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GenAI can help utility field teams access technical information faster, transfer expertise and reduce manual work while maintaining the security and oversight essential to critical infrastructure
Read the articleAI investment is accelerating as enterprises confront power, cooling, supply chain and network constraints that can determine how quickly AI infrastructure scales
Read the articleEnterprise technology must now support analytics, real-time insight and AI while organizations meet stronger requirements for security, governance, resilience and sovereignty
Read the articleHow HCLTech and AWS help you scale agentic AI, modernize processes and deliver value with trust and governance.
Listen nowScaling AI takes value someone signs for, governance designed before autonomy and work redesigned around meaningful human oversight
Read the articleMainframe autonomy will depend on trusted automation, stronger observability and clear boundaries for when AI can act without human approval
Read the articleC Vijayakumar, CEO and Managing Director, HCLTech and John Roese, Global CTO and Chief AI Officer, Dell Technologies, share insights on scaling AI while maintaining control, trust and efficiency
Watch nowAI is expanding the mainframe into a real-time decision platform that combines trusted enterprise data, business logic and embedded intelligence
Read the articleAhead of AWS re:Invent, Prabhakar Appana explores how enterprises can scale Agentic AI through process redesign, trusted operating models and measurable business outcomes
Read the articleAt AI Infra Summit 2026, HCLTech’s C Vijayakumar and Dell Technologies’ John Roese explored why enterprise AI needs hybrid infrastructure, trusted processes and redesigned processes to scale
Read the articleAs AI agents take on more enterprise work, organizations need new ways to govern access, execution, cost and value across platforms
Read the articleVoice biometrics can reduce authentication friction, strengthen fraud detection and help contact centers respond to AI-enabled voice threats
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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