2025 Predictions-AI Finds a Reason to Tap Industry Data Lakes

As we edge closer to 2025, the global landscape is on the brink of a monumental transformation driven by artificial intelligence (AI). Spearheaded by advancements in large language models (LLMs) and the strategic utilization of vast data repositories known as data lakes, industries from healthcare to retail are poised to undergo unprecedented changes. According to NVIDIA AI experts, this convergence of AI and data is set to unlock the full potential of an estimated 120 zettabytes of untapped data, reshaping the way businesses operate and innovate.

The Unseen Potential of Data Lakes

Since the inception of the computer age, industries have amassed colossal amounts of data—so much that much of it remains unused. These data lakes, comprising trillions of terabytes, represent an immense resource that, until recently, was too unwieldy to harness effectively. The integration of AI, particularly LLMs, has changed this dynamic, enabling industries to transform raw data into actionable insights and intelligent systems.

Manuvir Das, Head of Enterprise Computing at NVIDIA, articulates this shift:
"For decades, industries have collected data without truly knowing how to harness its potential. Today, AI is finally giving us the tools to transform these massive datasets into engines of innovation."

Intelligent Stores- Revolutionizing Retail

Retail is at the forefront of AI-driven transformation. Intelligent stores, powered by AI, are set to redefine the shopping experience by offering hyper-personalized interactions, optimizing inventory management, and predicting consumer trends with remarkable accuracy.

Intelligent supply chain: Intelligent supply chains created using digital twins, generative AI, machine learning and AI-based solvers will drive billions of dollars in labor productivity and operational efficiencies. Digital twin simulations of stores and distribution centers will optimize layouts to increase in-store sales and accelerate throughput in distribution centers.

Agentic robots working alongside associates will load and unload trucks, stock shelves and pack customer orders. Also, last-mile delivery will be enhanced with AI-based routing optimization solvers, allowing products to reach customers faster while reducing vehicle fuel costs.

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‍Azita Martin, Vice President of Retail at NVIDIA, explains:
"Retailers are moving beyond traditional analytics. AI reasoning enables a deeper understanding of customer behavior, empowering businesses to meet demands instantly while driving profitability."

How to Learn and Harness It:

• Enroll in AI for Retail Courses: Platforms like Coursera, Udemy, and LinkedIn Learning offer specialized courses focusing on AI applications in retail, including predictive analytics and customer personalization.
• Experiment with Retail AI Tools: Utilize tools such as Google Cloud AI and NVIDIA’s AI frameworks to create data-driven retail solutions.
• Understand Consumer Behavior: Study user experience design (UX) and consumer psychology to effectively implement AI-driven personalization strategies.

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‍‍Next-Generation Robotics in Manufacturing

Manufacturing is experiencing a renaissance with the introduction of reasoning-enabled robots. These advanced machines are not only performing repetitive tasks but also making autonomous decisions, adapting to dynamic environments, and collaborating seamlessly with human workers.

Redefining robots: When people think of robots today, they’re usually images or content showing autonomous mobile robots (AMRs), manipulator arms or humanoids. But tomorrow’s robots are set to be an autonomous system that perceives, reasons, plans and acts — then learns.

Soon we’ll be thinking of robots embodied everywhere from surgical rooms and data centers to warehouses and factories. Even traffic control systems or entire cities will be transformed from static, manually operated systems to autonomous, interactive systems embodied by physical AI.

The rise of small language models: To improve the functionality of robots operating at the edge, expect to see the rise of small language models that are energy-efficient and avoid latency issues associated with sending data to data centers. The shift to small language models in edge computing will improve inference in a range of industries, including automotive, retail and advanced robotics.

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Deepu Talla, Vice President of Robotics and Edge Computing at NVIDIA, states:
"Robots are evolving from task-based tools to intelligent collaborators. They will reshape how goods are made, improving efficiency and creating safer work environments for humans."

How to Learn and Harness It:

• Explore Robotics Platforms: Engage with platforms like NVIDIA Isaac Sim and ROS (Robot Operating System) to gain hands-on experience in building and programming intelligent robots.
• Join Robotics Communities: Participate in forums such as GitHub’s robotics repositories and ROS Discourse to collaborate on projects and learn from peers.
• Pursue Certifications: Enroll in certification programs offered by NVIDIA’s Deep Learning Institute or other institutions to validate your skills in robotics and edge AI.

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Healthcare-Data-Driven Breakthroughs

AI is set to revolutionize healthcare by enabling faster drug discovery, personalized treatments, and improved diagnostics. By leveraging proprietary medical data, AI models can uncover patterns and insights that drive innovation and enhance patient outcomes.

Digital health agents: The dawn of agentic AI and multi-agent systems will address the existential challenges of workforce shortages and the rising cost of care.

Administrative health services will become digital humans taking notes for you or making your next appointment — introducing an era of services delivered by software and birthing a service-as-a-software industry.

Patient experience will be transformed with always-on, personalized care services while healthcare staff will collaborate with agents that help them reduce clerical work, retrieve and summarize patient histories, and recommend clinical trials and state-of-the-art treatments for their patients.

Drug discovery and design AI factories: Just as ChatGPT can generate an email or a poem without putting a pen to paper for trial and error, generative AI models in drug discovery can liberate scientific thinking and exploration.

Techbio and biopharma companies have begun combining models that generate, predict and optimize molecules to explore the near-infinite possible target drug combinations before going into time-consuming and expensive wet lab experiments.

The drug discovery and design AI factories will consume all wet lab data, refine AI models and redeploy those models — improving each experiment by learning from the previous one. These AI factories will shift the industry from a discovery process to a design and engineering one.

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Kimberly Powell, Vice President of Healthcare at NVIDIA, highlights:
"Healthcare data has long been siloed and underutilized. With AI, we’re breaking down these barriers, enabling faster breakthroughs and ensuring better outcomes for patients worldwide."

How to Learn and Harness It:

• Study Bioinformatics and Medical AI: Platforms like edX and Coursera offer courses in bioinformatics and AI applications in healthcare.
• Engage with Healthcare AI Tools: Familiarize yourself with tools like NVIDIA’s Clara Discovery and AWS HealthLake to experiment with medical data analysis and drug discovery models.
• Understand Ethical Implications: Take courses on AI ethics, focusing on patient data privacy, bias reduction, and regulatory compliance to ensure responsible AI use in healthcare.

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Smarter Supply Chains in Manufacturing

Manufacturing is transitioning into smart factories, where AI optimizes every aspect of production, from equipment maintenance to supply chain logistics. Predictive maintenance, powered by LLMs, minimizes downtime by identifying potential equipment failures before they occur, while AI-driven supply chain management enhances efficiency and resilience.

Cheaper by the dozen: In addition to their smarts (or lack thereof), one big factor that has slowed adoption of humanoid robots has been affordability. As agentic AI brings new intelligence to robots, though, volume will pick up and costs will come down sharply. The average cost of industrial robots is expected to drop to $10,800 in 2025, down sharply from $46K in 2010 to $27K in 2017. As these devices become significantly cheaper, they’ll become as commonplace across industries as mobile devices are.

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Rev Lebaredian, Vice President of Omniverse and Simulation Technology at NVIDIA, explains:"AI-driven digital twins are helping manufacturers experiment and innovate like never before. What once took months can now be done in days."

How to Learn and Harness It:

• Understand Digital Twins: Enroll in courses on digital twin technology offered by platforms like Pluralsight or Siemens’ Mindsphere Academy.
• Work with IoT Devices: Gain practical experience with IoT sensors and software like Node-RED to collect and analyze real-time manufacturing data.
• Develop Automation Skills: Learn programming languages such as Python and MATLAB, essential for developing and implementing AI-driven automation solutions.

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‍Energy- Powering a Sustainable Future

The energy sector is leveraging AI to optimize grid management, integrate renewable energy sources, and forecast energy demand. AI models analyze data from sensors, satellite imagery, and historical consumption patterns to enhance efficiency and sustainability.

Welcoming the smart grid: Do you know when your daily peak home electricity is? You will soon as utilities around the world embrace smart meters that use AI to broadly manage their grid networks, from big power plants and substations and, now, into the home.

As the smart grid takes shape, smart meters — once deemed too expensive to be installed in millions of homes — that combine software, sensors and accelerated computing will alert utilities when trees in a backyard brush up against power lines or when to offer big rebates to buy back the excess power stored through rooftop solar installations.

Powering up: Delivering the optimal power stack has always been mission-critical for the energy industry. In the era of generative AI, utilities will address this issue in ways that reduce environmental impact.

Expect in 2025 to see a broader embrace of nuclear power as one clean-energy path the industry will take. Demand for natural gas also will grow as it replaces coal and other forms of energy. These resurgent forms of energy are being helped by the increased use of accelerated computing, simulation technology and AI and 3D visualization, which helps optimize design, pipeline flows and storage. We’ll see the same happening at oil and gas companies, which are looking to reduce the impact of energy exploration and production.

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Marc Spieler, Senior Managing Director of Global Energy Industry at NVIDIA, emphasizes: "The future of energy isn’t just renewable—it’s intelligent. AI is the key to achieving both efficiency and sustainability."

How to Learn and Harness It:

• Pursue Energy Analytics: Platforms like Skillsoft and Coursera offer courses on energy forecasting and smart grid technologies.
• Study Renewable Integration: Learn how AI integrates with renewable systems such as solar inverters and wind turbines through specialized courses and hands-on projects.
• Develop Coding Skills: Master languages like R and Python, which are crucial for modeling and analyzing energy systems.

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‍The AI Content Revolution

AI is revolutionizing the entertainment industry by enabling the creation of hyper-personalized content and enhancing audience engagement. LLMs trained on extensive libraries of scripts, music, and visual art allow creators to produce tailored experiences that resonate deeply with individual preferences.

Let AI entertain you: AI will continue to revolutionize entertainment with hyperpersonalized content on every screen, from TV shows to live sports. Using generative AI and advanced vision-language models, platforms will offer immersive experiences tailored to individual tastes, interests and moods. Imagine teaser images and sizzle reels crafted to capture the essence of a new show or live event and create an instant personal connection.

In live sports, AI will enhance accessibility and cultural relevance, providing language dubbing, tailored commentary and local adaptations. AI will also elevate binge-watching by adjusting pacing, quality and engagement options in real time to keep fans captivated. This new level of interaction will transform streaming from a passive experience into an engaging journey that brings people closer to the action and each other.

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Richard Kerris, Vice President of Media and Entertainment at NVIDIA, notes:
"AI is becoming the creative partner of the future. It’s not about replacing creators but empowering them to push boundaries."

How to Learn and Harness It:

• Engage with Creative AI Tools: Utilize platforms like NVIDIA’s GauGAN for generating realistic images or OpenAI’s ChatGPT for scriptwriting assistance.
• Study AI in Media Production: Take courses on AI applications in media production, including AI-driven editing and content personalization.
• Explore Interactive Media Development: Learn about virtual reality (VR) and augmented reality (AR) to create immersive, AI-enhanced entertainment experiences.

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Enhancing Connectivity with AI

Telecommunications providers are integrating AI to improve network reliability, optimize performance, and enhance customer service. AI-powered systems can predict network congestion, automate troubleshooting, and deliver personalized customer experiences.

The AI connection: Telecommunications providers will begin to deliver generative AI applications and 5G connectivity over the same network. AI radio access network (AI-RAN) will enable telecom operators to transform traditional single-purpose base stations from cost centers into revenue-producing assets capable of providing AI inference services to devices, while more efficiently delivering the best network performance.

AI agents to the rescue: The telecommunications industry will be among the first to dial into agentic AI to perform key business functions. Telco operators will use AI agents for a wide variety of tasks, from suggesting money-saving plans to customers and troubleshooting network connectivity, to answering billing questions and processing payments.

More efficient, higher-performing networks: AI also will be used at the wireless network layer to enhance efficiency, deliver site-specific learning and reduce power consumption. Using AI as an intelligent performance improvement tool, operators will be able to continuously observe network traffic, predict congestion patterns and make adjustments before failures happen, allowing for optimal network performance.

Answering the call on sovereign AI: Nations will increasingly turn to telcos — which have proven experience managing complex, distributed technology networks — to achieve their sovereign AI objectives. The trend will spread quickly across Europe and Asia, where telcos in Switzerland, Japan, Indonesia and Norway are already partnering with national leaders to build AI factories that can use proprietary, local data to help researchers, startups, businesses and government agencies create AI applications and services.

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Ronnie Vasishta, Senior Vice President of Telecoms at NVIDIA, states:
"AI is transforming telecommunications by making networks smarter and more responsive to both operator and customer needs."

How to Learn and Harness It:

• Study AI in Networking: Platforms like Coursera and edX offer courses on AI applications in telecommunications and network management.
• Work with AI-RAN Technologies: Explore AI-enhanced radio access networks (AI-RAN) through hands-on projects and industry partnerships.
• Focus on Network Security: Learn about AI-driven cybersecurity measures to protect telecom networks from emerging threats.‍

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Intelligent and Autonomous Vehicles

The automotive industry is embracing AI to develop more intelligent and autonomous vehicles. AI models are enhancing vehicle safety systems, optimizing traffic flow, and enabling autonomous decision-making in complex driving environments.

Pedal to generative AI metal: Autonomous vehicles will become more performant as developers tap into advancements in generative AI. For example, harnessing foundation models, such as vision language models, provides an opportunity to use internet-scale knowledge to solve one of the hardest problems in the autonomous vehicle (AV) field, namely that of efficiently and safely reasoning through rare corner cases.

Simulation unlocks success: More broadly, new AI-based tools will enable breakthroughs in how AV development is carried out. For example, advances in generative simulation will enable the scalable creation of complex scenarios aimed at stress-testing vehicles for safety purposes. Aside from allowing for testing unusual or dangerous conditions, simulation is also essential for generating synthetic data to enable end-to-end model training.

Three-computer approach: Effectively, new advances in AI will catalyze AV software development across the three key computers underpinning AV development — one for training the AI-based stack in the data center, another for simulation and validation, and a third in-vehicle computer to process real-time sensor data for safe driving. Together, these systems will enable continuous improvement of AV software for enhanced safety and performance of cars, trucks, robotaxis and beyond.

Xinzhou Wu, Vice President of Automotive at NVIDIA, remarks:"AI is at the heart of the next generation of autonomous vehicles, enabling safer and more efficient transportation systems."

How to Learn and Harness It:

• Pursue Automotive AI Courses: Enroll in specialized courses on platforms like Udacity and Coursera that focus on AI for autonomous driving and vehicle safety systems.
• Experiment with Simulation Tools: Use NVIDIA’s Drive Sim or other automotive simulation platforms to develop and test autonomous vehicle algorithms.
• Understand Vehicle Data Analytics: Learn how to analyze and interpret data from vehicle sensors and telematics systems to enhance AI-driven decision-making.

Sustainable AI Solutions

AI is pivotal in advancing sustainability within the energy sector. By optimizing power distribution, enhancing renewable energy integration, and reducing waste, AI helps create more sustainable and efficient energy systems.

Keith Strier, Vice President of Energy at NVIDIA, shares:"AI is enabling us to meet energy demands sustainably. From wind turbines to solar farms, data-driven insights are helping us scale renewable energy faster than ever."

How to Learn and Harness It:

• Focus on Sustainable Energy Courses: Platforms like Coursera and edX offer courses on sustainable energy systems and the role of AI in enhancing their efficiency.
• Engage with Renewable Energy Projects: Participate in hands-on projects that integrate AI with renewable energy technologies, such as solar and wind power.
• Develop Skills in Energy Modeling: Learn how to use AI tools for energy modeling and simulation to optimize power distribution and resource allocation.

Embracing the AI-Driven Future

As we approach 2025, the integration of AI and data lakes is not merely a technological upgrade—it signifies a fundamental shift in how industries operate and innovate. With $88 trillion worth of goods and services produced globally each year, the ability to harness AI-driven insights from vast data lakes is becoming a critical

Manuvir Das aptly concludes:
"We’re standing at the crossroads of data and reasoning, and the possibilities are endless.

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