{"id":6375,"date":"2026-02-04T10:08:23","date_gmt":"2026-02-04T18:08:23","guid":{"rendered":"https:\/\/www.visla.us\/blog\/?p=6375"},"modified":"2026-02-04T10:08:24","modified_gmt":"2026-02-04T18:08:24","slug":"what-is-ai","status":"publish","type":"post","link":"https:\/\/www.visla.us\/blog\/guides\/what-is-ai\/","title":{"rendered":"What Is AI? A Straightforward 2026 Guide for Non-Experts"},"content":{"rendered":"\n<div class=\"wp-block-group has-global-padding is-layout-constrained wp-block-group-is-layout-constrained\">\n<div class=\"wp-block-group has-base-2-background-color has-background has-global-padding is-layout-constrained wp-container-core-group-is-layout-1 wp-block-group-is-layout-constrained\" style=\"border-radius:20px;padding-top:var(--wp--preset--spacing--20);padding-right:var(--wp--preset--spacing--20);padding-bottom:var(--wp--preset--spacing--20);padding-left:var(--wp--preset--spacing--20)\">\n<h3 class=\"wp-block-heading is-style-asterisk\">Quick Answer<\/h3>\n\n\n\n<p>Artificial intelligence, or AI, means software that can make useful outputs like predictions, recommendations, decisions, or new content from the information it receives. People use \u201cAI\u201d as a shorthand for tools like chatbots and image generators, but it also includes things like fraud detection, spam filters, route planning, and factory quality checks. Most modern AI learns patterns from data instead of following only hand-written rules, which makes it powerful and also harder to reason about. This guide breaks down what researchers mean by AI, how today\u2019s \u201cGenAI\u201d fits in, and how to talk about AI clearly at work.<\/p>\n<\/div>\n\n\n\n<h2 class=\"wp-block-heading\">A basic definition of AI<\/h2>\n<\/div>\n\n\n\n<p>AI is software that takes inputs, applies some kind of logic or learned pattern, and produces outputs that look \u201csmart\u201d in context.<\/p>\n\n\n\n<p>That sounds broad on purpose. Researchers don\u2019t reserve the term for chatbots, or for human-like intelligence. They use it for systems that do tasks we\u2019d normally associate with human judgment, like recognizing speech, spotting fraud, summarizing a document, or deciding which product a customer might want next.<\/p>\n\n\n\n<p>A practical way to think about it:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Input:<\/strong> text, images, audio, sensor data, numbers, clicks, or anything else you can measure<\/li>\n\n\n\n<li><strong>Processing:<\/strong> rules, statistical models, machine learning, or combinations of these<\/li>\n\n\n\n<li><strong>Output:<\/strong> predictions, recommendations, decisions, or generated content<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\">What AI means in research vs everyday conversation<\/h2>\n\n\n\n<p>People often talk past each other because \u201cAI\u201d means different things in different rooms.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">What researchers often mean<\/h3>\n\n\n\n<p>Researchers use \u201cAI\u201d as an umbrella term for many approaches that help computers perform tasks that usually require intelligence.<\/p>\n\n\n\n<p>Some of those approaches look old-school, like logic rules and search. Others look modern, like machine learning models that learn from large datasets. Many real systems blend both.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">What most people mean right now<\/h3>\n\n\n\n<p>In 2026, when someone says \u201cAI\u201d casually, they often mean <a href=\"https:\/\/research.ibm.com\/blog\/what-is-generative-AI\" target=\"_blank\" rel=\"noreferrer noopener\">generative AI<\/a>, especially:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><a href=\"https:\/\/teachingcommons.stanford.edu\/teaching-guides\/artificial-intelligence-teaching-guide\/defining-ai-and-chatbots\" target=\"_blank\" rel=\"noreferrer noopener\">chatbots<\/a> that write and summarize text<\/li>\n\n\n\n<li>tools that generate images, audio, or video<\/li>\n\n\n\n<li><a href=\"https:\/\/www.sap.com\/resources\/what-is-ai-copilot\" target=\"_blank\" rel=\"noreferrer noopener\">copilots<\/a> that help draft emails, <a href=\"https:\/\/code.claude.com\/docs\/en\/overview\" target=\"_blank\" rel=\"noreferrer noopener\">code<\/a>, or presentations<\/li>\n<\/ul>\n\n\n\n<p>That usage makes sense because generative tools feel new and visible. Still, plenty of \u201cAI\u201d at work runs quietly in the background and never chats with you.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">AI is bigger than chatbots: common AI systems you already use<\/h2>\n\n\n\n<p>You\u2019ve probably relied on AI for years without calling it that. Here are a few examples you\u2019ll recognize:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Search and ranking:<\/strong> ordering results, products, or posts<\/li>\n\n\n\n<li><strong>Recommendations:<\/strong> \u201cYou might also like\u2026\u201d in retail, media, and news<\/li>\n\n\n\n<li><strong>Spam and fraud detection:<\/strong> flagging suspicious messages or transactions<\/li>\n\n\n\n<li><strong>Speech and vision:<\/strong> transcription, captioning, photo tagging, visual inspection<\/li>\n\n\n\n<li><strong>Forecasting:<\/strong> demand planning, inventory planning, and anomaly detection<\/li>\n\n\n\n<li><strong>Operations:<\/strong> route optimization, scheduling, and dynamic pricing<\/li>\n<\/ul>\n\n\n\n<p>These systems rarely generate paragraphs of text, but they still count as AI when they infer outputs from inputs to meet objectives.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">A simple map of the landscape: AI vs ML vs deep learning<\/h2>\n\n\n\n<p>You\u2019ll hear these terms constantly. Here\u2019s the cleanest way to relate them.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>AI<\/strong> is the big umbrella: any system that performs tasks we associate with intelligent behavior.<\/li>\n\n\n\n<li><strong>Machine learning (ML)<\/strong> sits inside AI: systems that learn patterns from data to improve performance.<\/li>\n\n\n\n<li><strong>Deep learning<\/strong> sits inside ML: a style of ML that uses multi-layer neural networks and often handles messy data like images, audio, and language well.<\/li>\n<\/ul>\n\n\n\n<p>Not every AI system uses ML, and not every ML system uses deep learning. A rule-based expert system can count as AI. A basic ML model can predict churn without any deep learning.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">A comparison table for AI and similar systems<\/h3>\n\n\n\n<figure class=\"wp-block-table is-style-stripes\"><table class=\"has-fixed-layout\"><thead><tr><th>Approach<\/th><th>What it does<\/th><th>Strengths<\/th><th>Trade-offs<\/th><th>Common examples<\/th><\/tr><\/thead><tbody><tr><td>Rules and logic<\/td><td>Follows explicit if-then rules<\/td><td>Transparent, predictable<\/td><td>Breaks in edge cases, hard to scale<\/td><td>eligibility checks, business rules<\/td><\/tr><tr><td>Classical ML<\/td><td>Learns patterns from structured data<\/td><td>Efficient, strong for prediction<\/td><td>Needs good data, can drift<\/td><td>churn prediction, fraud scoring<\/td><\/tr><tr><td>Deep learning<\/td><td>Learns rich patterns from large data<\/td><td>Great with images, audio, language<\/td><td>Needs more compute and data<\/td><td>speech recognition, vision models<\/td><\/tr><tr><td>Generative AI<\/td><td>Produces new content that matches patterns in training data<\/td><td>Fast drafting and ideation<\/td><td>Can sound confident while wrong<\/td><td>chatbots, image generation<\/td><\/tr><tr><td>Reinforcement learning<\/td><td>Learns through feedback from an environment<\/td><td>Good for sequential decisions<\/td><td>Hard to set rewards and test safely<\/td><td>robotics, game-playing, some optimization<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<h2 class=\"wp-block-heading\">How AI actually works<\/h2>\n\n\n\n<p>Most AI projects follow a simple lifecycle, even when the tech looks fancy.<\/p>\n\n\n\n<ol class=\"wp-block-list\">\n<li><strong>Define the objective.<\/strong> You pick a goal like \u201creduce support handle time,\u201d \u201cflag risky transactions,\u201d or \u201cdraft product descriptions.\u201d<\/li>\n\n\n\n<li><strong>Collect or choose data.<\/strong> You decide what inputs the system can see, and you check privacy, security, and quality.<\/li>\n\n\n\n<li><strong>Choose a method.<\/strong> You might use rules, a classical ML model, a deep learning model, or a combination.<\/li>\n\n\n\n<li><strong>Train or configure the system.<\/strong> Teams fit the model to data, tune it, and test it against clear metrics.<\/li>\n\n\n\n<li><strong>Run inference in production.<\/strong> The system generates outputs on new inputs, like predictions or generated text.<\/li>\n\n\n\n<li><strong>Monitor and improve.<\/strong> Teams watch for drift, errors, bias, and changes in the real world.<\/li>\n<\/ol>\n\n\n\n<p>If you want one mental model, treat AI like a decision or content engine that learns from examples and then generalizes.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Where GenAI and LLMs fit<\/h2>\n\n\n\n<p>Generative AI sits inside machine learning. It focuses on creating new outputs that resemble the patterns in its training data, like text that reads naturally or images that match a prompt.<\/p>\n\n\n\n<p>A <strong><a href=\"https:\/\/www.cloudflare.com\/learning\/ai\/what-is-large-language-model\/\" target=\"_blank\" rel=\"noreferrer noopener\">large language model (LLM)<\/a><\/strong> is a type of generative AI that works with text. LLMs learn patterns in language at scale, then generate likely next pieces of text based on what you ask and what you provide.<\/p>\n\n\n\n<p>Two ideas help demystify LLMs:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>They predict.<\/strong> In practice, they generate text by predicting what comes next given the context.<\/li>\n\n\n\n<li><strong>They generalize.<\/strong> They can adapt to many tasks because the same language patterns show up across many documents and workflows.<\/li>\n<\/ul>\n\n\n\n<p>That\u2019s why one tool can summarize a contract, draft a sales email, and outline a strategy memo. The interface stays the same, even though the task changes.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Foundation models and \u201cadaptation\u201d<\/h3>\n\n\n\n<p>You\u2019ll also hear the term <strong><a href=\"https:\/\/blogs.nvidia.com\/blog\/what-are-foundation-models\/\" target=\"_blank\" rel=\"noreferrer noopener\">foundation model<\/a><\/strong>. Teams train these models on broad datasets so they can handle many downstream tasks.<\/p>\n\n\n\n<p>After that, companies adapt them in a few common ways:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Prompting:<\/strong> you give better instructions, examples, and constraints<\/li>\n\n\n\n<li><strong>Retrieval:<\/strong> you ground the model in trusted documents by searching and quoting them<\/li>\n\n\n\n<li><strong>Fine-tuning:<\/strong> you train further on your data so the model learns your style or domain<\/li>\n\n\n\n<li><strong>Tool use:<\/strong> you connect the model to calculators, databases, or workflow systems<\/li>\n<\/ul>\n\n\n\n<p>This is where a lot of business value comes from. The base model gives you a strong general engine, then your data and workflow make it useful.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Different types of AI you\u2019ll hear about<\/h2>\n\n\n\n<p>People use \u201ctypes\u201d in a few different ways. Here are the most common categories, plus what they usually mean.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">By capability: narrow AI vs general AI<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Narrow AI:<\/strong> systems that perform specific tasks well, like translating, detecting fraud, or drafting copy.<\/li>\n\n\n\n<li><strong>Artificial general intelligence (AGI):<\/strong> a hypothetical system that can learn and perform across almost any task at a human level.<\/li>\n<\/ul>\n\n\n\n<p>Most real AI today sits firmly in narrow AI, including the most impressive chatbots.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">By output: predictive AI vs generative AI<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Predictive AI:<\/strong> predicts labels or numbers, like \u201cfraud\u201d vs \u201cnot fraud\u201d or \u201cexpected demand next week.\u201d<\/li>\n\n\n\n<li><strong>Generative AI:<\/strong> generates content like text, images, audio, or video.<\/li>\n<\/ul>\n\n\n\n<p>Both can support business decisions. Predictive systems often plug into operations and analytics. Generative systems often plug into communication and creative workflows.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">By learning setup: supervised, unsupervised, and reinforcement learning<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Supervised learning:<\/strong> the model learns from labeled examples, like emails marked as spam.<\/li>\n\n\n\n<li><strong>Unsupervised learning:<\/strong> the model finds structure without labels, like clustering customers.<\/li>\n\n\n\n<li><strong>Reinforcement learning:<\/strong> the model learns through feedback, like rewards or penalties.<\/li>\n<\/ul>\n\n\n\n<p>Don\u2019t over-index on these labels. Teams often combine methods, and product names rarely tell you what training setup they used.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">What AI can\u2019t do, even when it sounds confident<\/h2>\n\n\n\n<p>AI can look smooth and still fail in predictable ways. You\u2019ll make better decisions if you assume these limits up front.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>It doesn\u2019t guarantee truth.<\/strong> An LLM can produce a fluent answer that doesn\u2019t match reality.<\/li>\n\n\n\n<li><strong>It doesn\u2019t understand like a human.<\/strong> It patterns matches and generalizes, but it doesn\u2019t \u201cknow\u201d in the human sense. However, this is a larger philosophical debate than we have time for here (i.e. how do WE know what it means to &#8220;know&#8221; in a &#8220;human sense&#8221;). <\/li>\n\n\n\n<li><strong>It reflects its data and setup.<\/strong> If the data contains gaps or bias, outputs can inherit them.<\/li>\n\n\n\n<li><strong>It can drift.<\/strong> A model can lose accuracy when the world changes, like customer behavior or fraud tactics.<\/li>\n<\/ul>\n\n\n\n<p>You can still use AI responsibly. You just need guardrails, testing, monitoring, and clear ownership.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">How to talk about AI clearly at work<\/h2>\n\n\n\n<p>If you want to cut through hype in meetings, ask questions that force precision.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>What input does it use?<\/strong> Data sources matter more than buzzwords.<\/li>\n\n\n\n<li><strong>What output does it produce?<\/strong> Prediction, recommendation, decision, or generated content.<\/li>\n\n\n\n<li><strong>What does \u201cgood\u201d look like?<\/strong> Pick metrics like accuracy, time saved, error rate, or customer satisfaction.<\/li>\n\n\n\n<li><strong>How do we control risk?<\/strong> Think privacy, security, bias, and failure modes.<\/li>\n\n\n\n<li><strong>Who owns the outcome?<\/strong> A tool can assist, but a team still stays accountable.<\/li>\n<\/ul>\n\n\n\n<p>If you keep the conversation anchored to inputs, outputs, and measurable outcomes, \u201cAI\u201d turns into a normal business system you can evaluate.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">FAQ<\/h2>\n\n\n\n<div class=\"schema-faq wp-block-yoast-faq-block\"><div class=\"schema-faq-section\" id=\"faq-question-1770073093081\"><strong class=\"schema-faq-question\">What is AI, and why do formal definitions focus on outputs?<\/strong> <p class=\"schema-faq-answer\">In plain terms, what is AI is any machine-based system that takes inputs and infers outputs to achieve a goal, rather than simply executing a fixed script. Formal definitions often spell those outputs out as predictions, generated content, recommendations, or decisions, because that covers everything from chatbots to credit scoring. Many definitions also emphasize that the system can operate with some autonomy and may adapt after deployment, which is why the same definition can cover both a static fraud model and a self-improving one. Using this lens makes conversations clearer, because you can ask what the inputs are, what outputs it produces, and what real-world effects those outputs can influence.<\/p> <\/div> <div class=\"schema-faq-section\" id=\"faq-question-1770074121879\"><strong class=\"schema-faq-question\">What is AI in a product: is it the model, the chatbot, or the whole workflow?<\/strong> <p class=\"schema-faq-answer\">In a product, what is AI is rarely just the model; it is the whole system that wraps a model with data pipelines, retrieval of documents, guardrails, and a user interface. A useful way to separate terms is that an AI model is a component that transforms inputs into outputs, while an AI system is the engineered package that uses one or more models to deliver outcomes for an objective. That distinction matters because many failures come from the surrounding system such as poor source data, weak access controls, or missing human review, not from the core model alone. When evaluating a vendor, ask to see the full flow end to end, including what data it touches, how outputs are logged and audited, and how humans can override or correct results.<\/p> <\/div> <div class=\"schema-faq-section\" id=\"faq-question-1770074128465\"><strong class=\"schema-faq-question\"><strong>What is AI compared with automation, rules engines, and statistics?<\/strong><\/strong> <p class=\"schema-faq-answer\">A quick test for what is AI versus plain automation is whether the software infers or generalizes from data to produce outputs, instead of applying only deterministic if-then rules. Standards bodies like ISO define an AI system broadly as an engineered system that generates outputs like content, forecasts, recommendations, or decisions for human-defined objectives, which is why the boundary can be fuzzier than people expect. The practical difference is that learned or adaptive systems can handle messy inputs and novel cases better, but they also introduce uncertainty, drift, and harder-to-explain behavior. If someone calls a feature AI, you can cut through the marketing by asking whether it learns from data, whether it can change behavior after deployment, and how its errors are detected and corrected.<\/p> <\/div> <div class=\"schema-faq-section\" id=\"faq-question-1770074138853\"><strong class=\"schema-faq-question\">What is AI \u201cresponsible use\u201d in practice for a team?<\/strong> <p class=\"schema-faq-answer\">For most teams, what is AI responsible use comes down to treating it like any other high-impact system: define the intended purpose, test it against measurable requirements, and plan for failures. Risk frameworks like the one from NIST emphasize managing AI risks across the lifecycle, with attention to trustworthiness and governance rather than just model accuracy. In practice that means doing pre-deployment evaluations on realistic data, setting up human review where mistakes are costly, and monitoring performance over time for drift, bias, and security issues. It also means assigning an owner who can pause, roll back, or retire the system, because accountability cannot be delegated to software.<\/p> <\/div> <div class=\"schema-faq-section\" id=\"faq-question-1770074145798\"><strong class=\"schema-faq-question\">What is AI actually good for right now, and how do you measure value?<\/strong> <p class=\"schema-faq-answer\">What is AI best at today is accelerating work that has clear patterns and feedback loops, like triaging requests, extracting fields from documents, drafting first-pass content, or spotting anomalies that humans can verify. To measure value, set a baseline first and then track metrics like time saved, error rate, customer satisfaction, and the cost of human review, because \u201clooks great in a demo\u201d does not guarantee \u201cworks in production.\u201d Run small pilots with holdout comparisons, log edge cases, and update the process as you learn where the system is strong versus where it should defer to humans. If you treat adoption as an ongoing measurement and risk-management program, not a one-time install, you are far more likely to get durable ROI.<\/p> <\/div> <\/div>\n\n\n\n<details class=\"wp-block-details has-large-font-size is-layout-flow wp-block-details-is-layout-flow\"><summary>Glossary of basic AI terms<\/summary>\n<figure class=\"wp-block-table is-style-stripes\"><table class=\"has-fixed-layout\"><thead><tr><th>Term<\/th><th>Plain-English meaning<\/th><\/tr><\/thead><tbody><tr><td>Artificial intelligence (AI)<\/td><td>Software that produces useful outputs like predictions, recommendations, decisions, or content from the information it receives.<\/td><\/tr><tr><td>AI system<\/td><td>An engineered system that uses computation to infer outputs that influence a digital or physical environment.<\/td><\/tr><tr><td>Algorithm<\/td><td>A step-by-step method a computer follows to solve a problem.<\/td><\/tr><tr><td>Model<\/td><td>A mathematical function that maps inputs to outputs, like a churn score or a draft paragraph.<\/td><\/tr><tr><td>Training<\/td><td>The process where a team fits a model to data so it learns patterns.<\/td><\/tr><tr><td>Inference<\/td><td>The process where a model generates outputs on new inputs after training.<\/td><\/tr><tr><td>Machine learning (ML)<\/td><td>A set of methods where software learns patterns from data to improve performance.<\/td><\/tr><tr><td>Deep learning<\/td><td>A style of machine learning that uses multi-layer neural networks and often works well on language, images, and audio.<\/td><\/tr><tr><td>Neural network<\/td><td>A model structure that learns patterns through layers of interconnected \u201cnodes.\u201d<\/td><\/tr><tr><td>Supervised learning<\/td><td>ML that learns from labeled examples, like transactions tagged as fraud or not fraud.<\/td><\/tr><tr><td>Reinforcement learning<\/td><td>ML that learns by interacting with an environment and optimizing for feedback or rewards.<\/td><\/tr><tr><td>Generative AI<\/td><td>AI that creates new content like text, images, audio, or video that resembles its training data.<\/td><\/tr><tr><td>Foundation model<\/td><td>A broadly trained model that teams adapt for many downstream tasks through prompting, retrieval, or fine-tuning.<\/td><\/tr><tr><td>Large language model (LLM)<\/td><td>A generative model that processes and produces text, often across many tasks through prompting.<\/td><\/tr><tr><td>Prompt<\/td><td>The instructions and context you give a generative model to shape its output.<\/td><\/tr><tr><td>Fine-tuning<\/td><td>Additional training on a narrower dataset so a model better matches a domain, style, or task.<\/td><\/tr><tr><td>Hallucination<\/td><td>When a generative model outputs fluent content that doesn\u2019t match the facts or the provided sources.<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<p><\/p>\n<\/details>\n","protected":false},"excerpt":{"rendered":"<p>Quick Answer Artificial intelligence, or AI, means software that can make useful outputs like predictions, recommendations, decisions, or new content from the information it receives. People use \u201cAI\u201d as a shorthand for tools like chatbots and image generators, but it also includes things like fraud detection, spam filters, route planning, and factory quality checks. Most [&hellip;]<\/p>\n","protected":false},"author":9,"featured_media":6382,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[25],"tags":[],"class_list":["post-6375","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-guides"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.2 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>What Is AI? A Straightforward 2026 Guide for Non-Experts - The Visla Blog<\/title>\n<meta name=\"description\" content=\"AI, explained for busy professionals: what it is, why it&#039;s more than chatbots, how ML and GenAI differ, and where LLMs fit at work.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.visla.us\/blog\/guides\/what-is-ai\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"What Is AI? A Straightforward 2026 Guide for Non-Experts - The Visla Blog\" \/>\n<meta property=\"og:description\" content=\"AI, explained for busy professionals: what it is, why it&#039;s more than chatbots, how ML and GenAI differ, and where LLMs fit at work.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/www.visla.us\/blog\/guides\/what-is-ai\/\" \/>\n<meta property=\"og:site_name\" content=\"The Visla Blog\" \/>\n<meta property=\"article:published_time\" content=\"2026-02-04T18:08:23+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2026-02-04T18:08:24+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/www.visla.us\/wp-content\/uploads\/2026\/02\/Thumbnail-Draft-1.jpg\" \/>\n\t<meta property=\"og:image:width\" content=\"1280\" \/>\n\t<meta property=\"og:image:height\" content=\"720\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/jpeg\" \/>\n<meta name=\"author\" content=\"May Horiuchi\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"May Horiuchi\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"12 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"Article\",\"@id\":\"https:\/\/www.visla.us\/blog\/guides\/what-is-ai\/#article\",\"isPartOf\":{\"@id\":\"https:\/\/www.visla.us\/blog\/guides\/what-is-ai\/\"},\"author\":{\"name\":\"May Horiuchi\",\"@id\":\"https:\/\/www.visla.us\/blog\/#\/schema\/person\/dcb20e581baf8b9574924cab20d6ae6d\"},\"headline\":\"What Is AI? 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It also means assigning an owner who can pause, roll back, or retire the system, because accountability cannot be delegated to software.","inLanguage":"en-US"},"inLanguage":"en-US"},{"@type":"Question","@id":"https:\/\/www.visla.us\/blog\/guides\/what-is-ai\/#faq-question-1770074145798","position":5,"url":"https:\/\/www.visla.us\/blog\/guides\/what-is-ai\/#faq-question-1770074145798","name":"What is AI actually good for right now, and how do you measure value?","answerCount":1,"acceptedAnswer":{"@type":"Answer","text":"What is AI best at today is accelerating work that has clear patterns and feedback loops, like triaging requests, extracting fields from documents, drafting first-pass content, or spotting anomalies that humans can verify. To measure value, set a baseline first and then track metrics like time saved, error rate, customer satisfaction, and the cost of human review, because \u201clooks great in a demo\u201d does not guarantee \u201cworks in production.\u201d Run small pilots with holdout comparisons, log edge cases, and update the process as you learn where the system is strong versus where it should defer to humans. If you treat adoption as an ongoing measurement and risk-management program, not a one-time install, you are far more likely to get durable ROI.","inLanguage":"en-US"},"inLanguage":"en-US"}]}},"_links":{"self":[{"href":"https:\/\/www.visla.us\/blog\/wp-json\/wp\/v2\/posts\/6375","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.visla.us\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.visla.us\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.visla.us\/blog\/wp-json\/wp\/v2\/users\/9"}],"replies":[{"embeddable":true,"href":"https:\/\/www.visla.us\/blog\/wp-json\/wp\/v2\/comments?post=6375"}],"version-history":[{"count":6,"href":"https:\/\/www.visla.us\/blog\/wp-json\/wp\/v2\/posts\/6375\/revisions"}],"predecessor-version":[{"id":6381,"href":"https:\/\/www.visla.us\/blog\/wp-json\/wp\/v2\/posts\/6375\/revisions\/6381"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.visla.us\/blog\/wp-json\/wp\/v2\/media\/6382"}],"wp:attachment":[{"href":"https:\/\/www.visla.us\/blog\/wp-json\/wp\/v2\/media?parent=6375"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.visla.us\/blog\/wp-json\/wp\/v2\/categories?post=6375"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.visla.us\/blog\/wp-json\/wp\/v2\/tags?post=6375"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}