What Is Artificial Intelligence (AI)?

Artificial intelligence (AI) has been described as a set of technologies that gives computers vision and lets them understand written and spoken language; as machines capable of performing functions usually associated with human minds; as the simulation of human intelligence; as an unfathomably large potential boon to human productivity; and as the possible doom of humankind.

In short, AI is at the center of several raging business and societal debates—but the terms of those debates have shifted dramatically. For more than a century, AI had captured the imaginations of a small group of philosophers, science-fiction fans, mathematicians, and computer scientists. Now, three years past the debut of ChatGPT, businesses of all sizes are actively implementing AI solutions, from chatbots and predictive analytics to autonomous agents that can complete complex multistep tasks.

For small to medium-sized businesses (SMBs), however, the questions have become practical and urgent: Which AI tools should we implement first? How do we integrate AI into our existing workflows? And how do we scale AI capabilities as they become more sophisticated? These are questions born not of curiosity but of competitive necessity.

This article addresses those practical questions with practical answers. It explains the highlights of AI’s history, what it can do for SMBs, how it works, and the business benefits it can bring.

What Is Artificial Intelligence?

AI is a set of technologies and practices meant to create computer-based systems capable of performing tasks that previously relied on human intelligence. These tasks range from recognizing patterns and making predictions to understanding language and solving complex problems.

But what they all have in common is that they require making decisions based on information. Therefore, all AI systems include data and algorithms that process and, sometimes, act on the data. Some AI systems, such as generative AI chatbots, are trained on so much data that it becomes a newsworthy talking point. But the multitude of AI systems that are more narrowly focused on individual business tasks need only enough data to address the job at hand—which may still be a large volume of data by everyday standards.

AI algorithms are so numerous and varied that they are tricky to discuss at a high level. But they all share the ability to recognize patterns in data, make decisions or predictions based on that information, and, in many cases, learn from the quality of their decisions. It is how each algorithm works that makes the primary differences among many of the AI technologies and approaches you’ve likely heard of, such as machine learning (ML), deep learning, natural language processing (NLP), computer vision, and the neural networks that underlie most of them. These technologies enable machines to understand—and generate—speech, recognize images, and make autonomous, problem-solving decisions.

Any discussion about AI systems must also include the human factor. People create and fine-tune the software that embodies the algorithms, design the hardware to run the software, judge the quality of AI systems’ output, and, often, provide feedback that helps the systems improve.

Key Takeaways

• AI can enable computers to learn, solve problems, and make decisions.
• Integrating AI capabilities into business technology solutions can help companies automate more operations, gain better insights from their data, and improve customer experiences.
• The future of AI is being shaped by several advancements and the increasing importance of human-AI collaboration, both of which present opportunities and challenges for businesses.
• Agentic AI systems are being developed that can autonomously manage entire business workflows.
• As AI moves forward, businesses that effectively harness its capabilities and adapt to the changing landscape will be better positioned to thrive than those that fall behind.

Artificial Intelligence Explained

Why should businesses, particularly SMBs, care about AI? The reason is because a multitude of narrowly focused AI algorithms have been embedded in, or added to, practical business applications that can help improve efficiency, reduce costs, and drive growth. Business software with AI capabilities can help SMBs automate more repetitive tasks, gain valuable insights from data, and enhance customer experiences in ways that, before AI, were open only to bigger companies with deeper pockets.

For example, AI-powered customer service chatbots and virtual assistants can handle basic customer inquiries 24/7, freeing up human staff to focus on more complex issues. They can also learn from customer interactions to provide more personalized and efficient support over time. Today’s AI-enhanced chatbots are far more sophisticated than the rules-based systems that frustrated customers in earlier years; they’re capable of understanding context, handling complex queries, and even detecting customer sentiment.

In marketing and sales, AI algorithms can analyze customer data to identify patterns and preferences, enabling businesses to create targeted marketing campaigns and personalized product recommendations that can lead to higher conversion rates and increased customer loyalty. And that, of course, means more revenue.

Internally, AI can help companies improve automation in data entry and inventory management tasks, for example, reducing the risk of human error and saving time. It can also assist with financial management and accounting functions, such as demand forecasting, budgeting and planning, and producing financial statements. Increasingly, AI agents—systems that can autonomously plan and execute multistep tasks—are being deployed to handle complete workflows, from processing invoices to onboarding new employees.

How Does AI Work?

SMBs owners and managers should understand two important aspects about how AI works. One is about how AI works in the big picture—where does its ability to analyze company information and help businesses make better-informed decisions come from? The other is how they are likely to experience AI, in action, in their own organizations.

How AI Works: The Big Picture

AI works by processing data, identifying patterns in the data, using the patterns to make decisions or predictions, getting feedback on the quality of its choices—and iterating that process hundreds, thousands, or millions of times. Let’s break that into steps:

1. Data input: AI systems generally need training or a combination of careful instruction and data access before they can be released into the world. Either way, that means large datasets. The data can come from a combination of different sources, such as internet-connected sensors, company databases, and user interactions. And it can have multiple modalities, such as text, images, audio, and video.
2. Initial processing: Whatever its type, real-world data is usually messy. It must be preprocessed to remove irrelevant and redundant information and transformed into a format that the AI system can understand and analyze. This usually involves techniques like data cleaning and normalization.
3. Algorithm writing/selection: The heart of any AI system is its algorithms—the mathematical models and instructions that tell the system how to process and learn from the data. Historically, there have been many different types of AI algorithms, but today most AI systems use one or more of the various approaches to ML, including deep learning and neural networks. ML algorithms also include ones that train the AI system (discussed later in this article). Each approach—and, of course, each individual algorithm—has its own strengths and weaknesses that can make it better or worse at any given task.
4. Training: This involves feeding the data into the AI system (which may include dozens of algorithms) and allowing it to learn and adjust its internal parameters to better fit the patterns and relationships within the data.
5. Testing and validation: After training, the AI system is tested on data it has never seen before to evaluate its performance and accuracy. This helps to determine that the system has not simply memorized the training data but can generalize to new situations. If the system’s performance is not satisfactory, it may need to be retrained with more data or its algorithm(s) may need to be revised.
6. Deployment: The AI system can now be deployed into a production environment to make predictions or decisions based on real-world data.

Throughout the entire process, human oversight and intervention are crucial. Data scientists and AI experts are involved in selecting and preparing the data, choosing the appropriate algorithms and fine-tuning the system’s performance. They also monitor the system’s outputs for accuracy.

Bear in mind that all of this is still rapidly evolving. As AI technologies continue to advance, new approaches and architectures are emerging that can handle more complex tasks and larger datasets. For example, the AI “transformer” architecture was introduced in a June 2017 academic paper and, based on that, the generative pretrained transformer (GPT) model was first described a year later in a June 2018 paper. Since then, GPTs have pushed the boundaries of what AI can do in terms of understanding and generating language, making ChatGPT possible only four years and five months later, in November 2022.

Despite these ongoing advancements, AI systems are still limited by the quality of the data they are trained on, the quality of the data they are given to act on in business applications, and the potential inherent biases and assumptions built into data and algorithms.

How AI Works for SMBs

In practice, SMBs experience AI in two ways: through the enhanced capabilities and efficiencies it brings to their everyday tools and processes and through standalone AI software, such as ChatGPT, Claude, and other models, the public versions of which have been rapidly adopted by businesses. For example, Verizon’s “2025 Mobile Security Index” report found that 93% of responding organizations report that employees have incorporated generative AI tools on mobile devices into their daily workflows. However, the report also found that only half of business organizations have formal guidelines in place to govern the safe use of GenAI.

AI algorithms are usually integrated into various business applications that organizations are familiar with and, often, already using. For example, AI may already be integrated in relatively subtle ways into software-as-a-service (SaaS) applications, such as customer relationship management (CRM), marketing automation, and accounting software, since many SaaS providers have begun to incorporate AI capabilities. A CRM system might use AI algorithms to analyze customer data and provide personalized recommendations for sales and marketing strategies. A business manager simply interacts with the CRM interface, while the AI works in the background to process data and generate insights.

Other products that SMBs may use, or may want to consider using, make more ambitious use of AI capabilities, including recent advances in GenAI. Companies are still figuring out how to incorporate these capabilities, so many different approaches are emerging.

Weak AI vs. Strong AI vs. Superintelligence

One of the ways that philosophers and computer scientists have long categorized AI is “weak” or “strong.” Weak AI is everywhere, referring to the scope of what an AI system can do, not the quality with which it performs its tasks. All AI systems used in businesses today are narrowly focused on specific functions or capabilities, rather than on trying to replicate general human intelligence. Even large language models (LLMs) are considered weak/narrow, despite their wide-ranging knowledge, because they are limited to language. Their abilities cannot be extrapolated to visual understanding, motor control, or complex decision-making, and they don’t learn and adapt the way human intelligence can.

No instances of strong AI, also called artificial general intelligence (AGI), are known to exist—which is ironic in that AGI has been discussed consistently in science fiction and news media, garnering the most societal debate since the emergence of GenAI. An AGI would, theoretically, be equivalent in intelligence to the smartest humans. Debate continues in scientific communities about how close to AGI some AI systems may come.

Beyond AGI lies an even more theoretical concept: artificial superintelligence (ASI), also called superintelligence. ASI refers to a hypothetical AI system that would vastly surpass human cognitive abilities across all domains—from scientific reasoning and creative problem-solving to emotional intelligence and strategic thinking. Superintelligence is firmly in the realm of speculation.

For SMBs, understanding the distinction between weak AI, AGI, and superintelligence is important context. The business applications available today, which are all forms of weak AI, are already powerful enough to transform operations. The key is to focus on implementing proven, narrowly focused AI solutions that can deliver measurable value now, while staying informed about how the broader AI landscape evolves.

What Can AI Do?

People tend to anthropomorphize—that is, project human attributes onto—just about everything, including AI. So many people think of AI as “thinking.” But it does no such thing. What does AI actually do? Here are eight key features, which can be incorporated into business applications in ways that lead to potentially significant benefits—and, sometimes, to the uncanny sense that the software can actually think.

• Analyze data: If data is the new oil, then AI can be a major refinery. AI algorithms can process and analyze vast amounts of structured and unstructured data much faster and more accurately than humans. This enables them to discover hidden patterns and trends that can inform business intelligence and company decision-making processes. AI-powered data analysis can be applied to virtually any field.
• Automate processes: From data entry and document processing to inventory management, warehousing, and accounting, AI can automate repetitive and time-consuming tasks, freeing up human resources to focus on work that adds significant business value. AI-powered automation can improve efficiency, accuracy, and productivity across various industries.
• Detect objects and patterns: Humans are excellent pattern-recognizers, but AI can do it with far more data than we can keep in our heads—and many times faster. AI algorithms can identify and recognize objects, patterns, and anomalies across visual, textual, and numerical data. AI-powered computer-vision and image-recognition technologies can accurately identify and classify objects, faces, and patterns in images and videos, which is why it is so useful in security and surveillance, medical imaging, and autonomous vehicle applications. Beyond visual data, AI algorithms can be used in business analytics to detect patterns and anomalies in large datasets. It can help identify fraudulent transactions in financial data, recognize spam or malicious content in emails, and discover trends in customer-behavior data.
• Personalize recommendations: AI can analyze user behavior and/or purchase data and provide personalized recommendations and experiences. This capability is widely used in ecommerce, streaming services, and content platforms to improve user engagement and satisfaction.
• Translate languages: AI-powered language translation tools can instantly translate text and speech from one language to another, with increasing accuracy. This can help managers and executives communicate across borders and cultures, whether they are traveling abroad or doing business with international partners.
• Generate text and images: AI tools that can generate human-like text and realistic images based on written prompts and examples took the world by storm. From art and design to content creation and beyond, some business analysts believe GenAI has the potential to add trillions of dollars to the global economy annually by increasing knowledge worker productivity. Tools like ChatGPT and Claude for text generation, Perplexity for research, and Midjourney and DALL-E for image generation have shown GenAI’s potential.
• Summarize data and text: AI can automatically summarize large volumes of data and text, extracting key points and insights. In a world drowning in information, this capability can be a lifeline. It makes it far easier for researchers and business managers, for example, to cut through the details of large documents or datasets and get to the heart of what matters to their organizations.
• Converse in natural language: AI algorithms can make computer systems understand speech. This capability lies at the core of tools like AI-powered chatbots and virtual assistants. But, over time, all kinds of information technology systems can benefit from integrated algorithms that let people interact with them in natural language.

Types of AI

There are many ways to categorize AI systems. The weak versus strong AI discussion above is one. But AI systems can also be differentiated by training method, capability level, and the underlying algorithm’s core approach. Complicating the matter is that over the course of an 80-year history, various avenues of AI research appeared to reach dead ends, only to be “rediscovered” years, or even decades, later—often with different names.

Here is a useful way to categorize AI systems based on the types widely in discussion now:

1. Decision trees

   are a prime example of a pre-deep-learning/neural network approach to AI algorithms. Tree models make decisions based on a series of questions; so-called “random forests” are collections of decision trees working together that are more accurate than individual trees. Symbolic regression, genetic algorithms, and Bayesian networks are other examples of different algorithmic approaches to the challenge of building machines that learn. These AI approaches, and many more, continue to operate in products today.

2. Machine learning

   is a subset of AI at the same level as all the approaches in the first category, but it’s the big one—at least for now. ML encompasses a multitude of algorithms and statistical models that enable computers to improve their performance on a task through experience, without being explicitly programmed. The next two categories, below, are specialized applications of ML. And so are many AI systems that are trained to perform narrow, well-defined functions using large, structured datasets (i.e., data that is labeled and usually organized in rows and columns). ML techniques are used in most modern AI systems.

3. Deep learning

    is a subset of ML that can take advantageous use of neural network architectures with more than two “hidden” layers of artificial neurons. Depending on the complexity of the use case, deep learning neural networks can have up to hundreds of layers. Although AI systems using pre-deep-learning approaches, such as decision trees, could perform image recognition and process natural language, newer AI systems using deep-learning ML techniques and running on neural network architectures outperform them. Self-driving cars, however, are an example of a use case that became possible only through deep learning.

4. Generative AI

    uses a subset of deep-learning ML technologies and deep neural networks to construct AI models that, once trained, can rapidly create content in response to text prompts. Different GenAI tools can produce new audio, image, and video content, but it is the text-oriented conversational AI of LLMs that has generated the most excitement. GenAI models represent a significant advance in AI because they exhibit many AI capabilities that begin to bridge the gap between weak and strong AI systems, including natural language understanding and generation, knowledge synthesis, problem-solving across multiple domains of expertise, and complex reasoning. Consequently, people can converse with and learn from advanced GenAI models in pretty much the same way they do with humans.

The Relationships Among AI, ML, and Deep Learning

Approaches to AI Model Training

SMBs are unlikely to work directly on training AI models. But it is nonetheless important to understand AI training because, without it, an AI system would be virtually useless. Training is where AI models learn to perform specific tasks by absorbing information from examples—that is, data, and lots of it. The quality and quantity of data used in training, as well as the choice of training approach—different training methods are suited to different types of AI models, tasks, and data—significantly affect the resulting AI system’s performance. For these reasons, selecting the appropriate training approach is key to building effective AI applications.

But why choose only one? The most practical applications of ML tend to use a combination of techniques instead of relying on one approach. There are five main approaches to training AI models:

1. Supervised learning: In this method, AI systems are trained using labeled data, meaning that both input data and the correct output are provided. The AI learns to map inputs to outputs based on the examples it is shown. Supervised learning is commonly used for tasks such as image classification, sentiment analysis, and predictive modeling, where a target variable or outcome to predict is clear.
2. Unsupervised learning: AI systems trained with unsupervised learning are given unlabeled data and must identify patterns, structures, or relationships on their own. The AI learns to group similar data points together or detect anomalies without being explicitly told what to look for. This approach is often used for tasks like customer segmentation, anomaly detection, and data compression, where the goal is to discover hidden data patterns or insights.
3. Reinforcement learning: In reinforcement learning, AI systems learn through trial and error, receiving “rewards” or “punishments” based on their actions. The AI learns to make decisions that maximize its cumulative reward over time. One technique, known as reinforcement learning through human feedback (RLFH), has recently come to public prominence because it played a crucial role in the development of GenAI. In RLFH, human feedback helps the AI create a reward model that represents human preferences and values, which then become part of the model’s output when it is put to work on its assigned task(s). Reinforcement-learning techniques are commonly used in game-playing AI and robotics applications; recommendation systems, such as those used by Netflix and Amazon; and certain self-driving car technologies.
4. Semi-supervised learning: This is a hybrid that combines elements of supervised and unsupervised learning. In this technique, AI systems are trained using a small amount of labeled data along with a larger amount of unlabeled data. The AI learns to generalize from the labeled examples and to leverage the structure in the unlabeled data to improve its performance. This is particularly useful when labeled data is scarce or expensive to obtain, as it allows the AI to learn from a combination of labeled and unlabeled examples. It’s usually used in combination with one or more of the other training models for applications in robotics, text and image classification, recommendation systems, autonomous vehicles, and more.
5. Transfer learning: With transfer learning, AI systems use knowledge from one training task to accelerate learning on a different but related task. Developers start with a model that has been trained on a large dataset—such as one that recognizes objects in millions of photographs—and then retrain only the final layers of the model to adapt it for a more specific purpose, such as identifying manufacturing defects or classifying medical images. This reduces both the amount of training data needed and the time required to train for the new task. Transfer learning is used in computer vision applications—such as quality-control systems and product recognition—and in NLP tasks, like customer sentiment analysis and document classification.

Benefits of AI

The list of benefits that AI systems can bring to a business is long, varied, and still growing. Keep in mind that these advantages stem directly from AI’s ability to recognize patterns, make decisions or predictions, and learn by reviewing their performance—all of which humans do as well or better. But because of their computational power, AI systems can reach these conclusions much faster than humans and do so while analyzing many times more data.

• Improves accuracy: AI systems process and analyze vast amounts of data with a high degree of precision, reducing the risk of errors and inconsistencies. For example, AI-powered medical tools can analyze patient data and imaging results to provide more accurate diagnoses, while AI-based fraud detection systems can identify suspicious transactions with greater reliability than human analysts.
• Increases efficiency: By automating repetitive and time-consuming tasks, AI can help businesses and individuals work more efficiently and productively. For instance, AI-based document processing tools can extract relevant information from large volumes of text, saving time and effort.
• Enhances decision-making: AI’s ability to analyze many times more data than people can lead to better-informed decisions. AI systems, particularly in fields like healthcare, finance, and logistics, are assisting in decision-making through advanced predictive modeling and data analysis.
• Offers high availability and scalability: These generally are benefits of the underlying IT infrastructure that supports an AI system rather than of the AI itself. Nonetheless, because AI systems for SMBs are almost always deployed as cloud-based software, it is the case that they can operate 24/7 and are always available when needed. This is particularly valuable in industries such as healthcare, where AI embedded in monitoring systems can continuously track patient vital signs and alert medical staff to potential issues, or in customer service, where AI chatbots can provide around-the-clock assistance. On the scalability side, the same cloud infrastructure makes AI systems easy to scale up or down to accommodate changing demands and workloads.
• Personalizes results: AI algorithms can analyze customer data and preferences to provide highly personalized experiences and recommendations. This is evident in applications such as streaming services, where AI algorithms suggest content based on a user’s viewing history, or in ecommerce, where AI-powered product recommendations are tailored to individual shopping behaviors and interests.
• Reduces repetitive tasks: Business applications with embedded AI capabilities can automate mundane and repetitive tasks, allowing humans to focus on more creative or strategic value-added activities. AI-capable apps can handle data entry, invoicing, and other routine administrative tasks, while content moderation systems with AI can automatically flag and/or remove inappropriate material from online platforms.
• Converses with humans: NLP capabilities can enable any business system to engage in natural, conversational interactions with workers, providing information, assistance, and support. This can improve accessibility and make new forms of human-machine collaboration possible. For example, workers in assembly plants can already get real-time guidance from AI systems via augmented reality headsets, and AI-powered virtual health assistants are interacting with patients via voice and text messages to help them stay on track with their medication regimens.
• Creates computer code: Generative AI systems can assist in writing and optimizing software code, boosting developer productivity and reducing errors. AI-powered code completion tools can suggest relevant code snippets and functions as developers type, and AI-based code optimization systems can automatically refactor and streamline existing codebases.
• Accelerates innovation: AI can speed up research and development processes, leading to faster innovation. Because they can process large volumes of scientific data rapidly, AI systems are accelerating discovery in fields from pharmaceuticals and material science to astrophysics. Similarly, AI can accelerate creative processes by collaborating with human workers to generate ideas, designs, music, art, and even literature.
• Refines risk mitigation: AI’s predictive capabilities can help identify and mitigate potential risks before they become problems. For example, an AI system could analyze data related to weather, geopolitics, and transportation routes to predict potential supply chain disruptions. Business managers could identify alternative suppliers, routes, etc., in advance.
• Optimizes predictive maintenance: In industries that use heavy machinery, such as manufacturing, AI can predict when machinery or equipment will fail or require maintenance, helping to prevent breakdowns before they happen. This reduces downtime and improves operational efficiency.
• Improves accessibility: AI can make services and information more accessible to people with disabilities. A website’s map of bus routes, for instance, could contain AI-generated text aligned with the images that describe the routes in detail. That could be paired with NLP to read the route descriptions out loud for visually impaired people.
• Leads to better healthcare outcomes: In medicine, AI can lead to earlier disease detection and more personalized treatment plans.

Examples of AI Technologies

AI research and evolution have produced many different, specialized technologies, each with its own distinct applications and potential to reshape industries. The following nine AI technologies are some of the most impactful and widely adopted.

• Computer vision:

  Computer vision enables machines to understand visual information from the world around them. It is used in facial recognition, object detection, and image classification applications. In retail stores, for example, it’s used in automated checkout systems and inventory management (for example, by deducting an item from inventory as it is purchased). It’s also a crucial component for autonomous vehicles. In security, it powers surveillance systems; in healthcare, it aids in medical imaging analysis and disease diagnosis.

• Weather modeling:

  AI algorithms analyze vast amounts of meteorological data, including satellite imagery, radar, and historical weather patterns, to generate accurate and detailed weather forecasts. Beyond meteorologists, weather models are used by farmers to optimize crop planting and harvesting, by utilities to anticipate energy demand, and by emergency services organizations to better prepare for and respond to severe weather events. Some large businesses incorporate weather data into their demand forecasting analyses, a practice likely to become available to SMBs, too, as the technology becomes less expensive and easier to use.

• Autonomous vehicles:

  Self-driving cars, trucks, drones, and other autonomous vehicles rely on a combination of AI technologies, including computer vision, sensor fusion (integrating data from multiple sensors to create a more comprehensive and accurate understanding of the surrounding environment), and decision-making algorithms to navigate roads (and skies) safely without human intervention. This technology is being developed and tested by automotive manufacturers, technology companies, and transportation services. The potential benefits include reduced traffic accidents, increased mobility for elderly and disabled individuals, and better traffic flow in cities.

• Fraud detection:

  In fraud detection systems, AI algorithms analyze patterns of user behavior to find anomalies that suggest fraudulent activities and then try to prevent the associated action, either on their own or by alerting human agents, depending on the situation. This technology is widely used in banking, insurance, and ecommerce. It helps financial institutions protect customers’ assets, reduces business losses due to fraud, and helps organizations comply with industry regulations. In ecommerce, AI-powered fraud detection can reduce chargebacks and enhance trust in online transactions.

• Speech recognition:

  Speech recognition technology uses AI algorithms to convert spoken language into written text. It’s often included as an initial step in an NLP application but shouldn’t be confused with that broader technology (see below). To avoid such confusion, it’s often called speech-to-text technology. By either name, it is used in applications like dictation, virtual assistants, and customer service automation. In telecommunications, it powers voice-activated dialing and customer support. In healthcare, it enables voice-to-text transcription of medical notes and hands-free documentation. In automobiles, speech recognition is a part of voice-controlled navigation and entertainment systems.

• Natural language processing:

  NLP is an AI technology that enables computers to understand, interpret, and generate human language. While speech recognition focuses on the acoustic-to-text conversion, NLP is meant to achieve—and output—a deeper understanding of language content, regardless of whether it originated as speech or text. It’s used in applications such as sentiment analysis, text summarization, and automated translation from one language to another. In marketing, NLP helps companies analyze customer feedback and social media mentions. In media, NLP assists in content recommendation systems and automated news aggregation. In finance, it powers the analysis of financial reports and market sentiment for investment decisions.

• Virtual assistants:

  Virtual assistants are AI-powered software agents that can understand natural language commands and perform tasks on behalf of users. They are fairly sophisticated, able to handle complex, multistep tasks that require contextual understanding, and they can learn from their interactions to improve future performance. They are used in smartphones, smart home devices, and enterprise software. In healthcare, for example, they can engage in natural conversations with patients, collect symptom information, and assist doctors in making diagnoses. In education, virtual assistants can offer personalized learning experiences and answer student queries. In corporate settings, they can manage schedules, set reminders, and control conference systems. Virtual assistants can even personalize their interactions, adapting to individual user preferences over time.

• Chatbots:

  Think of chatbots as virtual assistants’ younger cousins. They are simpler AI-based conversational interfaces that can interact with users through text or voice, answering easy questions, providing information, and completing tasks. They excel in scenarios with predictable patterns of customer interaction and repetitive tasks. In customer service, for example, chatbots handle routine inquiries, such as checking the status of an order or explaining the differences among standard service plan offerings. In ecommerce, they can guide customers through the purchasing process and present personalized recommendations (that were determined by a separate AI system). Banking chatbots assist with account inquiries and transaction details. Telecommunications companies employ chatbots for troubleshooting common issues, account management, and service inquiries.

• AI agents:

  Think of AI agents as the ultimate virtual assistant. But instead of responding to individual commands or prompts, AI agents combine reasoning, memory, and autonomous decision-making to take actions that aim to achieve specific objectives with little or no human intervention. Agents can complete multistep processes end-to-end, such as drafting and sending marketing emails, updating a CRM, monitoring email open rates, and refining future campaigns. Businesses are embedding AI agents into applications for lead qualification, order management, expense approvals, and customer support. As adoption grows, these systems are evolving from simple task-handlers into collaborative agents that work together to automate increasingly complex workflows.

Generative AI

GenAI, of course, is the groundbreaking subset of AI that can create new, original content rather than only analyze or act on existing data. Unlike other AI technologies designed for specific tasks, like image recognition, language translation, or decision-making, GenAI can produce virtually any kind of text, image, music, speech, or other type of content that is like what a human might create, and do it all in response to text prompts.

At the heart of GenAI are LLMs and deep-learning algorithms that do the heavy lifting of enabling machines to understand and generate human-like content. GenAI models are trained on really large amounts of data so that they can learn the patterns, styles, and structures of different types of content and then use that knowledge to generate new, coherent outputs based on user prompts or other input parameters.

Market researchers, including McKinsey & Company, have said that GenAI has the potential to enhance human productivity to the tune of trillions of dollars per year in the coming years through enhanced creativity and collaboration. Rather than replacing human workers, GenAI can serve as a powerful tool to augment and accelerate human capabilities. For example, writers are using GenAI to brainstorm ideas, overcome writer’s block, and produce drafts that they can then refine and edit. Designers are using it to create multiple variations of a design concept, explore new styles, and automate repetitive tasks. Researchers are employing it to summarize large volumes of text, generate hypotheses, and identify patterns in complex datasets.

As GenAI technologies continue to advance, they have the capacity to transform the way people work, learn, and create. At the same time, however, GenAI has raised questions about intellectual property rights, the role of human creativity, and the potential for misuse.

Agentic AI

Agentic AI is an evolved use of GenAI that has captured the imaginations of businesspeople since discussion began in early 2024. Multiple research firms have declared agentic AI to be the #1 strategic technology for 2025. The excitement stems from the fact that, unlike GenAI tools, which react to human prompts, agentic AI tools can make proactive decisions without human intervention. For example, a business could, at least theoretically, build an agentic AI system, tell it a business goal, and let the system independently plan, execute, and adapt until it achieved that goal.

Since early 2024, many organizations have turned that theory into reality. For example, a bank studied by McKinsey was in the process of modernizing 400 legacy applications when it decided to switch from a human developer approach to a multiagent approach, which put the human developers in supervisory roles. Doing so cut the time and effort for the transition by 50%. Separately, a market research firm deployed a multiagent approach for its data quality-control operations and experienced a 60% productivity gain. It expects to save $3 million annually. Though such multiagent approaches are popular, it’s also possible to achieve quality results by having a single agent review and revise its own work from multiple perspectives.

AI researcher Andrew Ng is credited with popularizing “agentic AI” as both a concept and its label through a series of 2024 articles and talks that described its potential. Ng’s thinking starts with the “zero-shot” nature of conventional GenAI tools, such as chatbots. By this he means that the user gets one chance to write a prompt that evinces a usable result from the GenAI tool. To overcome the zero-shot limitation, AI researchers developed “chain of thought” prompting, which, for example, lets a GenAI tool produce a first result, then critique it, fix the issues, and only then share the result with the user. Ng’s idea was to apply a kind of parallel processing, similar to that used when microprocessors are banded together into a larger computational engine. By having multiple AI agents performing portions of a task independently and then sharing their results, an agentic AI system can rapidly produce sufficient high-quality information to make an independent choice.

“Tool use” is another critical aspect of Ng’s agentic AI vision. It’s the idea that an AI agent can manipulate specialized software tools, which could include other agents, to achieve specific objectives. A tool might, for example, search the web; another might operate a computer—a skill that opens up a vast potential for agentic autonomy.

Though the major AI technology companies are racing to simplify agentic systems, they are, at present, challenging to implement. Current agentic systems may use five or more distinct AI technologies and an orchestration layer that coordinates the agents’ activities. Plus, they must be integrated with a business’s existing systems so that they can access necessary data. Finally, one implementation element that no amount of technology simplification can eliminate is business process reinvention. To gain the most from agentic systems, businesses must be willing to rethink their processes as well as the ways that their human employees interact with AI systems.

AI Use Cases

AI technologies are being applied across a wide range of industries, sometimes simply enhancing the efficiency of existing operations and other times transforming how businesses operate and the way they deliver value to customers. Here are some of the most prominent AI use cases in eight important industries.

1. Retail

   Ever since Amazon pioneered the use of AI-powered recommendation engines roughly 20 years ago to analyze customer data and provide personalized product suggestions, their use has become widespread in the retail industry. The recommendations that come from AI analysis of customers’ purchase history and browsing behavior can not only help increase sales, but customer engagement, too. AI algorithms can also predict future demand for products by analyzing historical sales data, weather patterns, and other relevant factors, helping retailers optimize inventory management and reduce waste. Additionally, AI-powered computer-vision technologies enable automated checkout systems that allow customers to shop without waiting in line (theoretically), while also reducing labor costs for retailers. With coming agentic systems, these discrete functions could evolve into a series of autonomous agents that can manage complete customer journeys—from personalized product suggestions to inventory optimization and pricing adjustments.

2. Healthcare

   In healthcare, AI systems are powering faster and more accurate diagnoses and treatments. AI algorithms can analyze medical images, such as X-rays and MRIs, to detect abnormalities and assist clinicians in diagnosing diseases. AI can also accelerate new drug discovery by analyzing biomedical data, identifying potential drug candidates, and predicting the efficacy and safety of new medications—all of which should reduce the time and cost for pharmaceutical companies to bring new drugs to market. Furthermore, AI can analyze patient data, including genetic information and medical history, to develop personalized treatment plans that can lead to improved patient outcomes.

3. Finance

   In the finance sector, AI-powered systems analyze monetary transactions in real time, identifying patterns and anomalies that suggest fraudulent activity and, thus, helping financial institutions prevent losses and protect customers. Emerging agentic AI systems in finance could execute multistep processes, such as loan application reviews, coordinating document verification, credit checks, and approval workflows for routine cases. AI algorithms can also analyze market data, news sentiment, and other factors to make split-second trading decisions, optimizing portfolio performance and reducing risk. Likewise, AI is able to analyze alternative data sources, such as social media activity and mobile phone usage, to assess credit applicants’ creditworthiness. Again, this reduces risk for the lenders, but, in this case, it also broadens accessibility to credit markets for people with limited credit histories.

4. Logistics

   AI is helping the logistics industry improve efficiency and reduce costs. AI algorithms can analyze traffic patterns, weather conditions, and other factors to refine delivery routes, reducing fuel consumption and improving on-time performance. AI can also analyze sensor data from vehicles and equipment to predict when maintenance is needed, reducing downtime and extending asset life. Furthermore, AI-powered robotics and computer-vision technologies can automate warehouse operations, such as picking and packing, improving efficiency and accuracy while reducing labor costs.

5. Media

   Media companies are using AI algorithms to analyze user preferences and engagement data and provide personalized content recommendations. This approach has been shown to increase customer retention. AI-powered tools are also assisting news organizations in generating articles, summaries, and even videos, enabling them to scale content production and reach new audiences. As it does for other industries, AI can analyze customer data to identify distinct audience segments, which is beneficial for targeted advertising and personalized content delivery.

6. Cybersecurity

   AI is playing an increasingly important role in cybersecurity by enabling faster threat detection and response. AI algorithms can analyze network traffic and system logs to identify potential security threats, so that cyber defenses—whether also automated or initiated by human analysts—can respond faster, reducing the risk of a successful data breach. They’re especially useful when it comes to so-called zero-day threats, which, by definition, have never been seen before. AI can also learn normal user behavior patterns and then detect deviations from those norms, which may indicate insider threats or compromised accounts. Furthermore, AI can prioritize and automate the deployment of security patches based on their vulnerability level and potential impact on the business, reducing the window of exposure to the most dangerous cyber threats.

7. Manufacturing

   For manufacturers, AI is driving improvements in efficiency, quality, and productivity. AI agents can analyze sensor data from production equipment to predict maintenance needs, automatically schedule downtime, order replacement parts, and coordinate technician assignments—handling the entire workflow from start to finish. AI-powered computer-vision systems can inspect products for defects, improving quality control and reducing the need for manual inspections. AI also can analyze historical production data, sales trends, and external factors to predict future demand for products, helping manufacturers strengthen production planning and inventory management.

8. Energy

   AI is helping the energy sector optimize operations, reduce costs, and improve sustainability. AI can analyze sensor data from power-generation equipment to predict when maintenance is needed, reducing downtime and extending asset life. AI algorithms can also analyze historical consumption data, weather patterns, and other relevant factors to predict future energy demand, so that utilities can prepare for the necessary power generation and distribution. Moreover, AI can analyze real-time data from smart meters and other Internet of Things devices to optimize power flow and reduce transmission losses, improving grid efficiency, and reliability.

Key Dates in AI Development

Across a half century from the 1950s to the early 2000s, AI made slow and uneven progress. The AI community often made great promises but was unable to deliver, leading to cynicism about the technology. And apparent breakthroughs, like human-competitive chess-playing programs, didn’t generalize well to practical business problems. That said, by 2000 AI had practical value in a wide range of applications. Mostly, these involved training a computer program on a fairly narrow task using what is now considered medium-sized data, such as 10,000 to 100,000 examples. This level of AI is built into many products already in widespread use.

Starting in the early 2000s, one specific approach to AI started to advance rapidly: neural networks. These are multilayered networks of artificial neurons encoded in software. To envision them, imagine the familiar spreadsheet but in three dimensions because the artificial neurons are stacked in layers similar to how real neurons are stacked in the brain. They also mimic the way that the connections between brain neurons have different strengths. Neural networks are a very old technology, dating back to the 1950s; during their rapid progress in the 2000s, some of their practitioners rebranded them as “deep learning.”

Here are the key dates that tell the story of how AI emerged into the world:

• 1943, Artificial neuron: Warren McCulloch and Walter Pitts publish “A Logical Calculus of the Ideas Immanent in Nervous Activity,” introducing the concept of artificial neurons.
• 1950, Turing Test: In his seminal paper “Computing Machinery and Intelligence,” Alan Turing proposes “the imitation game,” which later became known as the Turing Test, as a way to determine whether a machine can think. In the game, a human tries to distinguish between a computer and a human based solely on their responses to questions.
• 1955, AI coined: The term “artificial intelligence” is first used in the title of a grant proposal to support the Dartmouth Summer Research Project on Artificial Intelligence, which took place the following year. The proposal was written by four people who would become legends in computer science and AI: John McCarthy, then an assistant professor of mathematics at Dartmouth; Marvin Minsky, a Harvard math professor; Nathaniel Rochester, head of information research at IBM; and Claude Shannon, the Bell Labs mathematician who founded information theory. The event marked the birth of AI as a field of study and came to be known as “the Dartmouth Conference.”
• 1958, Perceptron: Cornell Aeronautical Laboratory research psychologist Frank Rosenblatt develops the Perceptron, an early artificial neural network capable of learning and recognizing simple patterns. It had a single hidden layer between its input and output layers.
• 1959, MIT AI Project: McCarthy and Minsky, who both moved to the Massachusetts Institute of Technology the prior year, co-found the MIT Artificial Intelligence Project.
• 1960, LISP: McCarthy publishes his design of LISP in the April edition of Communications of the ACM (the Association for Computing Machinery). It quickly becomes the most popular programming language for AI research and applications.
• 1962, Backpropagation: Rosenblatt introduces the concept of “back-propagating error correction.” Decades later, the concept becomes crucial to ML and generative AI.
• 1963, Stanford Artificial Intelligence Lab (SAIL): McCarthy, who moved to Stanford University in 1962, founds SAIL; it is still in operation. Today, MIT and Stanford remain among the top U.S. universities for AI research.
• 1966, ELIZA: MIT computer science professor Joseph Weizenbaum debuts ELIZA, considered to be the first chatbot, which simulates conversations a person might have with a psychotherapist. Although it sounds impressively human, ELIZA is entirely rules-based; it parses a user’s input into keywords and then chooses a matching response from a preprogrammed library.
• 1969, Neural nets fall from favor: Minsky, along with Seymour Papert, both of whom are now co-directors of the MIT Artificial Intelligence Laboratory (a successor to MIT’s AI “Project”), publish Perceptron. The authors argue that neural networks like the Perceptron are a dead end and that the future of AI lies in symbolic systems. This fuels a long-running controversy among AI researchers and discourages research in neural nets.
• 1970, Backpropogation rediscovered: Finnish mathematician and computer scientist Seppo Linnainmaa reintroduces the AI research community to the idea of backpropagation, which he describes as the “reverse mode of automatic differentiation” in his master’s thesis.
• 1974, Expert systems emerge: Standford publishes a paper on Mycin, one of the earliest expert AI systems, which encodes physicians’ knowledge about antimicrobial therapies and recommends treatment for infectious diseases.
• 1978–1986, XCON savings boosts expert systems: XCON (for eXpert CONfigurer), another early AI expert system, is written in 1978 by Carnegie Mellon University professor John P. McDermott. Its goal is to help the university configure Digital Equipment Corporation’s (DEC) VAX computers, which was a tricky challenge in those days. DEC began using the program internally in 1980; by 1986 it was estimated to be saving the company $25 million ($72 million in 2024 dollars) annually, mainly from reduced configuration errors. XCON’s success was one reason expert systems came to dominate AI research in the 1980s.
• 1986, Backpropagation brings back neural nets: The article “Learning representations by back-propagating errors,” by David Rumelhart, Geoffrey Hinton, and Ronald Williams, is published in Nature, applying backpropagation algorithms to multilayer neural networks. Over the next decades, the combination of backpropagation and neural nets becomes the basis of machine learning, leading to multiple AI breakthroughs—including GenAI.
• 1980s, RNNs: Recurrent neural networks—a neural net architecture that incorporates feedback mechanisms like those thought to occur in the human brain—emerge. Though the inherent concepts have been around since the early 1900s, the 1980s brought working RNN models that are still in use.
• 1988, Statistical language translation: IBM researchers introduce statistical machine translation (SMT). Based on information theory and using Bayes’ Theorem, SMT makes a major improvement over the rules-based language translators of the time. Although it did not use neural nets, SMT’s use of large datasets and probabilistic algorithms laid a foundation for those techniques to reemerge in modern language translation systems that are built on neural nets.
• 1990s, CNNs: Convolutional neural networks grew out of attempts to refine RNNs. CNNs specialize in grid-like data and are, therefore, great at spatial data representations—they can generate pictures. Today’s popular text-to-image GenAI apps use CNNs as one of multiple neural net models.
• 1997, Deep Blue beats Kasparov: IBM’s Deep Blue chess-playing computer defeats world champion Garry Kasparov in a six-game match, marking a significant milestone in AI’s ability to compete with humans in complex tasks.
• 2005, DARPA Grand Challenge: Stanley, an autonomous vehicle developed by Stanford University’s Racing Team, wins the DARPA Grand Challenge, successfully navigating a 132-mile desert course without human assistance.
• 2011, Watson wins Jeopardy!: IBM’s Watson defeats two human champions in a televised game of Jeopardy!. Although Watson did not use deep learning and neural networks—it had a one-off software architecture specialized for question-answering—it showcased the power of NLP and led to surging interest in AI.
• 2011, Siri: Apple introduces Siri, a virtual assistant that uses speech recognition and NLP to interact with users and perform simple tasks on iOS devices.
• 2016, AlphaGo: Google DeepMind’s AlphaGo AI system defeats world champion Lee Sedol 4-1 in a five-game match of Go, demonstrating the potential of deep learning and reinforcement learning in tackling complex problems.
• 2017, The Transformer breakthrough: Transformer, a deep-learning neural network architecture whose “self-attention” mechanism eliminates the need for recurrence in neural nets, is introduced in the paper “Attention Is All You Need,” authored by eight employees and former employees of Google Brain and Google Research. The transformer breakthrough is that it can process sequential data, such as text, in a massively parallel fashion without losing its understanding of the meaning in the sequences. The parallel processing of sequential data revolutionized NLP and powered the creation of today’s LLMs.
• 2018, The GenAI breakthrough: In June 2018, four OpenAI researchers publish “Improving Language Understanding by Generative Pre-Training,” which describes how they combined generative pretraining with a transformer model to create the LLM that is known today as GPT-1.
• 2020, GPT-3: OpenAI releases GPT-3, then the largest and most powerful LLM.
• 2022, ChatGPT: In November, OpenAI launches ChatGPT, based on GPT-3.5. This highly capable conversational AI chatbot captures public attention and sparks discussions about the potential and implications of advanced LLMs and GenAI.
• 2023, GPT-4: OpenAI releases GPT-4 in March, representing a major leap in reasoning capabilities and the first model to pass challenging professional exams, such as the bar, at human-expert levels.
• 2024, Claude 3 family: Anthropic releases Claude 3 (Opus, Sonnet, Haiku) in March, with Claude 3 Opus matching or exceeding GPT-4 on most benchmarks. This begins a period in which new models coming to market consistently leapfrog each other.
• 2024, Agentic AI emerges: At his BUILD 2024 keynote in June, Andrew Ng introduces agentic AI to describe autonomous systems that independently plan and execute multistep workflows. Ng’s writings and talks give shape and definition to the nascent field. By year’s end, market researchers declare agentic AI as the top strategic technology trend for 2025.
• 2024, Reasoning models: OpenAI releases its o1 reasoning model in September, followed by o1-pro in December. These models use extended “thinking time” before responding—sometimes reasoning for minutes rather than seconds—achieving PhD-level performance on physics, chemistry, and math benchmarks. This is only the first of what becomes a fundamental architectural shift toward deliberate reasoning.
• 2024, Claude computer use: In October, Anthropic releases Claude’s computer use capability in public beta. The system can control desktop computers—moving cursors, clicking buttons, typing text—to complete complex tasks across multiple applications autonomously, marking a major step toward AI agents that interact with software like humans do.
• 2025, Claude Sonnet 4.5: Anthropic releases Claude Sonnet 4.5 in September, advancing coding capabilities and multistep reasoning. The model demonstrates significant improvements in computer use tasks and becomes widely adopted for agentic applications.

The Future of AI

AI technologies have been developing at a breathtakingly fast pace—partly because they can contribute to their own development—and are likely to continue doing so for the foreseeable future. The trajectory of that development appears to be moving toward increasingly autonomous, specialized, and deeply integrated AI systems. For businesses, planning for AI is becoming a strong strategic imperative. But it’s not just about adoption. It’s about thoughtfully deploying AI by redesigning business functions and operations around AI capabilities.

In the next year or so, businesses’ main activity will likely center on transitioning AI experiments and pilots to full production systems. As always in such technology transitions, a percentage won’t succeed. In the case of agentic AI, for example, the complexity required to coordinate multiple specialized agents, provide consistent performance, integrate with legacy systems, and maintain appropriate human oversight is likely to exceed many organizations’ expectations. Organizations that succeed will be those that approach AI deployment with realistic timelines, strong governance frameworks, and a willingness to iterate based on real-world performance.

During that same time frame, reasoning models that deliberate before responding—sometimes “thinking” for minutes to solve complex problems—will likely become mainstream, complementing the more familiar quick-response models. Context windows will continue expanding from thousands to hundreds of thousands of tokens, enabling AI to analyze entire documents, codebases, or datasets in a single interaction. Perhaps most significantly, AI systems will begin routinely controlling desktop computers directly, manipulating software interfaces the way humans do rather than requiring custom integrations for every application. Anthropic already has such a capability in beta test.

For SMBs, this period will bring great opportunity. Cloud-based platforms from major vendors will begin to make enterprise-grade AI accessible at SMB price points, and the ecosystem of prebuilt agents and templates will help reduce SMB implementation barriers.

By the late 2020s, the AI landscape may be characterized by a spectrum of specialized solutions rather than general-purpose tools, much like today’s enterprise software market. Organizations will assemble teams of AI agents from marketplaces and vendor catalogs, selecting specialized capabilities for legal research, financial analysis, customer service, software development, supply chain optimization, and dozens of other functions. Just as businesses today license different SaaS applications for different needs, they’ll deploy different AI agents with distinct expertise.

During this period, the most successful organizations will likely be those that redesign their workflows from scratch around AI capabilities, rather than merely automating existing processes. New professional roles may emerge and proliferate. Think: Agent managers who supervise squads of AI systems rather than human teams, focusing on goal-setting, performance monitoring, and exception handling; AI integration specialists who design the workflows that coordinate multiple agents toward business objectives; and ethics and compliance officers who confirm that agent behavior aligns with company values and evolving regulations.

Looking toward the 2030s, AI capabilities that seem remarkable today will be commonplace. AI embodied in robotics could finally achieve the reliability and cost-effectiveness needed for widespread deployment in physical industries like manufacturing, logistics, and agriculture. Entirely new AI architectures beyond today’s transformer-based models may emerge, potentially offering capabilities we can barely conceptualize today. What seems certain is that the AI systems of the 2030s will be substantially more capable than today’s technology—able to handle increasingly complex reasoning, operate autonomously across longer time horizons, and collaborate with humans in ways that feel more like partnerships than tool use.

The 2030s will bring a much higher bar for rate of progress—organizations that fall behind the progress curve will go out of business much faster.

The Future of AI’s Implications for Business Leaders

For business decision-makers, several strategic principles emerge from this future trajectory of AI:

• Speed matters, but thoughtfulness matters more: Organizations rushing to deploy AI without clear use cases, governance frameworks, or change management processes will likely join the ranks with canceled projects. Those that move deliberately—identifying high-value applications, establishing baseline metrics, and building organizational capability—will capture sustainable advantages.
• Process redesign trumps automation: The greatest returns come from reimagining workflows around AI capabilities rather than using AI to speed up existing processes.
• Human-AI collaboration is the endgame: Despite headlines about AI replacing jobs, the most successful implementations amplify human capabilities rather than eliminate human involvement. Workers become managers of AI systems, curators of outputs, handlers of exceptions, and providers of the judgment and creativity that remain distinctly human.
• Data infrastructure is foundational: Organizations with clean, well-organized, accessible data will extract far more value from AI than those with fragmented, siloed, or low-quality information.

The future of AI isn’t about machines replacing humans: It’s about fundamentally new ways of working where human intelligence and AI combine to solve problems neither could address alone. Organizations that embrace this collaborative model—investing in both technology and the human capabilities to leverage it—will be best positioned to thrive in the decades ahead.

Exceed Your Productivity Goals With NetSuite AI

Businesses navigating the rapidly evolving landscape of AI can consider NetSuite’s cloud-based enterprise resource planning (ERP) system, which offers a powerful set of AI capabilities that can help organizations of all sizes boost their productivity and gain a competitive edge. NetSuite’s embedded AI capabilities can help businesses automate a wide variety of repetitive tasks and gain more valuable insights from their data than previously possible, which equips business managers to make better-informed decisions. NetSuite does this by adding AI functions to its proven, user-friendly ERP with a unified central database.

NetSuite’s AI capabilities span business functions from invoice processing to financial management. For example, NetSuite’s intelligent financial management tools can scan invoices using AI-based object and character recognition, automatically categorize expenses, continuously analyze financial data to detect anomalies and recommend next steps, and provide predictive insights into cash flow and budgeting. NetSuite’s AI-powered demand forecasting and inventory management features can help businesses optimize their stock levels, reduce waste, and improve order fulfillment. By integrating these AI capabilities into a comprehensive ERP solution, NetSuite enables businesses to streamline their operations, enhance their agility, and unlock new opportunities for innovation and growth, all while keeping pace with the rapid advancements in AI technology.

AI is a transformative technology that is rapidly reshaping the business landscape. AI’s ability to analyze data, automate processes, and generate insights can help businesses improve efficiency and drive innovation and growth. As AI rapidly evolves, it’s essential for organizations to stay up-to-date about its potential benefits—and challenges—so they can develop strategies to integrate AI into their planning and operations.

Artificial Intelligence FAQs

What is AI mainly used for?

AI is mainly used for automating tasks, analyzing data, and making predictions to help businesses and individuals make better decisions and solve complex problems.

What is the purpose of AI?

AI research and products aim to create intelligent machines that can perform tasks that typically require human intelligence, such as learning, problem-solving, and decision-making.

Where is AI used today?

AI is used in virtually all industries, including healthcare (diagnosis and drug discovery), finance (fraud detection and algorithmic trading), transportation (autonomous vehicles), manufacturing (predictive maintenance), customer service (chatbots), and more.

Can AI replace humans?

While AI can automate many tasks and augment human capabilities, it is unlikely to replace humans. AI is best suited for specific, well-defined tasks, while humans excel at creativity, empathy, and general intelligence. The most successful applications of AI are likely to involve collaboration between humans and machines.

What are people using AI for?

People are using AI for a wide range of purposes. These include automating repetitive tasks and processes, analyzing large volumes of data to identify patterns and insights, making predictions and forecasts based on historical data, personalizing experiences and recommendations for customers, improving decision-making and problem-solving, enhancing creativity, generating new ideas in fields like art and design, augmenting human capabilities, and improving workplace productivity.

How is AI being used by businesses?

Business use of AI is limited only by imagination. In operations, AI automates repetitive tasks, such as invoice processing, inventory management, and customer support. In marketing and sales, algorithms analyze customer data to predict demand, personalize recommendations, and optimize campaigns. Finance teams use AI for fraud detection, forecasting, and anomaly spotting, while manufacturers apply it to predictive maintenance and quality control. AI agents have become able to handle multistep workflows—from onboarding employees to managing orders—largely autonomously. By combining automation with data-driven insight, AI is helping organizations of all sizes reduce costs, innovate faster, and deliver better customer experiences.

What percentage of businesses use AI?

Nearly all businesses use AI in one form or another. For example, Verizon’s “2025 Mobile Security Index” survey found that 93% of 762 responding organizations worldwide say their employees are using generative AI tools on their mobile devices.