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    Home»Blogs»Three key trends shaping AI deployments in 2025
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    Three key trends shaping AI deployments in 2025

    mmBy ITPulseJanuary 15, 2025112 Views
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    Artificial intelligence
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    The artificial intelligence (AI) landscape is rapidly evolving. Key trends shaping AI deployments in 2025 include a focus on financial accountability, the rise of Agentic AI, and the growing importance of domain-specific and smaller language models.

    According to GlobalData, a leading data and analytics company, organizations are moving towards a more mature approach to implementing Generative AI (GenAI). This includes developing realistic roadmaps, identifying key performance indicators (KPIs) to measure project impact, and conducting thorough return on investment (ROI) analyses to evaluate success.

    “Many organizations have been experimenting with GenAI with varying degrees of success,” says Rena Bhattacharyya, Chief Analyst and Practice Lead for Enterprise Technology and Services at GlobalData. “What was once an opportunistic approach is now evolving into a more structured and strategic one, focused on maximizing business impact.”

    GlobalData predicts that Agentic AI, with its enhanced memory and planning capabilities, will significantly accelerate GenAI adoption within enterprises. These systems can better understand and anticipate user intentions, proactively taking initiative and acting independently.

    “With improved reasoning capabilities, these GenAI agents can translate user intentions into a series of steps, taking the initiative and acting independently,” notes Beatriz Valle, Analyst for Enterprise Technology and Services at GlobalData.

    Furthermore, the emergence of smaller language models is making on-premises and edge deployments of AI more feasible. These smaller models offer lower costs and reduced complexity, enabling more organizations to consider running GenAI locally.

    “The transition to AI at the edge has significant implications for real-time applications that require low latency connectivity,” concludes Bhattacharyya. “Edge processing improves performance by minimizing data transportation and addressing data privacy and regulatory concerns. However, this shift necessitates a reevaluation of networking requirements and overall cloud strategies.

    AI deployments
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