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EU AI Act: What businesses need to know as regulations go live

AI News

They must demonstrate tangible ROI from AI investments while navigating challenges around data quality and regulatory uncertainty. Its already the perfect storm, with 89% of large businesses in the EU reporting conflicting expectations for their generative AI initiatives. For businesses, the pressure in 2025 is twofold.

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AI and Financial Crime Prevention: Why Banks Need a Balanced Approach

Unite.AI

Perhaps, then, the response from banks should be to arm themselves with even better tools, harnessing AI across financial crime prevention. Financial institutions are in fact starting to deploy AI in anti-financial crime (AFC) efforts – to monitor transactions, generate suspicious activity reports, automate fraud detection and more.

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How Quality Data Fuels Superior Model Performance

Unite.AI

Its not a choice between better data or better models. The future of AI demands both, but it starts with the data. Why Data Quality Matters More Than Ever According to one survey, 48% of businesses use big data , but a much lower number manage to use it successfully. Why is this the case?

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How Emerging Generative AI Models Like DeepSeek Are Shaping the Global Business Landscape

Unite.AI

Organizations must align AI investments with strategic priorities, ensuring implementation occurs in areas that offer operational efficiency with relatively quick and measurable ROI. This shift will accelerate the advancement of AI applications across behavioral insights , asset damage detection, medical imaging and various other functions.

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AI in DevOps: Streamlining Software Deployment and Operations

Unite.AI

Training AI models with subpar data can lead to biased responses and undesirable outcomes. When unstructured data surfaces during AI development, the DevOps process plays a crucial role in data cleansing, ultimately enhancing the overall model quality. Poor data can distort AI responses.

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Microsoft Research Introduces AgentInstruct: A Multi-Agent Workflow Framework for Enhancing Synthetic Data Quality and Diversity in AI Model Training

Marktechpost

The rapid advancement in AI technology has heightened the demand for high-quality training data, which is essential for effectively functioning and improving these models. One of the significant challenges in AI development is ensuring that the synthetic data used to train these models is diverse and of high quality.

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SolarWinds IT Trends Report 2024: Embracing AI – A Boon or a Risk?

Unite.AI

Key Insights AI Improves Efficiency and Productivity in IT Teams Automation and Efficiency : A significant portion of IT professionals (46%) believe that AI investments will lead to increased efficiency, making it the primary driver for adopting AI technologies.