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Agentic AI in finance speeds up operational automation

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In finance, achieving operational automation by integrating agentic AI requires a data-centric foundation to drive real value. Financial infrastructure provider SEI has engaged IBM to modernise its internal operations via AI and automation. The joint initiative focuses on process redesign and targeted system updates to deliver consistent client experiences, building a modern and data-enabled foundation […]

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Scaling intelligent automation without breaking live workflows

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Scaling intelligent automation without disruption demands a focus on architectural elasticity, not just deploying more bots. At the Intelligent Automation Conference, industry leaders gathered to dissect why many automation initiatives stall after pilot phases. Speaking alongside representatives from NatWest Group, Air Liquide, and AXA XL, Promise Akwaowo, Process Automation Analyst at Royal Mail, grounded the

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Goldman Sachs and Deutsche Bank test agentic AI for trade surveillance

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Banks are testing a new type of artificial intelligence, like agentic AI, that does more than scan for keywords or follow preset rules. Instead of relying only on static alerts, some trading desks are beginning to use systems designed to reason through patterns in real time and flag conduct that may need human review. Bloomberg

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Planning for AI Induced Economic Volatility

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Enterprise deployments of large language models and agentic workflows are shifting from experimental pilots to core infrastructure. In 2025, enterprises piloted AI. In 2026, they are going to production and, in 2027, they will scale.  As organizations go to production, the company focus is on operational efficiency and infrastructure cost optimization. However, enterprise leaders must

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Nithin Mohan — Why AI Breakthroughs Depend on Supercomputing Discipline

ai, AI (Artificial Intelligence), AI Infrastructure, Artificial Intelligence, Enterprise AI, Executive Interviews, Featured, governance, High Performance Computing

Executive Summary. As enterprises race to adopt AI, HPE leader Nithin Mohan explains why infrastructure, not algorithms, is becoming the real constraint. He outlines how exascale computing, agentic system reliability, and distributed AI operations are redefining what it takes to move from impressive demos to economically viable production systems. As generative AI captures boardroom attention,

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Anthropic: Claude faces ‘industrial-scale’ AI model distillation

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Anthropic has detailed three “industrial-scale” AI model distillation campaigns by overseas labs designed to extract abilities from Claude. These competitors generated over 16 million exchanges using approximately 24,000 deceptive accounts. Their goal was to acquire proprietary logic to improve their competing platforms. The extraction technique, known as distillation, involves training a weaker system on the

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How disconnected clouds improve AI data governance

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Disconnected clouds aim to improve AI data governance as businesses rethink their infrastructure under tighter regulatory expectations. Ensuring operational continuity in isolated environments has become increasingly vital for businesses. Facilities lacking continuous internet access face unique constraints where external dependencies become unacceptable. Microsoft recently expanded its capabilities to allow regulated industries and public sectors to

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Deploying agentic finance AI for immediate business ROI

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Agentic finance AI improves business efficiency and ROI only when deployed with strict governance and clear return on investment targets. A recent FT Longitude survey of 200 finance leaders across the US, UK, France, and Germany showed 61 percent have deployed AI agents merely as experiments. Meanwhile, one in four executives admit they do not

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Jim Wetekamp — Connected Risk Intelligence for the AI Enterprise

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Executive Summary. As risk becomes faster and more interconnected, traditional periodic review models are breaking down. In this conversation, Riskonnect CEO Jim Wetekamp explains why enterprise risk management is emerging as a key proving ground for AI, and how integrated data, agent-based workflows, and governance-first design are shifting organizations from retrospective reporting to continuous risk

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How financial institutions are embedding AI decision-making

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For leaders in the financial sector, the experimental phase of generative AI has concluded and the focus for 2026 is operational integration. While early adoption centred on content generation and efficiency in isolated workflows, the current requirement is to industrialise these capabilities. The objective is to create systems where AI agents do not merely assist

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