{"id":28156,"date":"2026-04-06T15:44:35","date_gmt":"2026-04-06T10:14:35","guid":{"rendered":"https:\/\/ivaluegroup.com\/en-in\/?p=28156"},"modified":"2026-04-06T16:01:13","modified_gmt":"2026-04-06T10:31:13","slug":"the-agentic-leap-why-indian-enterprises-need-new-infrastructure-before-they-deploy-ai-agents","status":"publish","type":"post","link":"https:\/\/ivaluegroup.com\/en-in\/resources\/blogs\/the-agentic-leap-why-indian-enterprises-need-new-infrastructure-before-they-deploy-ai-agents\/","title":{"rendered":"The Agentic Leap: Why Indian Enterprises Need New Infrastructure Before They Deploy AI Agents"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"28156\" class=\"elementor elementor-28156\" data-elementor-post-type=\"post\">\n\t\t\t\t<div class=\"elementor-element elementor-element-03f880d e-flex e-con-boxed e-con e-parent\" data-id=\"03f880d\" data-element_type=\"container\" data-core-v316-plus=\"true\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-eb2d667 elementor-widget elementor-widget-heading\" data-id=\"eb2d667\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.20.0 - 26-03-2024 *\/\n.elementor-heading-title{padding:0;margin:0;line-height:1}.elementor-widget-heading .elementor-heading-title[class*=elementor-size-]>a{color:inherit;font-size:inherit;line-height:inherit}.elementor-widget-heading .elementor-heading-title.elementor-size-small{font-size:15px}.elementor-widget-heading .elementor-heading-title.elementor-size-medium{font-size:19px}.elementor-widget-heading .elementor-heading-title.elementor-size-large{font-size:29px}.elementor-widget-heading .elementor-heading-title.elementor-size-xl{font-size:39px}.elementor-widget-heading .elementor-heading-title.elementor-size-xxl{font-size:59px}<\/style><h2 class=\"elementor-heading-title elementor-size-default\">India Is Racing Towards The New Frontier Of AI\n<span style=\"font-size: 2.5rem; font-style: inherit;\"><\/span><\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-ecc02ef e-con-full e-flex e-con e-parent\" data-id=\"ecc02ef\" data-element_type=\"container\" data-core-v316-plus=\"true\">\n\t\t\t\t<div class=\"elementor-element elementor-element-c4a29e2 elementor-widget elementor-widget-text-editor\" data-id=\"c4a29e2\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.20.0 - 26-03-2024 *\/\n.elementor-widget-text-editor.elementor-drop-cap-view-stacked .elementor-drop-cap{background-color:#69727d;color:#fff}.elementor-widget-text-editor.elementor-drop-cap-view-framed .elementor-drop-cap{color:#69727d;border:3px solid;background-color:transparent}.elementor-widget-text-editor:not(.elementor-drop-cap-view-default) .elementor-drop-cap{margin-top:8px}.elementor-widget-text-editor:not(.elementor-drop-cap-view-default) .elementor-drop-cap-letter{width:1em;height:1em}.elementor-widget-text-editor .elementor-drop-cap{float:left;text-align:center;line-height:1;font-size:50px}.elementor-widget-text-editor .elementor-drop-cap-letter{display:inline-block}<\/style>\t\t\t\t<p><span style=\"font-weight: 400;\">Earlier this year, the AI Impact Summit in New Delhi raised awareness on the ever expanding role AI is playing in daily life\u2026 and how India plans to be at the forefront of this space moving forward. That begs the question: <\/span><i><span style=\"font-weight: 400;\">what\u2019s the next big thing in the world of AI?<\/span><\/i><\/p><p><span style=\"font-weight: 400;\">Many enterprises believe the answer to that question is the incorporation of <\/span><i><span style=\"font-weight: 400;\">agentic AI<\/span><\/i><span style=\"font-weight: 400;\"> &#8211; systems that leverage AI to reason, plan and complete a wide variety of organizational tasks. Enterprise usage of agentic AI can be categorised in 4 different levels:<\/span><\/p><table><tbody><tr><td><p><b>Level 1:<\/b><\/p><p><b><i>Chain<\/i><\/b><\/p><\/td><td><p><b>Level 2:<\/b><\/p><p><b><i>Workflow<\/i><\/b><\/p><\/td><td><p><b>Level 3:<\/b><\/p><p><b><i>Partially Autonomous<\/i><\/b><\/p><\/td><td><p><b>Level 4:<\/b><\/p><p><b><i>Fully Autonomous<\/i><\/b><\/p><\/td><\/tr><tr><td><p><span style=\"font-weight: 400;\">Rule-based robotic process automation (RPA) with pre-defined actions and sequences.<\/span><\/p><\/td><td><p><span style=\"font-weight: 400;\">Actions are pre-defined, but the sequence can be dynamically determined by AI using LLMs, routers, etc.<\/span><\/p><\/td><td><p><span style=\"font-weight: 400;\">Given a goal, the agent can plan, execute and adjust a sequence of actions using a domain-specific toolkit with minimal human oversight.<\/span><\/p><\/td><td><p><span style=\"font-weight: 400;\">With little to no oversight, these agents proactively set goals, adapt to outcomes and possess capacity to even create their own tools in order to meet objectives.<\/span><\/p><\/td><\/tr><tr><td><p><i><span style=\"font-weight: 400;\">Use Case:<\/span><\/i><\/p><p><span style=\"font-weight: 400;\">Extraction of invoice details from PDFs into separate databases.<\/span><\/p><\/td><td><p><i><span style=\"font-weight: 400;\">Use Case:<\/span><\/i><\/p><p><span style=\"font-weight: 400;\">Drafting of customer emails through intelligent LLM capabilities.<\/span><\/p><\/td><td><p><i><span style=\"font-weight: 400;\">Use Case:<\/span><\/i><\/p><p><span style=\"font-weight: 400;\">Resolution of customer support tickets across multiple systems.<\/span><\/p><\/td><td><p><i><span style=\"font-weight: 400;\">Use Case:<\/span><\/i><\/p><p><span style=\"font-weight: 400;\">Strategic research agents with the capacity to independently discover, summarise and synthesise information.<\/span><\/p><\/td><\/tr><\/tbody><\/table><p><span style=\"font-weight: 400;\">Most AI adoption currently seen in enterprises lies between Level 1 &amp; 2. As capabilities rapidly improve, Level 3 &amp; beyond is when it starts to get transformational &#8211; an inflection point that McKinsey describes as a \u2018moment of strategic divergence\u2019 where early movers will redefine competitive dynamics.<\/span><\/p><p><span style=\"font-weight: 400;\">India is leading the charge in this as well, with the 2025 BCG AI At Work survey ranking the nation second globally when it comes to AI agent integration. Yet, the entire movement is shrouded in a state of introductory flux: 77% of respondents believe AI agents will be important in the next three to five years, yet only 33% say they have a proper understanding of what these agents actually are.<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-d9631b2 e-flex e-con-boxed e-con e-parent\" data-id=\"d9631b2\" data-element_type=\"container\" data-core-v316-plus=\"true\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-bb8df76 elementor-widget elementor-widget-heading\" data-id=\"bb8df76\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">What Needs To Change For Enterprises To Fully Take The Agentic Leap?\n<span style=\"font-size: 2.5rem; font-style: inherit;\"><\/span><\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-ab275b4 e-con-full e-flex e-con e-parent\" data-id=\"ab275b4\" data-element_type=\"container\" data-core-v316-plus=\"true\">\n\t\t\t\t<div class=\"elementor-element elementor-element-f93612e elementor-widget elementor-widget-text-editor\" data-id=\"f93612e\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">Research from Gartner shares a similar dichotomy in terms of what the future looks like for enterprise agentic AI in the next 3 years:<\/span><\/p><table><tbody><tr><td><p><b>More Integrations<\/b><\/p><p><span style=\"font-weight: 400;\">Gartner predicts that by 2028, 33% of enterprise software applications will contain agentic AI capabilities &#8211; rising from less than 1% in 2024.\u00a0<\/span><\/p><p><span style=\"font-weight: 400;\">Furthermore, 15% of day-to-day work decisions will be accomplished autonomously by then.<\/span><\/p><\/td><td><br \/><br \/><p><span style=\"font-weight: 400;\">\ud83d\udd01<\/span><\/p><\/td><td><p><b>More Failures<\/b><\/p><p><span style=\"font-weight: 400;\">Gartner also predicts that by the end of 2027, more than 40% of agentic projects will fail or get cancelled due to reasons like:<\/span><\/p><ul><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Escalating costs<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Unclear business value<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Not enough risk controls<\/span><\/li><\/ul><\/td><\/tr><\/tbody><\/table><p><span style=\"font-weight: 400;\">The difference between success and failure will hinge on the way organizations integrate AI agents into their business processes and technical infrastructures. Companies that win the agentic AI race won\u2019t be the ones that deploy fastest &#8211; <\/span><i><span style=\"font-weight: 400;\">they\u2019ll be the ones that build the foundations required to make AI agents actually function properly.<\/span><\/i><\/p><p><span style=\"font-weight: 400;\">These new foundations are critical because the truth is, <\/span><i><span style=\"font-weight: 400;\">current infrastructures are <\/span><\/i><b><i>not suitable <\/i><\/b><i><span style=\"font-weight: 400;\">for the elevated requirements of agentic AI.<\/span><\/i><span style=\"font-weight: 400;\"> Even at this nascent stage, teams that bolted agent execution onto existing infrastructure consistently reported:<\/span><\/p><ul><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Higher incident rates<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">More security concerns<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Greater operational overheads<\/span><\/li><\/ul>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-7247f51 e-flex e-con-boxed e-con e-parent\" data-id=\"7247f51\" data-element_type=\"container\" data-core-v316-plus=\"true\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-93fc97f elementor-widget elementor-widget-heading\" data-id=\"93fc97f\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h4 class=\"elementor-heading-title elementor-size-default\">The Two Pillars Driving New Agentic AI Infrastructure<span style=\"font-size: 1.5rem; font-style: inherit;\"><\/span><\/h4>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-de39da7 e-con-full e-flex e-con e-parent\" data-id=\"de39da7\" data-element_type=\"container\" data-core-v316-plus=\"true\">\n\t\t\t\t<div class=\"elementor-element elementor-element-d6b0102 elementor-widget elementor-widget-text-editor\" data-id=\"d6b0102\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">The biggest shift in AI in recent years &#8211; something that agentic AI is driving &#8211; has been a focus from training to inference. While training also requires massive levels of data and computing, the process is also quite predictable. On the other hand, inference tasks are relatively small in isolation, but they can add up fast.<\/span><\/p><p><span style=\"font-weight: 400;\">For agentic AI to really flourish into transformational use cases (Level 3 &amp; beyond), enterprises must create an event-driven, resilient infrastructure that can orchestrate complex, long-running processes across distributed systems. This will require inference capabilities that are far beyond what your current infrastructure can probably deliver, whether it\u2019s:<\/span><\/p><ul><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Persistent learning &amp; memory across a robust, governed data infrastructure<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Coordination capabilities spanning multi-part tasks and multi agent workflows<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Real-time orchestration &amp; decision-making in order to carry out complex requests<\/span><\/li><\/ul><p>\u00a0<\/p><p><span style=\"font-weight: 400;\">New infrastructure foundations that support all these instances of enhanced inference must be built with 2 key pillars:<\/span><\/p><table><tbody><tr><td><p><b>Pillar 1:<\/b><\/p><p><i><span style=\"font-weight: 400;\">Holistic + Persistent <\/span><\/i><b><i>Context<\/i><\/b><\/p><\/td><td><p><b>Pillar 2:<\/b><\/p><p><i><span style=\"font-weight: 400;\">Scalable + Flexible <\/span><\/i><b><i>Compute<\/i><\/b><\/p><\/td><\/tr><tr><td><p><span style=\"font-weight: 400;\">For agentic AI to autonomously reason &amp; execute to the best of its abilities, you must create data architectures that deliver business context that is reliably used by agents &#8211; and the humans governing them. This involves:<\/span><\/p><ul><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Breaking down data silos to create real-time visibility of all your assets<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Adding a unified knowledge layer that can efficiently store, index, retrieve and update contextual data on demand without latency bottlenecks<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Having dedicated memory components that enable context tracking over multiple sessions\u00a0<\/span><\/li><\/ul><\/td><td><p><span style=\"font-weight: 400;\">Once context is established, it is time to incorporate a corresponding compute infrastructure that enables agents to conduct all these various tasks in scale:<\/span><\/p><ul><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Operating in live environments<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Interacting with APIs<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Recalling previous decisions<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Reasoning across inputs<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Adjusting behaviour in real-time<\/span><\/li><\/ul><p><span style=\"font-weight: 400;\">However, running persistent infrastructure for these agents drives up costs, as there is a chance that these systems can remain idle at times due to the volatility of inference tasks. Therefore, <\/span><i><span style=\"font-weight: 400;\">cloud-based ephemeral approaches<\/span><\/i><span style=\"font-weight: 400;\"> that spin up resources only when needed seem to be the prevailing method of accommodating these agentic workflows.<\/span><\/p><\/td><\/tr><\/tbody><\/table><p><span style=\"font-weight: 400;\">Many public cloud options in the market are now ramping up their capabilities to handle these exponentially increasing compute requirements &#8211; announcements of several gigawatt-scale AI cloud data centre facilities in India is testament to this. However, as agents get more advanced and cloud-hosted models continue to improve, many enterprises &#8211; especially those in highly regulated industries &#8211; are increasingly preferring to create personalized hybrid clouds spanning public infrastructures and private setups in order to truly maximize agentic AI.\u00a0<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-43e4b9b e-flex e-con-boxed e-con e-parent\" data-id=\"43e4b9b\" data-element_type=\"container\" data-core-v316-plus=\"true\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-bffb71c elementor-widget elementor-widget-heading\" data-id=\"bffb71c\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h4 class=\"elementor-heading-title elementor-size-default\">The Other Must-Haves In Enterprise Agent Deployment\n\n<span style=\"font-size: 1.5rem; font-style: inherit;\"><\/span><\/h4>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-35faa1b e-con-full e-flex e-con e-parent\" data-id=\"35faa1b\" data-element_type=\"container\" data-core-v316-plus=\"true\">\n\t\t\t\t<div class=\"elementor-element elementor-element-4f3e385 elementor-widget elementor-widget-text-editor\" data-id=\"4f3e385\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">The two aforementioned pillars will provide the initial infrastructure required for you to run agentic AI. However, due to its complexities &#8211; both in terms of processes and the way it will ultimately shape your work culture &#8211; several must-have components (specific to agentic AI) must be added on top of this:<\/span><\/p><ul><li aria-level=\"1\"><b>Observability: <\/b><span style=\"font-weight: 400;\">Once operationalised, your enterprise must create an observability layer that helps track &amp; analyze agent performance, system health &amp; potential risks across all your environments and throughout the entire agent lifecycle. OpenTelemetry is considered to be the current open-source standard for real-time agentic AI monitoring.<\/span><\/li><\/ul><ul><li aria-level=\"1\"><b>Interoperability: <\/b><span style=\"font-weight: 400;\">Firstly, you must create individual isolated environments (sandboxes) for these agents where untrusted code can run without affecting the host system or other workloads. Then, you must combine individual agent sandboxing with environment-level orchestration &#8211; where agents can seamlessly interact with other systems and agents. Model Context Protocol (MCP) is becoming an emerging standard for supporting agent interoperability across multiple systems &amp; vendors.<\/span><\/li><\/ul><ul><li aria-level=\"1\"><b>Security: <\/b><span style=\"font-weight: 400;\">Your enterprise must construct a security framework to address novel agentic AI risks such as prompt filtering, response enforcement, data security and external access control. If your AI agents are dealing with highly sensitive information, they must be governed by access controls even stricter than what you use for your human users.<\/span><\/li><\/ul><ul><li aria-level=\"1\"><b>Governance: <\/b><span style=\"font-weight: 400;\">Considering the worldwide concerns about the use of \u2018responsible AI\u2019, it is important to embed governance right from the start to ensure AI deployments operate within clearly defined ethical, operational &amp; compliance boundaries.<\/span><\/li><li aria-level=\"1\"><b>Change Management: <\/b><span style=\"font-weight: 400;\">Finally, the dynamics of human-AI collaboration is key to making all of this work. Your enterprise needs comprehensive change management programs that address employee concerns about how AI agents will augment rather than replace humans. The focus must be on integrating AI agents as teammates rather than tools.<\/span><\/li><\/ul>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-d52584b e-flex e-con-boxed e-con e-parent\" data-id=\"d52584b\" data-element_type=\"container\" data-core-v316-plus=\"true\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-5f724dd elementor-widget elementor-widget-heading\" data-id=\"5f724dd\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h4 class=\"elementor-heading-title elementor-size-default\">How Do You Start Deploying AI Agents Into Your Enterprise?\n<span style=\"font-size: 1.5rem; font-style: inherit;\"><\/span><\/h4>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-c6b9268 e-con-full e-flex e-con e-parent\" data-id=\"c6b9268\" data-element_type=\"container\" data-core-v316-plus=\"true\">\n\t\t\t\t<div class=\"elementor-element elementor-element-7d8894d elementor-widget elementor-widget-text-editor\" data-id=\"7d8894d\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">During the overhaul of your existing infrastructure, the primary goal should be developing agentic pipelines that can execute reliably across a wide range of use cases. Once that is achieved, it is important to take things step-by-step:<\/span><\/p><p><b>Step 1: <\/b><span style=\"font-weight: 400;\">Start off with high-impact, low-risk use cases that address specific business pain points &#8211; several popular initial agentic AI missions include customer service automation and document processing.<\/span><\/p><p><b>Step 2:<\/b><span style=\"font-weight: 400;\"> Define these initiatives through measurable KPIs. You should be striving for accuracy rates above 95% and task completion rates above 90%.<\/span><\/p><p><b>Step 3:<\/b><span style=\"font-weight: 400;\"> Maintain coherent state management over time, with feedback mechanisms that catch mistakes before they cascade.<\/span><\/p><p><b>Step 4:<\/b><span style=\"font-weight: 400;\"> Once these particular use cases are deemed successful, responsibly expand scope and usage of agentic AI across other parts of your business.<\/span><\/p><p>\u00a0<\/p><p><span style=\"font-weight: 400;\">Of course, this is where a managed service partner well-versed in this domain &#8211; like iValue &#8211; can prove to be the difference. <\/span><span style=\"font-weight: 400;\">Click here<\/span><span style=\"font-weight: 400;\"> to speak to one of our experts about the kind of enterprise use cases you can start transforming today with the incorporation of agentic AI.<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"<p>India Is Racing Towards The New Frontier Of AI Earlier this year, the AI Impact Summit in New Delhi raised awareness on the ever expanding role AI is playing in daily life\u2026 and how India plans to be at the forefront of this space moving forward. That begs the question: what\u2019s the next big thing &hellip;<\/p>\n<p class=\"read-more\"> <a class=\"\" href=\"https:\/\/ivaluegroup.com\/en-in\/resources\/blogs\/the-agentic-leap-why-indian-enterprises-need-new-infrastructure-before-they-deploy-ai-agents\/\"> <span class=\"screen-reader-text\">The Agentic Leap: Why Indian Enterprises Need New Infrastructure Before They Deploy AI Agents<\/span> Read More \u00bb<\/a><\/p>\n","protected":false},"author":19,"featured_media":28164,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"site-sidebar-layout":"default","site-content-layout":"default","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","theme-transparent-header-meta":"default","adv-header-id-meta":"","stick-header-meta":"default","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","footnotes":"","_links_to":"","_links_to_target":""},"categories":[131],"tags":[498,249,669,670,672,671],"whitepapers":[],"case_studies":[],"acf":[],"_links":{"self":[{"href":"https:\/\/ivaluegroup.com\/en-in\/wp-json\/wp\/v2\/posts\/28156"}],"collection":[{"href":"https:\/\/ivaluegroup.com\/en-in\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/ivaluegroup.com\/en-in\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/ivaluegroup.com\/en-in\/wp-json\/wp\/v2\/users\/19"}],"replies":[{"embeddable":true,"href":"https:\/\/ivaluegroup.com\/en-in\/wp-json\/wp\/v2\/comments?post=28156"}],"version-history":[{"count":5,"href":"https:\/\/ivaluegroup.com\/en-in\/wp-json\/wp\/v2\/posts\/28156\/revisions"}],"predecessor-version":[{"id":28165,"href":"https:\/\/ivaluegroup.com\/en-in\/wp-json\/wp\/v2\/posts\/28156\/revisions\/28165"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/ivaluegroup.com\/en-in\/wp-json\/wp\/v2\/media\/28164"}],"wp:attachment":[{"href":"https:\/\/ivaluegroup.com\/en-in\/wp-json\/wp\/v2\/media?parent=28156"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/ivaluegroup.com\/en-in\/wp-json\/wp\/v2\/categories?post=28156"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/ivaluegroup.com\/en-in\/wp-json\/wp\/v2\/tags?post=28156"},{"taxonomy":"whitepapers","embeddable":true,"href":"https:\/\/ivaluegroup.com\/en-in\/wp-json\/wp\/v2\/whitepapers?post=28156"},{"taxonomy":"case_studies","embeddable":true,"href":"https:\/\/ivaluegroup.com\/en-in\/wp-json\/wp\/v2\/case_studies?post=28156"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}