machine-learning
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Digital transformation is evolving towards agentic AI, moving beyond optimization to disruptive autonomy. Despite high AI usage, many organizations falter in achieving tangible value due to legacy systems. This shift calls for a fundamental re-architecture, fostering decentralized data frameworks and multi-agent ecosystems to enhance productivity, efficiency, and innovation in business processes.
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In late 2025, AI has transitioned from “generative experimentation” to “agentic execution,” marking a shift to autonomous systems capable of multi-step planning. Innovations include enhanced memory frameworks and reason-and-search loops, while hardware is evolving towards custom accelerators. The socio-economic impact involves workforce transformation and stringent regulatory developments guiding AI’s future.
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The integration of AI and machine learning in investment management is transforming firms into competitive forces. Early adopters are building advanced capabilities for speed, scale, and adaptive intelligence. While current applications mainly augment human processes, a significant shift towards transformative AI adoption and alternative data utilization is expected, enhancing performance and risk management.
Journal for Policy & Market Research 