Introduction to the Artificial Intelligence (AI) & Machine Learning Industry

Artificial intelligence (AI) spending worldwide was estimated at $1.5 trillion for 2025 by analysts at Gartner and is expected to grow to $2.02 trillion by the end of 2026. This is an estimate on a broad basis that includes servers/chips, AI models, software and services. Plunket Research estimated a similar U.S. market for AI at $550.0 billion for 2026.

In many ways, AI is a logical extension of recent technology trends. A combination of ever-more-powerful computer chips, cloud computing, continued miniaturization of devices such as sensors, and the growth of the big data/data analytics sector are major enablers of AI on what is now a cost-effective basis.

AI, and the related machine, learning are creating vast changes in nearly all segments of business and industry over the mid-term. The effect of AI on consumers and households has been in broad evidence for many years, although the people benefiting from such technologies may not be aware of the process or the significance of what’s going on around them.

For example, utilizing machine learning, Amazon.com pioneered the development of advanced software that learns from a shopper’s actions online and then makes excellent product recommendations tailored to the individual. In its early years, Netflix famously offered a $1 million prize to anyone who could engineer an algorithm that would learn from a subscriber’s movie rental habits in a manner that would increase the accuracy and usefulness of its online recommendation engine by 10%. The more that Amazon or Netflix can display perfectly curated products for individual shoppers, the happier the consumer and the greater the amounts of sales completed. (Yes, Netflix paid off on this Progress Prize offer, selecting the work of a team of engineers that called themselves “BellKor’s Programmatic Chaos.”) Also, even the earliest robo-vacuum cleaners for home use relied on machine learning to map and navigate homes. In a similar, but much more advanced way, industrial robots map the warehouses from which they select and pick up products and components.

Also, in recent years, search engines like Google and Bing utilized similar technology to serve up billions of dollars’ worth of online ads weekly to carefully targeted readers of news, entertainment and data online. These recommendation engines run in the background 24/7; they learn more and more as time goes by and interactions with consumers increase; they benefit from frequent, incremental improvements made by software engineers; and they make the owners of these technologies highly efficient, effective and profitable in their business operations. These experiences helped give these companies experience and background that assisted them in rapidly developing AI models, software and generative AI services once the true AI boom started. (OpenAI, the developer of the ChatGPT generative AI services and related software tools, released its first software toolkit for developers at the end of 2015. ChatGPT was first released to the public on November 30, 2022, sending interest in AI soaring worldwide.)

Today, Amazon’s incredibly popular cloud computing subsidiary, Amazon Web Services (AWS), offers tools to enable companies of all sizes to quickly build machine learning and AI offerings and tools. AWS also offers easy-to-deploy AI-based tools for speech recognition, image recognition personalization engines, face recognition, forecasting and much more. Many other firms including Microsoft and Google also offer rapid-deployment AI platforms so that institutions and corporations of all types can easily put AI to work.

Consider the implications of machine learning for critical industrial processes. For example, airlines around the world spend hundreds of millions of dollars monthly on fuel. Imagine the benefit, both financially and in terms of reduced carbon emissions, if the air transport sector can reduce fuel usage a mere five percent through the utilization of machine learning—determinizing the most efficient air routes in light of current weather, setting the optimum engine speeds for fuel efficiency and assigning the most efficient flight paths in and out of airports by computer-aided air traffic controllers. Airlines thereby reduce both total time in the air and total fuel burnt. This is but one possibility from hundreds of thousands of potential applications—virtually all factory, supply chain and transportation sectors can benefit through such uses of AI.

Many consumers utilize ChatGPT and similar services when they want quick answers, as an alternative, as an alternative to relying on such search engines as Google and Bing. However, Google quickly created an AI-based answers feature at the top of most of its search results pages, This affords users a rapid, direct answer, while at the same time enabling them to link to other sites for further research. (AI is utilized exclusively if the user clicks “AI Mode” from the Google search home page.)

Meanwhile, the most popular software suites have added built-in AI tools, such as the Copilot feature in Microsoft Office 365 and Gemini AI tools in Google’s wide array of creativity software for email, spreadsheets, word processing and more. In other words, businesspeople, professionals and ordinary consumers alike are now surrounded by AI tools, agents and platforms, from their desktop PCs to their smartphones. The most popular Software-as-a-Service tools, including Salesforce, Adobe and ServiceNow have placed agentic AI features on top of their platforms. These agents or virtual assistants are integrated directly into the software and its underlying data, in order to analyze, reason and even perform multi-step tasks.

Technologies that Have Deep Synergies with Artificial Intelligence

Autonomous Vehicles

Big Data and Data Mining

Cloud Computing

Digital Assistants (Siri, Alexa, etc.)

Electronic Games

Energy Management and Conservation

Fraud Prevention Systems

Generative AI-- Text/Images/Code Creation

Insurance and Credit Risk Modeling

Internet of Things (IoT)

Internet Search

Investment Modeling

Tools for Movie and other Media Production

Pharmaceuticals Research

Predictive Analytics

Predictive Marketing

Robotic Process Automation/Customer Service

Robotics and Automation

Search Engines

Semiconductors

Sensors and Wireless Networks

Supercomputing

Voice, Image and Facial Recognition

Source: Plunkett Research, Ltd.

How AI works: Simply put, AI and machine learning work by finding patterns in data. The larger the pool of data, the more observable the patterns and the better the accuracy and outcomes of the machine learning (ML) process. To some extent, AI often simulates human brain-like functions such as learning, problem-solving, reasoning and perception. This means that this technology can greatly speed up human or robotic tasks, by completing or enhancing work, and do it at blinding speed.

ML is a key component of AI and involves training algorithms to learn from data and make predictions or decisions without being explicitly programmed in how to do so. ML algorithms are exposed to large datasets and then discern patterns from the data, enabling the algorithms to improve their performance over time. For example, ML might study a dataset containing the results of treatments of specific types of medical patients. By observing those results, the machine will see patterns and can be programmed to predict outcomes of one type of treatment compared to another type. There are several varieties of ML, including supervised learning, unsupervised learning and reinforcement learning. This study of datasets during ML is considered to be “training” the AI system involved.

Technologies commonly used in AI include neural networks, which are computational models inspired by the structure and function of the human brain. Neural networks consist of interconnected nodes (neurons) organized into layers that are capable of learning complex patterns from data. Deep learning is a subset of neural networks that involves training deep AI architectures with many layers, enabling them to learn hierarchical representations of data. Other AI technologies include natural language processing (NLP), which focuses on enabling computers to understand and generate human language; computer vision, which involves teaching computers to interpret and analyze visual information; and robotics, which combines AI with mechanical systems to create intelligent machines capable of performing physical tasks.

The “World Model” AI Alternative

The World Model for artificial intelligence is an alternative to the large language models (LLMs) utilized by most major platforms, including ChatGPT. World models attempt to create a data and analysis platform that simulates the physics, conditions and dynamics of an actual environment, such as the Earth, or a local or regional environment. The goal is to apply human-like reasoning while learning from cause-and-effect, real-time, multi-modal data, while providing more accurate and actionable data for the user. The output could hopefully be used in planning, 3-D or video visualization, design and strategic management.

In summary, AI works by simulating human intelligence through techniques such as ML, neural networks, and other technologies. ML, in particular, plays a crucial role in AI by enabling algorithms to learn from data and improve (train) their performance over time. The main point is that AI software can be trained by being constantly fed data, queried as to its meaning and receiving feedback as to the accuracy or usefulness of its responses. It is essentially training a machine to respond correctly to data of a given nature or to data within a given set of circumstances.

AI is growing worldwide at a staggering pace. Alphabet’s Google reported processing AI tokens (a token is a fundamental unit of data such as a word, part of a word or a character that large language models (LLMs) use to process, comprehend and generate language) at a rate of 9.7 trillion per month in late 2024. By October 2025, the rate hit 1.3 quadrillion tokens per month. Part of the reason for this monumental rise is the need to fight hallucinations (incorrect or nonsensical outputs from AI engines). Most cutting-edge models research the same query multiple times before issuing a response and/or consult a variety of sources. AI is now producing more reliable answers to questions but is utilizing far more resources to do so.

Industry Sectors with Significant Benefits from Artificial Intelligence and Machine Learning

(The higher the amount, recency and frequency of data available, the more useful the outcomes from applying AI to such data. Health care is a perfect example, with vast amounts of patient and outcome data captured daily on a global basis.)

Health Care

= Disease diagnosis and analysis of scans, samples and symptoms

= Pharmaceuticals Research

= Recommendations for optimum treatment and patient care

Internet, Media, Search and Digital Content or Productivity Tools

= Internet search and online advertising

= Writing reports and other communications, responding to emails

= Summarizing meetings and interviews

= Market research

= Production of graphics, video and art

Back-office and Professional Tasks

= Accounting and bookkeeping

= Human resources functions, including hiring

= Legal research and document creation

= Engineering support

Technology-Related Tasks

= Writing and testing software code

= Designing and testing systems, processes and circuits

Agriculture

= Enhancement of precision Agriculture, for efficient planting, irrigation and harvesting

= Prediction of weather

Transportation

= Providing traffic flow management

= Enabling self-driving (autonomous) cars and trucks

= Optimizing operations for aircraft, truck fleets, railroads and ships

Energy Efficiency and Production/Environmental Controls

= Developing technologically advanced “smart cities” and green buildings

= Improving energy efficiency in air conditioning, lighting and other systems

= Improving operations and outcomes at all types of energy production operations, from selecting better sites for drilling oil wells to gaining optimum output from windmills

= Enhancing air and water quality monitoring and control

Manufacturing

= Reducing plant downtime

= Increasing efficient use of materials and personnel

= Optimizing actions of robotic equipment

Financial Services

= Better analyzing risk for insurance underwriting

= Analyzing optimum investments for specific goals

= Approving loans and controlling credit risk

Supply Chain

= Optimizing timing of orders and shipping

= Reducing inventory wastage and delays

Entertainment and Publishing

= Writing/research/content creation

= Graphics and video creation

= Editing and production

Cybersecurity

= Providing the ability to rapidly analyze and react to potential cybersecurity threats

= Providing advanced techniques for user identity and digital log-on to accounts

Source: Plunkett Research Ltd.

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