Gaurav Bhandari
Gaurav is the Content Lead at Kore.ai. He has over 10 years of experience creating content and designing marketing campaigns that drive organic growth for B2B companies. As both an engineer and a marketer, he excels at translating technical concepts into compelling marketing content. Outside of work, he enjoys long walks and playing badminton with his 10-year-old daughter.
Articles by this author
How AI is redefining work productivity in enterprises
The Kore.ai survey of over 1,000 enterprise leaders shows AI has moved beyond automating repetitive tasks into acting as an intelligent collaborator. The top use cases are analytics and insights at 75%, search and information discovery at 65%, content generation at 61%, and workflow automation at 58%. The biggest barriers remain talent gaps, data security, and the cost of running large language models. Despite this, 89% of organizations plan to increase AI investment.
Enterprise search: How AI-powered search boosts work productivity
Enterprise search unifies scattered structured and unstructured data into one layer so employees get instant answers instead of hunting across systems. AI powered search goes beyond keyword matching, using natural language processing, semantic search, and agentic RAG to understand intent and return contextual answers. It is also becoming the foundation for AI agents, since they rely on the same search infrastructure to reason and act.
Agentic architecture: Blueprint for enterprise AI architecture
Traditional pipeline based architectures break down once AI agents start issuing thousands of queries a minute. Agentic systems need shared memory, real time context, and orchestration to function. Agentic architecture is the structural framework that lets AI agents act with autonomy and coordinate with other agents, humans, and data.
Agentic AI: What it means, how it works & real use cases (2026)
Agentic AI is not just smarter GenAI, it is operational, capable of handling complex tasks end to end. The real advantage comes from orchestration, where multi agent coordination combined with governance makes deployments scalable. Agents work through a five stage model: sensing the environment, planning, acting, learning from feedback, and collaborating with other agents. Enterprises are already using this for workplace productivity, customer service, and business process automation at scale.
Agentic AI vs Generative AI: Key differences enterprises need to know
Generative AI creates content like text, images, and code from a prompt, while Agentic AI plans and executes complex tasks end to end without waiting for instructions. GenAI makes individuals faster, but Agentic AI changes how entire processes run. The two are not competitors. Together, they create systems that both imagine solutions and carry them out.
Agentic AI vs AI agents: Key differences enterprises need to know
AI agents automate specific tasks and wait to be called, following rules within fixed boundaries. Agentic AI goes further, setting its own goals, planning multi step actions, and taking initiative without waiting for a prompt. The two are not rivals, they are layers of the same stack, with agents handling precise execution and agentic systems orchestrating outcomes across them. The more autonomy AI gets, the more governance it needs, including clear goals, audit trails, and human oversight.