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Strengthening enterprise governance for rising edge AI workloads

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Models like Google Gemma 4 are increasing enterprise AI governance challenges for CISOs as they scramble to secure edge workloads. Security chiefs have built massive digital walls around the cloud; deploying advanced cloud access security brokers and routing every piece of traffic heading to external large language models through monitored corporate gateways. The logic was […]

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IBM: How robust AI governance protects enterprise margins

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To protect enterprise margins, business leaders must invest in robust AI governance to securely manage AI infrastructure. When evaluating enterprise software adoption, a recurring pattern dictates how technology matures across industries. As Rob Thomas, SVP and CCO at IBM, recently outlined, software typically graduates from a standalone product to a platform, and then from a

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China’s Five-Year Plan details the targets for AI deployment

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China has approved its 15th Five-Year Plan [PDF] setting out the country’s economic, education, social, and industrial priorities through to 2030. As might be expected, there is a significant number of references to AI, with the technology mentioned in several contexts. AI is grouped alongside quantum computing, biotechnology, and energy as paths that are to

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KPMG: Inside the AI agent playbook driving enterprise margin gains

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Global AI investment is accelerating, yet KPMG data shows the gap between enterprise AI spend and measurable business value is widening fast. The headline figure from KPMG’s first quarterly Global AI Pulse survey is blunt: despite global organisations planning to spend a weighted average of $186 million on AI over the next 12 months, only

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SAP and ANYbotics drive industrial adoption of physical AI

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Heavy industry relies on people to inspect hazardous, dirty facilities. It’s expensive, and putting humans in these zones carries obvious safety risks. Swiss robot maker ANYbotics and software company SAP are trying to change that. ANYbotics’ four-legged autonomous robots will be connected straight into SAP’s backend enterprise resource planning software. Instead of treating a robot

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Secure governance accelerates financial AI revenue growth

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Financial institutions are learning to deploy compliant AI solutions for greater revenue growth and market advantage. For the better part of ten years, financial institutions viewed AI primarily as a mechanism for pure efficiency gains. During that era, quantitative teams programmed systems designed to discover ledger discrepancies or eliminate milliseconds from automated trading execution times.

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RPA matters, but AI changes how automation works

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RPA (robotic process automation) is a practical and proven way to reduce manual work in business processes without AI systems. By using software bots to follow fixed rules, companies can automate repetitive tasks like data entry and invoice processing, and to a certain extent, report generation. Adoption grew quickly in many sectors, especially in finance,

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Ocorian: Family offices turn to AI for financial data insights

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To gain financial data insights, the majority of family offices now turn to AI, according to new research from Ocorian. The global study reveals 86 percent of these private wealth groups are utilising AI to improve their daily operations and data analysis. Representing a combined wealth of $119.37 billion, these organisations want machine learning to

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Goldman Sachs sees AI investment shift to data centres

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Artificial intelligence investment is entering a more selective phase as companies and investors look beyond early excitement and focus on the data centre infrastructure required to run AI systems. Recent analysis from Goldman Sachs suggests the market is moving toward what the firm describes as a “flight to quality.” In practice, investors are paying closer

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How multi-agent AI economics influence business automation

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Managing the economics of multi-agent AI now dictates the financial viability of modern business automation workflows. Organisations progressing past standard chat interfaces into multi-agent applications face two primary constraints. The first issue is the thinking tax; complex autonomous agents need to reason at each stage, making the reliance on massive architectures for every subtask too

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