Zhiwei Jiang

Next-generation AI agents are redefining process automation, moving from traditional robotic to agentic automation. It’s truly exciting to see how organizations are unlocking entirely new possibilities to streamline workflows and drive innovation, leading to remarkable gains in efficiency, creativity, and agility. My colleagues Prakul Sharma, AJ M., Patricia Henderson, and Camille Chicklis explore how collective automation and autonomous AI agents are transforming essential business processes in a new Deloitte Insights report [https://deloi.tt/45guxjC]. To illustrate agents in action, they highlight the integration of AI agents with robotic process automation (RPA) technology in invoicing. While RPA excels at automating the routine tasks, it often struggles with missing or unstructured data and exceptions. Adding AI agents into the mix enables smarter, more adaptive automation—capable of managing exceptions like missing vendor details and learning from each new transaction to continuously improve. By adopting these advances and reinventing everyday processes, organizations enhance efficiency and generate value, positioning themselves for whatever comes next!  

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#5DaysofData posts! Day 1: AI-Ready Data Series - The Power of Semantic Consistency As data executives, we're all looking to harness the power of AI, but the journey starts with our data itself. For generative and agentic AI to truly thrive, our data environment needs a strategic overhaul! Today, let's talk about Semantic Consistency and Context. Imagine asking an AI to analyze "customer feedback" when some systems call them "clients," others "users," and still others track "account holders." Without a unified understanding of what a "customer" truly means across all your disparate data sources, your AI will stumble, generate irrelevant insights, or worse, make incorrect decisions. 1) Why this matters: • For Generative AI: It ensures AI understands the true meaning behind your prompts, leading to more accurate, relevant, and valuable content creation. • For Agentic AI: It empowers AI agents to reason effectively, make informed decisions, and take precise actions based on a coherent understanding of your business landscape. 2) The Strategy: Invest in creating a robust semantic layer for your data. This means defining common business terms, establishing clear relationships between data entities, and building a shared understanding across your enterprise. This also means your enterprise has clear data ownership, and a thriving data quality and data governance practice. This isn't just a technical task! It is a critical business imperative that enables your AI to speak the same language as your organization. Ready to dive deeper? Stay tuned for Day 2! #AIDataReady #DataStrategy #GenerativeAI #AgenticAI #DataTransformation #CIOs #CTOs #Executives

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Banks and FSIs want to reap the benefits of deploying AI at scale across their operations but are hindered by poor data quality and metadata inconsistency. To address these challenges, Cloudera and UOB are actively co-developing and implementing a solution framework to improve data quality and metadata governance to make GenAI more accessible, actionable, and trusted across UOB. More details will be shared at Cloudera’s upcoming #EVOLVE25 in Singapore. Register here to secure your spot and join us at the industry’s must-attend data and AI conference! Remus Lim, Senior Vice President for Asia Pacific and Japan and UOB’s Group Chief Data Officer, Richard Lowe marking the start of this collaboration with a firm handshake. Calvin Gan Petter Nguyen Dr. Brindha Jeyaraman Jenita Soetanto Rishiraj Singh Shermin Tan James Bisordi Priyank Patel Romila Mattu Wee Tee Lim Melvin Koh Zuling Kang Victor Ng Santosh Panda Georgie Nagaram Hanni Wong

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One of the great privileges of my role at STAC is getting to partner directly with leading exchanges like SGX Group, ASX, and Japan Exchange Group. These partnerships allow us to do deep research into the most pressing technical challenges in financial market infrastructure. STAC is a unique forum where fiercely competitive financial firms come together to share many of their research breakthroughs for the betterment of the industry. Hosting these events directly at the exchanges is both symbolic and practical for direct technical collaboration. Our current research areas include: - Fairness throughout the tick-to-trade cycle - Quantum particles for time synchronization - Reducing AI inference latency Our AI work is highly focused and problem-driven, and we’re quickly becoming the go-to hub for applied AI in finance, with so many experts from the world’s largest financial firms set to join us at these exchange venues to share insights from the advanced AI projects they’re working on. We are actively researching: - Context engineering best practices - Quality measurement beyond gameable benchmarks - Semantic drift in API-based LLMs The research offers an objective foundation for understanding performance, identifying areas for improvement, and navigating the technical demands of capital markets infrastructure alongside the rapidly evolving use of AI in exchange-based trading. I'm excited to head out to the ASX in Sydney and JPX in Tokyo this September, and look forward to sharing news about additional exchange partnerships very soon! Register here to be part of the action 😊 https://lnkd.in/eXPJf8Sy

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This is what AI 'thinks' of my interview with Prof. Kevin Werbach 😅: "David Hardoon delivers a masterclass in Accountable AI! From Singapore’s FEAT principles to Standard Chartered’s AI innovation, he proves governance is a catalyst for trust and progress in fintech. A must-watch for anyone shaping AI’s future—accountability isn’t a burden, it’s a competitive edge! 🚀" I think AI is being far too kind, or maybe its just AI particularly agreeing with the core theme that one can't outsource accountability in AI to AI 😁 Key Takeaways: Justify, Don’t Blindly Trust: AI decisions must be rigorously interrogated to ensure fairness and transparency. Governance Drives Innovation: Embedding accountability in AI processes boosts resilience and value, not bureaucracy. GenAI’s Dual Edge: Harness its potential to fight scams, but only with robust, human-led oversight. Global Lessons: Singapore’s collaborative AI policies (like Veritas) set a benchmark for balancing regulation and innovation. https://lnkd.in/gUVAyrWx

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