Try monday agents<\/a>\n"}]},{"main_heading":"What is an AI model?","content_block":[{"acf_fc_layout":"text","content":"An AI model is a mathematical system trained on data to recognize patterns and produce useful outputs (predictions, classifications, recommendations, or generated content) without being explicitly programmed for each scenario.<\/p>\n
Think of it this way: an AI model is like a recipe that a chef follows. The recipe was developed by studying thousands of meals, and now it can produce new dishes based on what it learned. The chef doesn’t memorize every meal. It learns how cooking works and applies that knowledge to new ingredients.<\/p>\n
Every AI model is built from four core components that work together to deliver results.<\/p><\/blockquote>\n
Understanding these components helps you evaluate AI solutions and talk to technical teams without getting lost in jargon:<\/p>\n
\n- Training data:<\/strong> the information the model learns from: text, images, numbers, customer records, or any other dataset your workflow needs<\/li>\n
- Architecture:<\/strong> The structure that determines how the model processes information, like neural networks or decision trees<\/li>\n
- Parameters:<\/strong> The internal settings the model adjusts during learning. Think of these as the “knobs” that it tunes to produce more accurate results with each pass through the data<\/li>\n
- Output:<\/strong> The prediction, classification, recommendation, or generated content the model produces after training and deployment<\/li>\n<\/ul>\n
How AI models differ from algorithms and AI systems<\/h3>\n
“AI model,” “algorithm,” and “AI system” are often used interchangeably, but they refer to three distinct things. Here’s how these concepts relate to each other:<\/p>\n\n
\n\n\n\t| Aspect<\/th> | Algorithm<\/th> | AI model<\/th> | AI system<\/th>\n<\/tr>\n<\/thead>\n |
\n\n\t| What it is<\/td> | A set of step-by-step instructions that defines how learning happens<\/td> | The trained result of applying an algorithm to data<\/td> | A complete application that uses one or more AI models to deliver functionality<\/td>\n<\/tr>\n |
\n\t| Analogy<\/td> | A recipe<\/td> | A trained chef who learned from the recipe<\/td> | The entire restaurant operation<\/td>\n<\/tr>\n |
\n\t| Role<\/td> | Defines the learning process<\/td> | Stores learned patterns and makes predictions<\/td> | Delivers end-to-end functionality to team members<\/td>\n<\/tr>\n |
\n\t| Example<\/td> | Gradient descent, random forest<\/td> | GPT-4, BERT, a trained image classifier<\/td> | A chatbot, a recommendation engine, an autonomous AI agent<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\n An algorithm is the method. A model is what you get when you apply that method to data. A system is the full application that puts the model to work.<\/p>\n"}]},{"main_heading":"How do AI models work?","content_block":[{"acf_fc_layout":"text","content":" Every AI model goes through the same three phases before it’s ready to use. This process works the same whether the model classifies support tickets, forecasts demand, or generates marketing copy. Here’s what happens in each phase:<\/p>\n Step 1: Training phase<\/h3>\nThe model is exposed to large amounts of data and adjusts its internal parameters to recognize patterns. It’s like a new employee studying past project reports to learn how the company works.<\/p>\n Step 2: Validation phase<\/h3>\nThe model is tested against a separate set of data it hasn’t seen before. This step checks whether the model learned real patterns or just memorized the training examples (a problem called overfitting).<\/p>\n Step 3: Inference phase<\/h3>\nThe trained model is deployed and begins making predictions or generating outputs on new, real-world data. This is where the model does its job: answering questions, classifying inputs, flagging risks, or creating content.<\/p>\n Your training data quality directly determines your model’s output quality. Incomplete, inconsistent, or outdated data leads to unreliable predictions, regardless of how sophisticated the model architecture is \u2014 a reality reflected in research showing that data quality and availability is a primary barrier to AI adoption for 65% of organizations<\/a>.<\/p>\nUnderstanding generative vs. discriminative models<\/h3>\nBefore we explore specific AI model types, let’s look at the two fundamental categories all AI models fall into:<\/p>\n\n \n\n\n\t| Dimension<\/th> | Discriminative models<\/th> | Generative models<\/th>\n<\/tr>\n<\/thead>\n | \n\n\t| Primary function<\/td> | Classify or predict<\/td> | Create new content or data<\/td>\n<\/tr>\n | \n\t| Question answered<\/td> | \"What is this?\"<\/td> | \"What could this look like?\"<\/td>\n<\/tr>\n | \n\t| Common applications<\/td> | Fraud detection, image recognition, sentiment analysis<\/td> | Text generation, image creation, content drafting<\/td>\n<\/tr>\n | \n\t| Everyday example<\/td> | Email spam filter<\/td> | AI writing assistant<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\n Discriminative models<\/strong> learn the boundary between categories. They answer “which category does this belong to?” For example: is this email spam or not spam?<\/p>\nGenerative models<\/strong> learn the underlying patterns of data. They answer “what would new data that looks like this look like?” For example: write a marketing email based on past successful campaigns.<\/p>\n"}]},{"main_heading":"5 types of AI models and how they are used","content_block":[{"acf_fc_layout":"text","content":"You can categorize AI models by how they learn and what they produce. These five types aren’t mutually exclusive. A single AI system might combine multiple model types to handle complex workflows. Understanding each type helps you pick the right approach for your workflows.<\/p>\n 1. Supervised learning models<\/h3>\nSupervised learning models learn from labeled data. They compare their predictions to known correct answers and adjust until they improve. Think of it like a student studying with an answer key.<\/p>\n Real-world business applications include:<\/strong><\/p>\n\n- Sales forecasting<\/strong><\/a>:<\/strong> Predicting quarterly revenue based on historical sales data, seasonality patterns, and pipeline activity<\/li>\n
- Customer churn<\/strong><\/a> prediction:<\/strong> Identifying which customers are likely to cancel based on past behavior patterns such as declining usage or support ticket frequency<\/li>\n
- Document classification:<\/strong> Automatically sorting incoming requests into the correct category based on content analysis<\/li>\n
- Quality scoring:<\/strong> Evaluating leads, applications, or submissions against historical success criteria<\/li>\n<\/ul>\n
2. Unsupervised learning models<\/h3>\nUnsupervised learning models find hidden patterns and groupings in data without anyone telling them what to look for. It’s like organizing a messy closet: you group similar items together without knowing the categories ahead of time.<\/p>\n Real-world business applications include:<\/strong><\/p>\n\n- Customer segmentation<\/strong><\/a>:<\/strong> Grouping customers by behavior patterns that weren’t previously identified<\/li>\n
- Anomaly detection:<\/strong> Flagging unusual transactions, system behaviors, or workflow patterns that deviate from normal activity<\/li>\n
- Topic discovery:<\/strong> Identifying recurring themes across thousands of support tickets or feedback entries without pre-defining the categories<\/li>\n<\/ul>\n
3. Reinforcement learning models<\/h3>\nReinforcement learning models learn through trial and error. They get rewards for correct actions and penalties for wrong ones, gradually optimizing their strategy.<\/p>\n Real-world business applications include:<\/strong><\/p>\n\n- Dynamic pricing:<\/strong> Adjusting prices in real time based on demand, competition, and inventory levels<\/li>\n
- Resource allocation<\/strong><\/a>:<\/strong> Optimizing how people, budget, or equipment are distributed across projects<\/li>\n
- Campaign optimization:<\/strong> Continuously adjusting ad spend and targeting parameters based on performance feedback<\/li>\n<\/ul>\n
4. Deep learning models<\/h3>\nDeep learning is a type of machine learning that uses multi-layered neural networks to process complex, unstructured data like images, audio, and natural language.<\/p>\n Real-world business applications include:<\/strong><\/p>\n\n- Natural language processing (NLP):<\/strong> Understanding and generating human language for chatbots, translation, and sentiment analysis<\/li>\n
- Computer vision:<\/strong> Analyzing images and video for quality control, security monitoring, and document scanning<\/li>\n
- Speech recognition:<\/strong> Converting spoken language to text for meeting transcription and voice assistants<\/li>\n<\/ul>\n
5. Generative AI models<\/h3>\nGenerative AI<\/a> models create new content, including text, images, code, audio, or video, by learning patterns from existing data. These are the models behind platforms like ChatGPT, Claude, and similar applications.<\/p>\nReal-world business applications include:<\/strong><\/p>\n\n- Content creation:<\/strong> Drafting marketing copy, project reports, emails, and stakeholder communications<\/li>\n
- Code generation:<\/strong> Writing, debugging, and explaining code based on natural language prompts<\/li>\n
- Data synthesis:<\/strong> Generating realistic test data for software development<\/li>\n<\/ul>\n"}]},{"main_heading":"What are the benefits of AI models for businesses?","content_block":[{"acf_fc_layout":"text","content":"
Understanding AI model types is valuable, but what really matters is what these models actually do for your team. The benefits go beyond simple automation. They change how teams make decisions and get work done.<\/p>\n Faster and more informed decision-making<\/h3>\nAI models process and analyze data faster and at larger scale than any manual analysis. Leaders and managers can make decisions based on data instead of gut feel, and they can do it in real time.<\/p>\n Key capabilities include:<\/strong><\/p>\n\n- Project risk<\/strong><\/a> identification:<\/strong> AI models scan across all active projects and flag schedule risks, dependency conflicts, or workload imbalances before they become problems<\/li>\n
- Financial forecasting:<\/strong> Models trained on historical data surface revenue trends and budget variances that inform strategic planning<\/li>\n
- Market intelligence:<\/strong> Models continuously monitor competitor activity and customer sentiment, surfacing actionable insights<\/li>\n<\/ul>\n
Automated workflows and reduced manual effort<\/h3>\nAI models power automation that’s smarter than simple if-then rules. Traditional automation handles repetitive, predictable actions. AI-powered automation can make judgment calls: like analyzing a support ticket, figuring out its priority, and drafting a response.<\/p>\n Practical applications include:<\/strong><\/p>\n\n- Intelligent routing:<\/strong> AI models classify incoming requests and route them to the appropriate team based on content and urgency<\/li>\n
- Report generation:<\/strong> Models summarize project status, highlight blockers, and generate stakeholder-ready reports<\/li>\n
- Data cleanup:<\/strong> Models identify duplicate records and inconsistent entries across systems<\/li>\n<\/ul>\n"}]},{"main_heading":"How AI models are evolving into AI agents","content_block":[{"acf_fc_layout":"text","content":"
The next phase of AI development moves beyond individual models to AI agents<\/a> and orchestrated systems that can take action autonomously. This evolution represents a fundamental shift from AI that responds to prompts to AI that proactively manages workflows, and it is already underway: nearly half of global business leaders (46%)<\/a> say their companies are already using AI agents to fully automate workflows or processes.<\/p>\nThe evolution from assistive to autonomous AI<\/h3>\nAI is evolving in three stages: from assistants that help when asked, to teammates that collaborate proactively, to agents that work autonomously within set boundaries:<\/p>\n \n- Assistive AI (copilots):<\/strong> You prompt the model, it responds. You remain in control of every action.<\/li>\n
- Collaborative AI (teammates):<\/strong> The model monitors context, suggests actions, and executes with your approval.<\/li>\n
- Autonomous AI (agents):<\/strong> The model operates independently within defined guardrails, executing multi-step workflows around the clock.<\/li>\n<\/ol>\n
This evolution uses the same AI model types we covered earlier, but now they work together within agent frameworks that can act, not just answer questions.<\/p>\n Why cross-department context makes AI agents effective<\/h3>\n"},{"acf_fc_layout":"image","image_type":"normal","image":322265,"image_link":""},{"acf_fc_layout":"text","content":"An AI agent’s effectiveness depends entirely on the context it can access. If an agent can only see data from one department, its actions are limited and might conflict with what other teams need.<\/p>\n Consider a marketing agent planning a product launch campaign. If it can only see marketing data, it might schedule the campaign for a date when the product team hasn’t finished development. But if the agent can see marketing, product, sales, and operations data in one place, it can coordinate timing, messaging, and resources across all teams.<\/p>\n This is where monday agents stand out: they work within a unified workspace where all departments already collaborate, giving them access to cross-functional context that makes their actions smarter and more aligned with your organization’s actual needs.<\/p>\nTry monday agents<\/a>\n"}]},{"main_heading":"What to consider when implementing AI models","content_block":[{"acf_fc_layout":"text","content":"Successful AI implementation requires careful planning around data quality, governance, and whether your organization is ready. These considerations determine whether your AI models actually help or just create new problems.<\/p>\n Data quality and governance requirements<\/h3>\nYour AI models are only as good as the data they learn from and act on. If you’re considering AI adoption, data quality is the most important thing to get right first.<\/p>\n Essential data quality factors:<\/strong><\/p>\n\n- Completeness:<\/strong> Are there gaps in your data that could lead to biased or inaccurate model outputs?<\/li>\n
- Consistency:<\/strong> Is data formatted and categorized uniformly across teams and systems?<\/li>\n
- Accessibility:<\/strong> Can the AI model access the data it needs, or is critical information siloed?<\/li>\n
- Freshness:<\/strong> Is the data current, or is the model learning from outdated information?<\/li>\n<\/ul>\n
Trust, transparency, and human oversight principles<\/h3>\nMany organizations hesitate to adopt AI because they worry about losing control or making unexplainable decisions, and those concerns are well-founded: only one in five companies<\/a> reports having a mature governance model for autonomous AI agents. Your AI implementation should meet these principles:<\/p>\nCore trust requirements:<\/strong><\/p>\n\n- Explainability:<\/strong> You can see what the AI model did, why it did it, and what it will do next<\/li>\n
- Permissions and access control:<\/strong> You define exactly which data the AI can access<\/li>\n
- Human-in-the-loop:<\/strong> You can validate the AI’s actions before they take effect<\/li>\n
- Data privacy:<\/strong> The AI provider ensures your data stays private and is not used to train third-party models<\/li>\n<\/ul>\n"}]},{"main_heading":"How monday work management puts AI models into action","content_block":[{"acf_fc_layout":"text","content":"
Now that you understand what AI models are and how they work, here’s the real question: how do you put them to work across your organization? monday work management turns these AI model concepts into capabilities teams can use in their existing workflows.<\/p>\n AI agents that work alongside your team<\/h3>\n"},{"acf_fc_layout":"image","image_type":"normal","image":303737,"image_link":""},{"acf_fc_layout":"text","content":"monday work management embeds AI agents directly into the workspace where teams already get work done. These agents use the AI model types we covered in this article. Teams interact with them through natural language, not code.<\/p>\n Ready-made agents handle common business processes out of the box:<\/strong><\/p>\n\n- Risk Analyzer<\/strong> flags schedule and dependency risks across projects<\/li>\n
- Sentiment Detector<\/strong> monitors tickets and feedback for sentiment shifts in real time<\/li>\n
- Lead Scorer<\/strong> evaluates leads using fit, intent, and engagement signals<\/li>\n
- Meeting Summarizer<\/strong> creates notes, extracts action items, and assigns owners automatically<\/li>\n
- Ticket Assignment<\/strong> classifies intent, urgency, and required expertise, then routes accordingly<\/li>\n<\/ul>\n
Create custom agents for unique workflows<\/h3>\n"},{"acf_fc_layout":"image","image_type":"normal","image":321247,"image_link":""},{"acf_fc_layout":"text","content":"Any team member can build a custom agent in three steps: describe the agent’s role and triggers, connect relevant knowledge sources and integrations, then test and refine. No coding required.<\/p>\n What makes these agents effective is their access to data across departments. Because monday work management is the shared work environment for marketing, sales, operations, IT, HR, and more, agents can connect information across departments.<\/p>\n Built-in governance and enterprise-grade trust<\/h3>\nThe trust and transparency principles we covered earlier are built into monday work management’s AI infrastructure by default:<\/p>\n Security and control features:<\/strong><\/p>\n\n- Control:<\/strong> Explicitly define what each agent can and cannot do, both on monday.com and across connected external applications<\/li>\n
- Permissions:<\/strong> Set granular access levels determining which data each agent can read, edit, or create<\/li>\n
- Human-in-the-loop validation:<\/strong> Use simulation mode to review and validate agent actions before activating them<\/li>\n
- Compliance:<\/strong> HIPAA compliant, with ISO\/IEC 27001, SOC 2 Type II, and ISO\/IEC 27701 certifications<\/li>\n<\/ul>\n"}]},{"main_heading":"Getting started with AI models in your organization","content_block":[{"acf_fc_layout":"text","content":"
AI models can fundamentally change how your teams work, make decisions, and deliver results.\u00a0Start by evaluating your data quality and identifying workflows where AI can help right away\u2014processes involving pattern recognition, classification, or content generation.<\/p>\n Look for platforms like monday work management that embed AI capabilities directly into your existing workflows. monday agents work where your teams already collaborate, reducing context-switching and giving AI access to cross-departmental data that makes their actions smarter and more aligned with your organization’s needs.<\/p>\nTry monday agents<\/a>\n | |