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Virender Singh, VP, Digital Business Services, HCLTech, shares how Agentic AI is reshaping the experience value chain, from platform design and personalization to operating models and team structures
Watch nowThe next disruption in commerce will not come from another channel. It will come from AI agents that search, recommend and increasingly transact on behalf of the customer.
Read the articleProcurement in Retail and CPG is becoming a strategic decision engine for margin protection, supply resilience and better buying in volatile markets
Read the articleJoin a conversational deep dive on how Industry AI Solutions and Responsible AI drive trusted, customer‑centric transformation - led by Dr. Gaurav Dhakar and Dr. Heather Domin.
Watch nowAs enterprises pursue better customer and employee experiences, Agentic AI moves beyond platform integration to interoperable systems that can reason, act and optimize outcomes
Read the articleWith customer journeys fragmenting, Agentic AI empowers brands to connect data, content and decisions in real time, turning disconnected interactions into more contextual and seamless experiences
Read the articleAI is exposing the limits of fragmented MarTech stacks, creating an opportunity to rethink data, workflows and operating models so marketing teams can focus more on outcomes than tools
Read the articleThe core challenge in Agentic AI is no longer model capability but industrialization, with GCCs rising as the control towers that can scale pilots into governed, production-grade enterprise execution
Read the articleHow AI agents are transforming banking from reactive service to intelligent, always-on engagement
Read the articleIn oil and gas, disruption is no longer episodic; it is structural and resilience now depends on diversification, real-time visibility and faster decision-making across the supply chain
Read the articleIn 2026, the real shift is not more AI pilots; it is enterprise-grade execution tied to measurable business and patient outcomes
Read the articleAs AI moves from centralized models to distributed intelligence, telecom networks are emerging as the critical infrastructure that connects creation, distribution and real-world application of AI
Read the articleTrending questions
The term "artificial intelligence" was coined by John McCarthy in 1956. Alan Mathison Turing, followed by Newell, Simon, McCarthy and Minsky, are key figures in AI development. Newell and Simon's 1956 "Logic Theorist" program marked a milestone. These pioneers, known as the founding fathers of AI, significantly advanced the field.
- Increased productivity
- Increased automation
- Smart decision-making
- Solve complex problems
- Managing repetitive tasks
- Strengthens economy
- Personalization
- Disaster management
- Enhances lifestyle
- Global defense
Machine learning is the brain of AI that emulates logical decision-making based on the data fed to it and an AI model is the creation, training and deployment of the ML algorithms. With advancements in intelligence methodologies, AI models support in tandem with real-time analytics, predictive analytics and augmented analytics using natural language processing (NLP), ML, statistical analysis and algorithmic execution.
Loaded with human capabilities and beyond, the importance of artificial intelligence (AI) is rising and gradually spreading to various industries, making way for new possibilities and better efficiencies. Data in today’s world is an asset and valued across industries. With AI, humans are now able to absorb, interpret and make complex decisions.
While artificial intelligence is a system that mimics or imitates human intelligence, machine learning is the brain that helps it work.
Generative AI refers to a subset of artificial intelligence algorithms and models designed to generate new data that resembles existing data. These sophisticated algorithms can create content in various forms, such as text, images, music and even complex structures like designs and models. Generative AI is primarily powered by advances in neural networks, particularly Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs).
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