Category: AI

  • The Changing Face of AI

    The Changing Face of AI

    For much of the past decade, artificial intelligence has been discussed primarily as a tool. We spoke about models, accuracy, deployment pipelines, and proofs of concept. The implicit assumption was simple: AI supports human decisions, and humans remain firmly in control. That assumption is now starting to break. The most important shift in AI today…

  • The Rise of Agentic AI: When Software Begins to Take Responsibility

    The Rise of Agentic AI: When Software Begins to Take Responsibility

    Recently my opinion on Agentic AI got published in Digital Terminal. Putting some We are entering a critical moment in artificial intelligence evolution. For decades, AI systems were designed to respond. They processed inputs, generated outputs, and waited for instructions. Even the most advanced models remained fundamentally passive. That ceiling has now been reached. A…

  • When Decisions Are Driven by Models

    When Decisions Are Driven by Models

    AI was built to help us understand the world. Somewhere along the way, many organizations have started letting it replace the world instead. This shift is subtle. It arrives as convenience rather than as a visible failure. Dashboards look complete, models sound confident, and simulations feel safer than messy reality. Over time, leaders stop asking…

  • When Scale Is No Longer Enough

    When Scale Is No Longer Enough

    Over the past few years, I have found myself thinking less about how large our AI models have become and more about how they behave once they are deployed in the real world. Scale has delivered substantial progress. Larger models, more data, and more compute have unlocked capabilities that were previously out of reach. But…

  • India’s AI Agriculture Outlook for 2026

    India’s AI Agriculture Outlook for 2026

    Recently I got published in The Hindu BusinessLine where I discussed about the India AI outlook for Agriculture in 2026. Below is India’s use of artificial intelligence in agriculture is entering an important phase of maturity. In 2026, the focus is expected to move beyond pilots and isolated experimentation toward questions of integration, reliability, and…

  • At 48th Fujitsu Convention

    At 48th Fujitsu Convention

    At the 48th annual Fujitsu Convention in Japan, I served as a speaker in the plenary session, presenting on strengthening Fujitsu’s presence in India and why India is increasingly central to global AI capability, digital engineering scale, and innovation ecosystems. I spoken Why India and why it’s unique my version – Democracy, Demography, Diversity, Digital…

  • Can Intelligence Be Centralized? Rethinking RAG in the Era of Federated AI

    Can Intelligence Be Centralized? Rethinking RAG in the Era of Federated AI

    Over the past year, RAG has quietly become the backbone of modern AI systems. It gives language models access to real-world knowledge, helping them stay relevant, factual, and grounded. But as enterprises start scaling RAG across teams, countries, and regulations, one question keeps surfacing i.e., Can intelligence truly be centralized? In theory, it’s yes! It…

  • Course Launched – Software Engineering with Knowledge Graphs and RAG

    Course Launched – Software Engineering with Knowledge Graphs and RAG

    In an era where software complexity continues to surge, traditional development and maintenance practices are struggling to keep pace. Fragmented documentation, disjointed systems, and siloed knowledge often hinder progress, leading to inefficiencies and risks across the software lifecycle. To address these, I have developed one short 1 hour course where you can understand merging the…

  • AI in 2025 | From Experimentation to Execution and Impact

    AI in 2025 | From Experimentation to Execution and Impact

    In 2025, Artificial Intelligence (AI) is expected to transition from being a transformative technology to a fundamental enabler of progress. The year promises advancements in generative AI, domain-specific applications, collaborative ecosystems, and strategic national initiatives, collectively shaping a more practical and impactful AI-driven future Emerging areas like Agentic AI and Quantum AI will also continue…

  • Transforming Labour and Welfare Through Digital Innovation

    Transforming Labour and Welfare Through Digital Innovation

    India is a complex dynamics country, and there is an urgent need to digitize the labour and employment department. I have identified few quick problem/opportunities area where we can apply emerging technologies. From transforming governance to enhancing social equity, technologies like AI offers solutions to long-standing inefficiencies and gaps. Based on my observations about this…

  • Graph DB vs Vector DB for AI

    Graph DB vs Vector DB for AI

    The discussion regarding graph and vector databases is increasing due to organizations putting their weight behind AI. In fact, each has unique strengths, and choosing the right one depends on various factors, like how application processes data, handles queries, scales, and what specific objectives you aim to achieve. Graph databases (e.g., Neo4j, Amazon Neptune, ArangoDB,…