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From journeys to flows: Why most AI in travel and logistics is still theatrics
The industry’s AI story is comforting. That is the problem with it.
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From fragmented operations to connected decisions in transport, travel, logistics and hospitality
Why digital twins, Edge AI and cloud intelligence matter now across asset-intensive, service-critical industries
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Enterprises cannot scale AI on fragmented data, technical debt and aging core systems, making legacy modernization a strategic requirement for turning AI ambition into operational impact
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AI can help organizations accelerate product development, improve productivity and strengthen decision-making, but only when it is connected across the full product lifecycle
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As AI moves into business-critical workflows, enterprises need to match the right model to the right task while controlling cost, risk and complexity
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By combining shared data, real-world sensing, intelligent agents and tiered governance, sentient logistics can help organizations anticipate disruption and coordinate action across the value chain
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As Agentic AI transforms work, leading organizations will treat skills, role redesign and human-agent collaboration as operating model priorities rather than training exercises alone
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AI is moving into medical devices, robots and sensors, but the real story is whether the industry can overcome the challenges that will determine its success in clinical settings
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Join Dr. Andy Packham, David Carmona and Dr. Reena Gollapudy as they explore how Microsoft Discovery, combined with HCLTech’s life sciences expertise, is accelerating AI-powered pharmaceutical R&D.
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AgentOps is emerging as the next evolution of enterprise AI, helping supply chains move beyond prediction toward governed orchestration across systems, teams and the wider value chain
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Physical AI is moving enterprise AI into real-world operations, where intelligent systems can sense, decide and act to improve safety, efficiency and resilience
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The gap between AI Leaders and Followers is no longer defined by adoption alone, but by the ability to deliver measurable impact and sustained competitive advantage
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AI can create enterprise business value in aerospace and defense when it is embedded into the processes that design, plan, manufacture, certify, deliver, maintain and support aircraft
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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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