What is AI (artificial intelligence)?

AI is a catch-all term for tools and systems built to perform tasks that typically require human intelligence, such as understanding language or recognizing patterns. AI gives machines the ability to understand, communicate, learn, problem-solve, and create. AI is the product of several disciplines, including computer science, data science, linguistics, neuroscience, philosophy (particularly the study of logic), and psychology.

>AI represents a transformative paradigm in data analysis, enabling insights at a scale and speed that far exceed human capabilities. Today, enterprises worldwide use AI for tasks such as data retrieval, correlation, and processing. It’s integrated into daily applications such as observability, cybersecurity, customer experience, and risk management.

In 1950, Alan Turing (yes, the famous WWII codebreaker) published "Computing Machinery and Intelligence," which advanced the notion of machine intelligence.¹ Then, in 1956, John McCarthy founded the Dartmouth Summer Research Project on Artificial Intelligence, coining the term artificial intelligence and cementing AI as a field of study.²

ELIZA, the first chatbot, was created in 1966 by Joseph Weizenbaum.³ ELIZA used natural language processing (NLP) to mimic human interaction and trick users into believing it was a psychotherapist.

Then came the 80s, a decade known for the "AI boom."⁴ This era focused on expert systems — rule-based programs mimicking decision-making — which sparked excitement about the potential of machine intelligence, even though deep learning and large language models (LLMs) were still decades away.

After a period of stagnation in the late 80s — caused by high expectations, limited computing power, and disappointing results — enthusiasm and funding dried up. This period came to be known as the "AI winter," which lasted until a resurgence of interest in the late 1990s and early 2000s introduced AI agents and enhanced automation capabilities. From the first Roomba in 2002 and NASA's Spirit and Opportunity rovers landing on Mars in 2004 to machine learning (ML)-based recommendation engines by social media platforms and Netflix, AI was everywhere. 2011 saw two major AI achievements: IBM's Watson and Apple's Siri. Just like that, AI was a part of daily life — but it was still mostly controlled by engineers and enterprises.

Fast-forward to 2022: The launch of ChatGPT by OpenAI made generative AI easy and accessible to the general public. While Google's BERT5 launched in 2018, ChatGPT was a global game-changer, spurring a new age of innovation. Some might call this the new AI boom, marking a significant milestone in the adoption of AI.

AI is important because it is the foundation of a significant portion of modern tech innovation. While not all new tech uses AI, AI is often used behind the scenes. As the amount of data produced and consumed grows and digital ecosystems become increasingly complex, organizations can struggle to see clearly into their operations.

AI can be an invaluable tool to assist organizations in performing complex data-related tasks at scale, when used correctly. Sophisticated AI technology can improve the speed and accuracy of data-related operations that drive business decisions. For example, in demand forecasting, AI can analyze historical sales data to predict how much inventory to order. At its best, AI drives innovation by transforming how we work and live.

As adoption continues, AI has the potential to take over mundane and repetitive tasks, freeing humans to take on more valuable tasks that require creative and critical thinking. However, AI is only as good as the data it's trained on and the sophistication of the technology.

AI functions by employing machine learning, deep learning, natural language processing, and neural networks to analyze data, recognize patterns, and simulate aspects of human cognition.

Machine learning

Machine learning uses algorithms to learn from data by recognizing patterns and relationships between data points, and it uses them to improve performance on tasks over time. This basic principle enables computers to make predictions in new contexts. Machine learning is behind AI's ability to recognize and respond to sentiment and adapt to new data or changing environments. It is responsible for AI capabilities such as sentiment analysis, anomaly detection, image recognition, and predictive analytics.

Organizations can use pretrained machine learning models with new data or train their models from scratch.

Natural language processing

Natural language processing (NLP) is a form of AI that relies on machine learning algorithms, deep learning, and computational linguistics to teach computers human (or natural) language. NLP is a foundational layer of the technology that enables speech recognition, semantic search, chatbots, and more language-related applications.

Thanks to NLP, computers can recognize, process, understand, and generate human language.

Neural networks

Neural networks are a type of machine learning algorithm that enables deep learning. These algorithms are modeled on the structure of the human brain (hence “neural”) and feature advanced pattern recognition technology. Layers of nodes analyze complex and vast datasets.

The multiple layers between a deep neural network's input and output layers enable deep learning. This, in turn, allows data features to be extracted automatically. While not exact replicas of the brain, neural networks are simplified computational models inspired by its structure and function, enabling powerful pattern recognition.

Deep learning

Deep learning is a subset of machine learning that uses multilayered neural networks to automatically learn and extract features from large amounts of data. These models are inspired by the structure of the human brain and excel at recognizing complex patterns.

AI training and fine-tuning

AI training refers to the process of training machine learning algorithms to learn statistical patterns from data. Fine-tuning teaches models to perform specific tasks.

First comes AI training, in which the algorithm is fed a dataset and then learns to make predictions through one of several training techniques:

• Supervised learning is a pattern recognition process. Labeled data is fed into a machine learning algorithm that recognizes the relationships between data points and their labels. By learning statistical associations between these relationships, it can predict the correct labels to similar data points.
• Unsupervised learning refers to training algorithms on unlabeled data to find hidden patterns or structures. Tasks include clustering (such as grouping similar items) and dimensionality reduction (such as simplifying data while preserving specific features).
• Semi-supervised learning is a hybrid of supervised and unsupervised learning, in which a model is taught with both labeled and unlabeled data. The aim is to improve model performance on tasks such as classification or regression.

Once a model has been trained, teams can start fine-tuning it. Tasks like anomaly detection or sentiment analysis are often approached by fine-tuning existing pretrained models. This is faster, less resource-intensive, and more cost-effective.

While AI relies on its training and fine-tuning to deliver accurate, relevant results, it most importantly relies on the quality of the data it is trained on. This is why the process of training and fine-tuning begins with data collection and preprocessing. And the more thorough this process is, the better the outcomes of AI.

While AI comprises several technologies, there are also many types of AI.

Narrow AI

Narrow AI is trained to perform very specific tasks. It is the most used type of AI today. Think voice and speech recognition systems, basic chatbots (typically rule-based or retrieval-based, unlike generative models that create original content), and recommendation systems (like the ones used by streaming services).

General AI

General AI is a theoretical type of AI that can learn, understand, and adapt to perform different tasks across a broad, or general, set of use cases.

We can better illustrate the concept of general AI by comparing it to robotics for a practical example, where most systems are still limited to narrow and predefined capabilities. Robots that can adapt and adjust their actions based on their environment rely on computer vision and some pretraining to understand their surroundings, but they are limited to familiar circumstances, settings, and parameters.

For instance, a robotic arm that has been programmed to pour the contents of a can of soda into a cup can only do so with fixed parameters (i.e., the same type of soda can, in the same room, with a consistent volume of liquid). If it were handed a glass bottle, put in a different room with a higher table, or given a champagne flute rather than a cup, it would fail. General AI would allow the robotic arm to conceptually understand the task of pouring a drink and adapt its behavior to new containers, environments, and constraints.

Generative AI

Generative AI distinguishes itself by its ability to generate, or create new and original text, code, software, images, and videos. Generative AI relies on machine learning to adapt and learn from its interactions with users. It also uses LLMs, NLP, neural networks, vector databases, and several types of algorithms to analyze prompts and produce responses.

Generative AI models are trained on large datasets to learn relationships between various data points. This type of pattern recognition is what enables generative AI models to make predictions when prompted or queried. So, when generative AI generates a response in the form of new content, this response is the product of a very sophisticated prediction.

Deep dive into 15 generative AI use cases.

When trained well, AI offers significant benefits to enterprises and individuals across industries. Its powerful automation capabilities drive efficiency and accuracy in diverse applications, enhance customer experiences through personalization, and help organizations manage costs by optimizing operations.

• Improved efficiency: AI-powered automation is a breakthrough for organizations working to improve efficiency. Whether used by site reliability engineers (SREs) to help monitor and optimize performance or by customer service representatives to field queries so employees can focus on high-priority cases, AI’s ability to process vast amounts of data at scale is key to improving efficiency.
• Better business decisions: If using a sophisticated, well-trained model, AI data analysis capabilities at scale can greatly support human capabilities. In the case of root cause analysis or risk management, AI can deliver more accurate insights, minimizing human error. This translates to better data-driven business decisions.
• Enhanced customer experiences: AI-powered search can offer customers sophisticated, conversational search experiences that deliver relevant results. From chatbots available 24/7 to AI-enabled personalization capabilities, customers benefit from faster service, tailored experiences, and more efficient problem-solving, enhancing their overall satisfaction and engagement with brands. Learn how to build a digital customer experience strategy powered by AI.
• Cost savings: By improving efficiency and accuracy, optimizing operations, and building greater brand loyalty with enhanced customer experiences, AI ultimately has a positive impact on the bottom line. In fact, 82% of IT leaders believe they can increase revenue with their ability to ingest data in real time, use data analytics tools for business decision-making, and use AI for data-driven insights.

Despite its numerous advantages for organizations and users and the rapid pace of development and adoption in recent years, AI still has limitations and poses challenges that warrant discussion.

• Bias: AI is only as good as the data it is trained on. And there is an inherent danger of training AI on biased data, especially data that people do not identify as biased. This can reinforce unconscious biases and, in application, have devastating consequences. Consider healthcare organizations that rely on AI to process claims or law enforcement organizations that use AI in criminal investigations. Some AI systems used in resume screening have exhibited bias, penalizing resumes with terms associated with women and downgrading candidates from women's colleges.
• Ethical concerns: AI raises many ethical questions. Is the AI trained on private data or data that is the intellectual property of individuals who have not explicitly given their consent? Is it a facial recognition model trained on street camera footage without people's knowledge? Generative AI, in particular, has raised many questions relating to copyright infringement and stolen likenesses. Responsible and ethical use of AI is an ongoing concern for regulators and organizations.
• Job displacement: While AI is meant to be used as a tool to augment human skill, its automation capabilities are already leading to job displacement in many industries, from entertainment to manufacturing.
• Explainability: Deep learning models are extremely complex algorithms. They function like black boxes. This makes it difficult to understand their decision-making process and, therefore, check the validity or accuracy of their responses.

These challenges require careful consideration on the part of individual users, organizations, and governments. AI has the potential to drive tremendous democratization and inclusivity. To fully capitalize on these benefits, decision-makers must promote and enforce responsible and ethical use.

Organizations have applied AI across a wide range of industries and use cases.

Enterprise AI applications

For organizations, AI is revolutionizing operations, cybersecurity, and decision-making.

• Data analytics: AI-powered data analytics are the lifeblood of observability and cybersecurity. AI's ability to analyze vast and diverse sets of data allows organizations to correlate information across different domains for better business insights and improve anomaly detection and root cause analysis.
• Customer service: Chatbots and virtual assistants help customer service agents triage tickets and offer personalized service around the clock. AI can be used for recommendation engines or conversational search features that improve customer engagement and overall results relevance.

Industry-specific AI uses

Some industries have been using AI for decades, while others, like manufacturing and healthcare (especially patient-facing healthcare), are now beginning to implement AI to address their unique challenges.

• Healthcare: AI is used for diagnostics, personalized treatment planning, and accelerating drug discovery processes.
• Finance: Fraud detection systems, algorithmic trading, and credit risk assessments are all enhanced by AI's ability to process and analyze complex financial data.
• Manufacturing: AI powers robotics to take over dangerous and repetitive tasks and enables predictive maintenance, quality control, and supply chain optimization.
• Retail: AI provides personalized product recommendations and demand forecasting, enhancing customer experiences and streamlining operations.

Consumer AI applications

From "Hey, Siri!" to "Alexa, play …," AI has become an integral part of people's daily lives. Behind them: AI.

• Smart home devices: From browsing the internet to turning lights on or off, AI-powered home devices like Google Home and Amazon Echo bring automation directly to consumers.
• Personalized streaming: Platforms like Netflix and Spotify use AI recommendation algorithms to tailor content to individual preferences.
• Fitness and health apps: AI-powered tools offer coaching, activity tracking, and personalized fitness plans to help users achieve their wellness goals.

As organizations continue to integrate traditional AI and adopt generative AI, the future is teeming with possibilities: new capabilities, new products, and new and transformative technological advancements. The future also calls for responsible AI development, ensuring ethical use, fairness, and transparency as adoption accelerates globally.

Generative AI, in particular, is set to redefine content creation, product design, and problem-solving. Meanwhile, general AI — still a distant goal — remains the ultimate aspiration for scientists and innovators, who envision a unified AI "brain" capable of executing diverse tasks and controlling multiple machines.

Search AI, the fusion of search technology and artificial intelligence, promises to improve knowledge-sharing and operational efficiency, streamlining internal processes and customer experience. While search revolutionized information retrieval through its ability to instantly return relevant results from massive datasets, it can still struggle to fully understand context and generate deeper insights. And while AI excels at analyzing complex patterns and generating insights, it might lack precision in finding and accessing specific information within vast data stores. When you combine AI with search technologies, you get the best of both, creating the unique ability to transform underutilized, unstructured data into the answers you need.

Elastic's Search AI Platform, an end-to-end solution, transforms the exponential growth of underutilized, unstructured data into the right answers, impactful actions, and meaningful outcomes that can help organizations make better decisions, maximize efficiency, drive innovation, elevate customer experiences, improve operational resilience, and mitigate security risk.

The Search AI Platform serves as the foundation to Elastic's two out-of-the-box solutions — Elastic Observability and Elastic Security. It's the platform of choice for developers seeking to build next generation, generative AI powered applications and services. The platform is open by design, built for performance, and wired for innovation.

Discover Elastic Search AI.

• AI in business comprehensive guide
• AI in cybersecurity comprehensive guide
• AI Adoption in cybersecurity: Top use cases and mistakes to avoid
• How to build a digital customer experience strategy powered by AI
• AI in government
• What is search AI?
• What is generative AI?
• What is conversational AI?

How is AI used in search and discovery?

Modern search systems use AI to move beyond keyword matching, analyzing semantics, context, and user behavior. AI models prioritize relevant results and surface insights that might otherwise go unnoticed, which is critical in both consumer applications and enterprise environments.

Additionally, AI improves search by interpreting language more precisely, taking into account context, phrasing, and user behavior. With AI, systems can surface relevant information even when queries are incomplete or ambiguous. This is especially useful in large or complex datasets.

Transform unstructured data into a strategic edge with Search AI.

What's the difference between AI and machine learning (ML)?

While AI covers a range of technologies from rule-based systems to robotics, ML specifically uses statistical methods to learn from data. AI is the broader field that includes any method of simulating human-like intelligence in machines. ML is a data-driven approach within AI, where algorithms learn from experience rather than relying on hard-coded rules. 

Deep dive into the differences of machine learning vs. AI.

What is generative AI and how is it different from traditional AI?

Unlike traditional AI, which identifies or responds based on inputs, generative AI produces entirely new outputs. It learns structure, style, and relationships from data and uses that to generate novel results. Traditional AI typically focuses on analysis, classification, or prediction. Generative models expand AI's role from decision support to content creation.

1. A. M. Turing, "Computing Machinery and Intelligence," Mind 49: 433–460, 1950.

2. Dartmouth, "Artificial Intelligence Coined at Dartmouth," 1956.

3. Joseph Weizenbaum, Communications of the ACM, "ELIZA—a computer program for the study of natural language communication between man and machine," 1966.

4. Thomas Haigh, "Historical Reflections: How the AI Boom Went Bust," 2024.

5. Devlin, Jacob, "BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding," 2019.