS&C Blue Prism is guiding clients from RPA to agentic AI, a necessary evolution for complex workflows. Traditional RPA struggled with unstructured data, while agentic AI, leveraging LLMs, can reason and adapt in real-time. SS&C Blue Prism focuses on an outcome-oriented approach, setting goals rather than dictating steps. While fully autonomous AI is still developing due to trust and regulatory concerns, SS&C Blue Prism is introducing new technology to embed AI agents into existing workflows, aiming to unlock significant further automation potential.