{"id":20392,"date":"2026-04-28T10:58:22","date_gmt":"2026-04-28T05:28:22","guid":{"rendered":"https:\/\/www.vtiger.com\/blog\/?p=20392"},"modified":"2026-08-11T17:24:28","modified_gmt":"2026-08-11T11:54:28","slug":"https-www-vtiger-com-blog-types-of-ai","status":"publish","type":"post","link":"https:\/\/www.vtiger.com\/blog\/https-www-vtiger-com-blog-types-of-ai\/","title":{"rendered":"10 Key Types of AI \u2013 Real World Examples and Use Cases in 2026"},"content":{"rendered":"\n<p>Types of AI are commonly classified based on capability and functionality. The three main categories by capability are narrow AI, general AI, and superintelligent AI. Another classification includes reactive machines, limited memory, theory of mind, and self-aware AI. These categories help explain how AI systems evolve from simple task-based tools to advanced autonomous intelligence.<\/p>\n\n\n\n<p>AI has moved well past the experimental phase. It now drives underwriting decisions at insurance firms, surfaces leads in enterprise CRMs, flags compliance risks in real time, and runs the recommendation logic behind platforms handling billions of daily interactions. For business leaders, the question is which type of AI is relevant, and where.<\/p>\n\n\n\n<p>Deploying the wrong category of AI or misreading what a given system is actually capable of, leads to underperformance at best and expensive misalignment at worst. A team expecting autonomous decision-making from a system built for pattern recognition will hit a wall fast.<\/p>\n\n\n\n<p>Read this blog to learn about different types of AI in detail, where they are applied in real scenarios, and how to identify the ones that align with your workflows and business goals.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">What Are the Types of AI?<\/h2>\n\n\n\n<p>AI classification is less about taxonomy for its own sake and more about creating a shared language for capability expectations. When a CTO asks whether the AI their team is evaluating can &#8220;learn from customer behavior over time&#8221; or &#8220;operate autonomously across departments,&#8221; the answer lives entirely in which type of AI is under discussion.<\/p>\n\n\n\n<p>Three frameworks answer three different questions:<\/p>\n\n\n\n<figure class=\"wp-block-table\"><table class=\"has-fixed-layout\"><tbody><tr><td><strong>Framework<\/strong><\/td><td><strong>Question it answers<\/strong><\/td><td><strong>Types it covers<\/strong><\/td><\/tr><tr><td>Capability<\/td><td>How smart is the system, and in what sense?<\/td><td>Narrow AI, General AI (AGI), Superintelligent AI (ASI)<\/td><\/tr><tr><td>Functionality<\/td><td>How does it process inputs and retain context?<\/td><td>Reactive machines, Limited memory, Theory of mind, Self-aware<\/td><\/tr><tr><td>Technology<\/td><td>What technique actually powers it?<\/td><td>Machine learning, Generative AI, Agentic AI<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<h2 class=\"wp-block-heading\">3 Major Types of AI based on Capabilities<\/h2>\n\n\n\n<p>Capability describes how advanced a system is, from mastering one task to the theoretical ceiling of machine superintelligence. Three categories sit on this scale.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Narrow AI (Weak AI)<\/h3>\n\n\n\n<p>Narrow AI is the whole of commercial AI today. Gartner reports that nearly all deployments through 2026 remain narrow: systems built for one job, such as predictive analytics, NLP, or workflow automation.<\/p>\n\n\n\n<p>For most business problems, narrow AI is the correct choice, not a compromise. What decides success is the training domain, not the model. Verticalized systems, like BloombergGPT for finance and Harvey for legal, outperform general models on their specialty, a pattern that holds across<a href=\"https:\/\/www.vtiger.com\/blog\/ai-in-finance\/\"> AI in finance<\/a> broadly.<\/p>\n\n\n\n<p>McKinsey research shows narrow AI in sales operations, such as lead scoring, raises sales productivity by 15 to 20%. A CRM assistant like<a href=\"https:\/\/www.vtiger.com\/calculus-ai\/\"> Calculus AI<\/a> works this way: it predicts deal outcomes and recommends next steps, while the person decides.<\/p>\n\n\n\n<p><strong>Did you know?<\/strong> Gartner estimates that narrow AI accounts for effectively all commercial AI in use today. Every chatbot, recommendation engine, and fraud model you rely on is task-specific by design, not a step away from general intelligence.<\/p>\n\n\n\n<p>Narrow AI is designed for a specific task or domain, so its capabilities do not automatically transfer to other use cases. For example, a fraud detection model trained on banking data would need new training data and adjustments to identify equipment anomalies in manufacturing.&nbsp;<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">General AI (AGI)<\/h3>\n\n\n\n<p>General AI, or artificial general intelligence (AGI), refers to a hypothetical AI system that could learn, reason, and solve problems across different domains without being retrained for each new task.&nbsp;<\/p>\n\n\n\n<p>Unlike narrow AI, which is designed for specific applications, AGI would be able to transfer knowledge and skills from one context to another. No AI system has demonstrated these capabilities reliably yet.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Superintelligent AI (ASI)<\/h3>\n\n\n\n<p>Superintelligent AI would surpass human intelligence across the board and improve itself faster than people could intervene. Narrow AI already beats humans in single domains like chess and protein folding; ASI&#8217;s defining trait is recursive self-improvement.<\/p>\n\n\n\n<p>Nick Bostrom&#8217;s intelligence-explosion argument frames most current AI safety work. The practical limits are real: the computing hardware, the energy supply, and a solution to alignment all remain out of reach.<\/p>\n\n\n\n<p>For business leaders, ASI is a strategic-planning concern that shapes governance and investment views, not an operational one.<\/p>\n\n\n\n<p>The governance work is already underway. Groups such as the Future of Life Institute are developing safeguards, including interpretability tools and coordination protocols, for systems that could one day outperform human strategists<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">4 Types of AI Based on Functionality<\/h2>\n\n\n\n<p>This classification describes the operational architecture of AI systems: how they handle inputs, what they retain between interactions, and the degree to which they model the world beyond immediate data. Each category represents a different level of complexity in how the system relates to context, time, and the humans it works with.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Reactive Machines<\/h3>\n\n\n\n<p>Reactive machines represent the foundational layer of AI architecture. They operate with no memory, no learning, and no model of the world beyond the immediate input. Every event is processed as a fresh transaction. This is not a limitation in systems where it is the appropriate design choice.<\/p>\n\n\n\n<p>The defining advantage of stateless execution is predictability. Because a reactive system carries no historical state, it cannot develop memory leaks, historical bias, or compounding errors from past interactions. In safety-critical environments, this deterministic behavior is precisely what makes reactive systems reliable. Core use cases where reactive machines remain the right architectural choice include:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Manufacturing brake triggers and fault detection systems where microsecond response is a safety requirement.<\/li>\n\n\n\n<li>Rule-based compliance screening where auditability requires that every decision trace back to a fixed, stateless logic tree.<\/li>\n\n\n\n<li>Industrial control systems where any latency introduced by memory retrieval creates unacceptable operational risk.<\/li>\n<\/ul>\n\n\n\n<p>IBM&#8217;s Deep Blue, which defeated Garry Kasparov in 1997, was a reactive machine. It evaluated board positions using hard-coded heuristics and brute-force calculation, with no memory of past games and no learning between moves. Netflix&#8217;s earliest recommendation algorithm, before reinforcement learning was introduced, operated similarly: match user input to predefined categories, return output, clear state. There was no persistent model of user preference evolving over time.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Limited Memory AI<\/h3>\n\n\n\n<p>Limited memory AI is the architecture behind virtually every production AI system operating at enterprise scale today. This includes large language models, autonomous vehicle perception systems, fraud detection engines, and <a href=\"https:\/\/www.vtiger.com\/blog\/what-are-ai-agents-and-how-do-they-work-in-vtiger-crm\/\">AI agents<\/a> managing multi-step business workflows. The defining characteristic is the ability to use historical data within a defined window to inform current decisions.<\/p>\n\n\n\n<p>The technical foundation is the transformer architecture, introduced by Google researchers in 2017. The core innovation was the attention mechanism: the ability to process an entire sequence of inputs and selectively weight the relevance of earlier elements when generating later ones. This gave language models the capacity to maintain coherent context across multi-turn interactions. ChatGPT, the major enterprise LLMs of 2026, and the AI agents now being deployed in enterprise workflows are all built on variations of this architecture.<\/p>\n\n\n\n<p>How limited memory works in practice across different deployment contexts:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Autonomous vehicles<\/strong> maintain a rolling window of the last 30 seconds of sensor data to continuously update trajectory predictions, without storing months of irrelevant highway footage.<\/li>\n\n\n\n<li><strong>Fraud detection engines<\/strong> evaluate transaction patterns across a defined time window, flagging anomalies based on deviation from established behavioral baselines.<\/li>\n\n\n\n<li><strong>Enterprise AI agents<\/strong> use conversation history and session context to maintain coherent multi-step task execution across a single workflow run.<\/li>\n<\/ul>\n\n\n\n<p>The most significant development that extends limited memory AI into enterprise use cases is Retrieval-Augmented Generation (RAG). RAG gives a limited memory model effective access to long-term, domain-specific knowledge by connecting it to a vector database of private documents including contracts, product documentation, support history, and compliance records, without retraining the underlying model. A deployed<a href=\"https:\/\/www.vtiger.com\/blog\/ai-crm\"> AI CRM<\/a> built on RAG can surface account-specific context, historical interaction data, and pricing history at query time. This is how most serious enterprise AI deployments are structured in 2026.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Theory of Mind AI<\/h3>\n\n\n\n<p>Theory of Mind AI does not yet exist in deployable form, but its component capabilities are being assembled across research labs and early commercial products. The concept originates in developmental psychology: the cognitive ability to model the mental states of others including their beliefs, intentions, emotions, and goals, and use those models to predict behavior.<\/p>\n\n\n\n<p>In AI terms, this means moving from &#8220;what did the user say&#8221; to &#8220;why did they say it, what are they trying to accomplish, and how are they likely to respond to different answers.&#8221; The gap between current AI and this capability is meaningful but narrowing. The capability progression researchers are tracking moves through three distinct stages:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Keyword matching<\/strong>: The system identifies intent based on surface-level language patterns.<\/li>\n\n\n\n<li><strong>Intent inference<\/strong>: The system models what the user is trying to accomplish beyond what they literally said.<\/li>\n\n\n\n<li><strong>Emotional modeling<\/strong>: The system adapts its response based on inferred emotional state, not just task objective.<\/li>\n<\/ul>\n\n\n\n<p>Affective computing research, covering systems that detect micro-expressions, vocal stress patterns, and physiological signals to infer emotional state, is already informing next-generation customer service interfaces. Forrester has flagged Human-Centric AI as a defining trend for 2026, driven by demand for systems that adapt not just to what users ask but to how they are feeling when they ask it.<\/p>\n\n\n\n<p>At the research frontier, mental state modeling is being explored for organizational communication applications. These are systems that can simulate how different stakeholder groups will respond to corporate announcements, policy changes, or product launches by modeling the belief systems and likely reactions of those groups in advance.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Self-Aware AI<\/h3>\n\n\n\n<p>Self-aware AI is entirely theoretical. There is no functional system that meets any serious definition of machine consciousness or self-awareness. What exists is a rigorous and genuinely contested scientific debate about whether the concept is even coherent and what would constitute evidence for it.<\/p>\n\n\n\n<p>The most developed scientific framework for approaching the question is Integrated Information Theory (IIT), which proposes that consciousness is a property of systems with a sufficiently high degree of integrated information, measured as a value called Phi. Under IIT, consciousness is not uniquely human. It is a property of certain information-processing architectures. Whether any current or near-future AI system could achieve a Phi value associated with subjective experience remains an open and vigorously debated question.<\/p>\n\n\n\n<p>The philosophical distinction that matters most for near-term AI development is the difference between autonomy and agency:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Autonomy<\/strong> is what today&#8217;s most advanced AI systems have. They can execute complex, multi-step tasks with minimal human direction.<\/li>\n\n\n\n<li><strong>Agency<\/strong> is what they do not have: their own desires, goals, or interests independent of what they were trained to optimize.<\/li>\n\n\n\n<li>The emergence of genuine machine agency would require not just new architectures but new frameworks for rights, accountability, and governance.<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>Types of AI Technologies Driving Innovation<\/strong><\/h2>\n\n\n\n<p>Capability and functionality describe what an AI system does, while the technology layer describes the techniques that enable it. Although AI applications differ widely, three technologies underpin many of the systems used today: machine learning, generative AI, and agentic AI.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Machine Learning<\/strong><\/h3>\n\n\n\n<p>Machine learning is the foundation of most modern AI systems. Instead of relying entirely on predefined rules, machine learning models learn patterns from data and use them to make predictions, classifications, or decisions. Depending on the problem, they may use supervised, unsupervised, or reinforcement learning.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Generative AI<\/strong><\/h3>\n\n\n\n<p>Generative AI creates new content such as text, images, audio, and code rather than only classifying information or making predictions. Large language models are commonly used for text generation, while diffusion models are widely used for image generation.<\/p>\n\n\n\n<p>In business environments, <a href=\"https:\/\/www.vtiger.com\/blog\/generative-ai-for-business\/\">generative AI<\/a> is used for tasks such as drafting documents, summarizing information, analyzing large volumes of text, and generating content. These applications can reduce manual effort and accelerate routine knowledge work, but their outputs still require human review because models can produce inaccurate or misleading information.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Agentic AI<\/strong><\/h3>\n\n\n\n<p>Agentic AI refers to systems that can plan and execute multiple steps toward a defined goal, often by using external tools, accessing business systems, and adapting their actions based on results. An <a href=\"https:\/\/www.vtiger.com\/blog\/ai-sales-agents\/\">AI sales agent<\/a>, for example, can research prospects, prepare outreach, update CRM records, and schedule follow-ups as part of a broader sales workflow.<\/p>\n\n\n\n<p>The defining feature of <a href=\"https:\/\/www.vtiger.com\/blog\/what-is-agentic\/\">agentic AI<\/a> is its ability to act rather than simply generate an answer. This also makes control and oversight important. Agents can handle routine tasks and recommendations independently within defined boundaries, while decisions involving significant financial, operational, or customer impact should remain subject to human approval.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>Real-World Examples of AI Types<\/strong><\/h2>\n\n\n\n<p>The categories become concrete when mapped to everyday tools. Each example below shows which type is doing the work.<\/p>\n\n\n\n<figure class=\"wp-block-table\"><table class=\"has-fixed-layout\"><tbody><tr><td><strong>Tool<\/strong><\/td><td><strong>AI type at work<\/strong><\/td><td><strong>What it shows<\/strong><\/td><\/tr><tr><td>Chatbots,<a href=\"https:\/\/www.vtiger.com\/blog\/ai-in-customer-service\/\"> AI in customer service<\/a><\/td><td>Limited memory<\/td><td>Matches intent using training data plus the current conversation<\/td><\/tr><tr><td>Recommendation engines<\/td><td>Limited memory<\/td><td>Ranks the item most likely to drive an action<\/td><\/tr><tr><td>Autonomous vehicles<\/td><td>Limited memory + reactive<\/td><td>Short-window sensor processing and object tracking<\/td><\/tr><tr><td>Enterprise AI agents<\/td><td>Limited memory + agentic + RAG<\/td><td>Retrieval and coordination against company data<\/td><\/tr><tr><td>Fraud detection<\/td><td>Narrow, limited memory<\/td><td>Window-based pattern analysis against a baseline<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<p>The enterprise-agent row is where the frameworks meet in practice. An AI CRM like<a href=\"https:\/\/www.vtiger.com\/nextgen\/\"> NextGen by Vtiger<\/a> runs this stack: narrow, limited memory AI with RAG that surfaces account context at query time, with the person still deciding what to do.<\/p>\n\n\n\n<p>The lesson repeats across the table. Almost every tool in daily use is narrow AI on a limited memory architecture, not a step toward general intelligence, and reading it that way sets the right expectations.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Why Understanding AI Types Matters for Businesses<\/h2>\n\n\n\n<p>The most common and expensive mistake in enterprise AI adoption is misalignment between what a system can do and what the business expects it to do. That misalignment almost always traces back to unclear classification.<\/p>\n\n\n\n<p><strong>Choosing the right solution: <\/strong>It starts with knowing what category of AI fits the problem. An organization trying to automate structured, rule-bounded tasks should be evaluating narrow AI with well-defined training data, not waiting for AGI-level reasoning capabilities that do not yet exist. Conversely, a business building a five-year AI roadmap needs to account for the current narrow AI capability ceiling and plan for the transition.<\/p>\n\n\n\n<p><strong>Managing expectations: <\/strong>When business stakeholders understand that current LLMs are limited memory systems operating within a probabilistic reasoning architecture\u2014not AGI\u2014they stop expecting them to &#8220;just know&#8221; things outside their training scope and start designing better integration patterns: RAG pipelines, human-in-the-loop validation, structured output parsing.<\/p>\n\n\n\n<p><strong>Strategic AI adoption<\/strong>: It requires reading both frameworks together. The best<a href=\"https:\/\/www.vtiger.com\/blog\/ai-in-business\/\"> <\/a>implementations pair the right capability level (narrow, domain-specific AI) with the right functional architecture (limited memory with RAG for context) and the right deployment model (human oversight at decision inflection points). Organizations that build this alignment into their AI strategy from the start compound their advantages faster than those treating AI as a plug-and-play feature.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Best Practices for Using AI Effectively<\/h2>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Start with narrow AI use cases:<\/strong> Define the specific task, the success metric, and the data source before evaluating any model. Broad mandates (&#8220;use AI to improve operations&#8221;) generate expensive pilot failures.<\/li>\n\n\n\n<li><strong>Align AI type to business goals:<\/strong> A long-range workforce transformation strategy maps to monitoring AGI development timelines. Do not conflate the two in planning conversations.<\/li>\n\n\n\n<li><strong>Invest in data quality before model selection.<\/strong> Narrow AI performs proportionally to the quality and specificity of its training data. A verticalized model trained on clean, domain-specific data will consistently outperform a general model with broader coverage.<\/li>\n\n\n\n<li><strong>Map potential human oversight:<\/strong> Limited memory AI systems can and do fail outside their training distribution. High-stakes decisions like credit, diagnosis, legal should have human validation gates regardless of model confidence scores.<\/li>\n\n\n\n<li><strong>Design for integration, not isolation.<\/strong> AI systems that cannot exchange data with existing CRM, ERP, or workflow infrastructure create new data silos. <a href=\"https:\/\/www.vtiger.com\/blog\/what-is-ai-automation\/\">AI automation<\/a> delivers compounding value when it operates inside connected systems.<\/li>\n\n\n\n<li><strong>Monitor for drift and bias continuously:<\/strong> Narrow AI models degrade when real-world data distributions shift away from training data. Performance monitoring, bias auditing, and scheduled retraining should be built into deployment architecture from day one.<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\">Frequently Asked Questions (FAQs)<\/h2>\n\n\n\n<h3 class=\"wp-block-heading\">Q1. What are the main types of AI?&nbsp;<\/h3>\n\n\n\n<p>AI is commonly classified in two ways. By capability: narrow AI (task-specific), general AI (AGI, human-level reasoning across domains), and superintelligent AI (theoretical, surpassing human intelligence). By functionality: reactive machines, limited memory AI, theory of mind AI, and self-aware AI.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Q2. What is the difference between narrow AI and general AI?&nbsp;<\/h3>\n\n\n\n<p>Narrow AI is designed and optimized for a specific task, it cannot operate meaningfully outside its training domain. General AI (AGI) would be capable of reasoning, learning, and transferring knowledge across domains the way a human professional does. AGI does not yet exist in any deployable form.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Q3. Is artificial general intelligence real today?&nbsp;<\/h3>\n\n\n\n<p>No. Current AI systems, including the most advanced LLMs, remain narrow by capability classification. They exhibit impressive pattern recognition and language generation within their training distribution, but they lack the causal reasoning and cross-domain transfer that would constitute AGI. Research timelines suggest AGI-like capabilities may emerge by 2030, but this remains probabilistic.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Q4. What type of AI is ChatGPT?&nbsp;<\/h3>\n\n\n\n<p>ChatGPT is narrow AI (capability) built on a limited memory architecture (functionality). It uses transformer-based attention mechanisms to maintain context within a conversation window, and it is optimized for language tasks. It cannot reason causally across domains or acquire knowledge outside its training data without augmentation (e.g., RAG or tool use).<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Q5. What is limited memory AI?&nbsp;<\/h3>\n\n\n\n<p>Limited memory AI uses historical data within a defined time window to inform current decisions. It does not store permanent memories but retains context long enough to make decisions that account for recent inputs. Most production AI systems\u2014including LLMs, recommendation engines, and autonomous vehicle perception systems\u2014are limited memory architectures.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Q6. Can AI become self-aware?&nbsp;<\/h3>\n\n\n\n<p>There are minor glimpses here and there but there is no robust technical pathway to machine self-awareness currently understood or demonstrated. Whether self-awareness in a machine is possible at all is an open scientific and philosophical question, contested across AI research, neuroscience, and philosophy of mind. Integrated Information Theory offers one framework for approaching the question, but no empirical test for machine consciousness exists.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Q7. Which type of AI is most commonly used today?&nbsp;<\/h3>\n\n\n\n<p>Narrow AI, operating on limited memory architectures, is the overwhelming majority of commercial AI in use as of 2026. This includes enterprise language models, predictive analytics tools, recommendation systems, fraud detection, and AI agents managing workflow automation. Gartner estimates this category accounts for nearly all AI deployments through the near-term horizon.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Types of AI are commonly classified based on capability and functionality. The three main categories by capability are narrow AI, general AI, and superintelligent AI. Another classification includes reactive machines, limited memory, theory of mind, and self-aware AI. These categories help explain how AI systems evolve from simple task-based tools to advanced autonomous intelligence. AI&hellip;&nbsp;<a href=\"https:\/\/www.vtiger.com\/blog\/https-www-vtiger-com-blog-types-of-ai\/\" class=\"\" rel=\"bookmark\">.<span class=\"screen-reader-text\">10 Key Types of AI \u2013 Real World Examples and Use Cases in 2026<\/span><\/a><\/p>\n","protected":false},"author":49,"featured_media":20393,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_eb_attr":"","neve_meta_sidebar":"","neve_meta_container":"","neve_meta_enable_content_width":"","neve_meta_content_width":0,"neve_meta_title_alignment":"","neve_meta_author_avatar":"","neve_post_elements_order":"","neve_meta_disable_header":"","neve_meta_disable_footer":"","neve_meta_disable_title":"","neve_meta_reading_time":"","_themeisle_gutenberg_block_has_review":false,"_ti_tpc_template_sync":false,"_ti_tpc_template_id":"","footnotes":""},"categories":[6],"tags":[],"class_list":["post-20392","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-artificial-intelligence-ai"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v21.8 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>10 Key Types of AI Explained \u2013 Examples &amp; Use Cases in 2026 | Vtiger<\/title>\n<meta name=\"description\" content=\"Explore the different types of AI, including narrow AI, general AI, and superintelligent AI. 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