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As organizations explore the promise of Agentic AI, many are discovering that scaling from pilot programs to enterprise‑wide deployment is far more complex than anticipated. Intelligent agents can deliver immense value—but only when deployed in a controlled, interoperable, and secure manner. Industry surveys show that while many enterprises are experimenting with AI agents, only about 23% are scaling agentic AI systems across broader operations, underlining that robust framew

As we move toward 2026, the enterprise landscape is on the verge of a defining shift. The focus is no longer just on automation—it’s about autonomy. The rise of Agentic AI marks a turning point where intelligent digital agents can think, plan, and act independently to achieve business objectives. According to Gartner, up to 40% of enterprise applications are expected to integrate task‑specific AI agents by 2026, up dramatically from less than 5% today, signaling how quickly a

Digital transformation has become the backbone of modern enterprise strategy, but the real driver behind this shift is data. Every major business decision, customer interaction, and operational improvement now depends on how effectively an organization uses its data. As enterprises modernize their systems and adopt smarter technologies, three trends are emerging at the centre of this transformation: AI, Real-Time Analytics, and Cloud Data Modernization. These trends are not j

As we step into 2026, one trend is becoming impossible to ignore: enterprises are preparing to adopt Agentic AI in a more structured and intentional way. The initial experimentation phase with generative AI is behind us. Companies now want AI that can take action, make decisions, and independently run essential tasks.  Agentic AI represents this leap. These agents behave like intelligent digital teammates, capable of planning and executing work across systems. Instead of simp