AI vocabulary moves fast. The same terms mean different things depending on who’s using them, and at a conference where concepts like agentic AI, grounding, and orchestration come up in keynotes, demos, and hallway conversations alike, fluency changes what you take home.

This guide covers the core AI concepts you’ll encounter across every track at Elevate ’26, the monday.com-specific terms worth knowing before you arrive, and how to apply this fluency during different session formats. You don’t need to memorize it. Scan what’s relevant to your role, bookmark it on your phone, and use it as a quick reference onsite.

What are the core AI terms you’ll hear at Elevate?

These are the foundational concepts that appear across keynotes, masterclasses, roundtables, and demos at Elevate ’26. They’re explained in plain language, with practical context for why they matter.

What is generative AI, and how is it different from other AI?

Generative AI creates new content — text, images, code — rather than analyzing what already exists. The AI capabilities built into work platforms, along with tools like ChatGPT, fall into this category.

The technology powering most generative AI text capabilities is large language models (LLMs). They’re trained on massive amounts of text, which lets them understand context, follow instructions, and generate human-like language. When you interact with an AI assistant inside a work platform or use AI to draft an update, summarize a project, or suggest next steps, you’re working with generative AI.

These terms come up in nearly every AI-focused session at Elevate. Understanding them helps you follow discussions about how AI generates suggestions, why outputs vary, and how the technology connects to real work.

What do prompts, context, and grounding mean in practice?

These three concepts work together. They’re what make AI output useful instead of generic.

Masterclasses at Elevate frequently focus on prompting technique and how to give AI better context. Understanding grounding helps you evaluate when AI output needs a second look.

What is the difference between automation and AI workflows?

This distinction comes up constantly at Elevate, and it matters for how you design processes.

Automation follows fixed logic: if X happens, do Y. It’s predictable, consistent, and reliable for standardized processes. Ideal when the inputs are always the same.

AI workflows incorporate AI decision-making at key steps, which allows for flexibility, pattern recognition, and handling of variability that would break traditional automations. The trigger might still be a status change, but instead of a fixed action, the AI interprets the situation and routes, summarizes, or responds based on context.

Choosing between the two isn’t about which is better. It’s about matching the tool to the process. Sessions in the PMO and Operations track at Elevate spend significant time on exactly this question.

What is agentic AI, and how is it different from an AI assistant?

This is probably the most important distinction to understand before arriving at Elevate ’26, because it sits at the center of the event’s theme.

AI assistants respond to requests and provide suggestions. They’re advisors. You still take the action — clicking send, saving a file, approving a decision. Every step requires your input.

AI agents take action autonomously within defined parameters. They execute, make decisions based on context, and complete workflows without waiting for you to confirm each step. Human oversight is built in at the points where it counts, not at every micro-step.

Agentic AI is the broader category describing AI systems designed to act with autonomy. The shift from assistant to agent isn’t just a product update. It’s a change in what becomes possible for a team — and what becomes necessary for governance.

Where you’ll hear it: Opening keynote, AI track sessions, customer stories about scaling operations.

What is human-in-the-loop design?

Human-in-the-loop is a design approach where humans review, approve, or guide AI actions at specific decision points. The goal is to capture the speed and scale of AI while keeping human judgment where it’s genuinely needed.

It’s the answer to “but what about oversight?” that comes up in nearly every enterprise AI discussion. Well-designed human-in-the-loop systems aren’t a workaround for AI you don’t trust. They’re intentional architecture for AI you deploy responsibly.

Where you’ll hear it: Executive track, governance sessions, IT and ops discussions about enterprise deployment.

What does AI governance mean at the organizational level?

Governance covers the policies, processes, and controls that guide how AI is used across an organization: who can access AI capabilities, what data AI can use, how AI-driven decisions get reviewed, and who’s accountable when something goes wrong.

As AI moves from individual productivity tools to organization-wide deployment, governance stops being a compliance checkbox and becomes a strategic function. Sessions at Elevate covering AI at scale consistently return to governance as the variable that separates successful rollouts from stalled ones.

Where you’ll hear it: Executive track, IT sessions, customer stories from enterprises leading AI transformation.

What monday.com AI terms should you know before Elevate?

This section covers the platform-specific terms you’ll see in demos, masterclasses, and customer stories. The general fluency above is more important. Think of this as the layer on top.

1. monday AI is the umbrella term for all AI capabilities built into monday.com. Because these capabilities work with the structured data already in your workflows, the outputs are grounded in your actual work rather than generic assumptions.
2. monday sidekick is monday.com’s AI assistant. It can summarize project status, draft stakeholder updates, surface items that need attention, and take action across connected platforms. It sits between a pure assistant and a full agent — it responds to prompts and can execute actions, but you define the boundaries.
3. monday agents are AI agents that operate autonomously within defined parameters on the platform. You can use pre-built agents designed for specific functions (sales development, meeting scheduling, pipeline management) or configure custom agents for your own processes.
4. monday workflows and AI blocks are how AI processes get built on the platform. Workflows are visual, multi-step processes that incorporate AI decision-making. AI blocks are pre-built components — categorization, summarization, sentiment analysis, intelligent routing — that you combine without writing code.
5. monday vibe is monday.com’s capability for building custom applications using AI. You describe what you need, and Vibe generates functional applications without requiring development resources.
6. monday AI Notetaker joins your Zoom, Teams, or Google Meet sessions and turns conversations into structured outputs: real-time transcription, speaker identification, summaries, and action items that connect directly to your boards and workflows. You can ask follow-up questions about the meeting using an AI chat after it ends.

Where you’ll hear all of this: Product keynotes, hands-on masterclasses, and customer stories across every track.

Which AI terms matter most for your role?

How do you get more from sessions when you know the terminology?

Vocabulary is the entry point. Applying it during sessions is how you actually get more from every conversation.

In keynotes, when you hear “agentic AI,” listen for what tasks agents complete autonomously, where humans stay in control, and what business outcomes organizations report. When you hear “AI at scale,” listen for what infrastructure decisions enabled it and what surprised leaders about enterprise deployment.

In masterclasses, when instructors demonstrate prompts, notice how they structure requests, what context they provide, and how they iterate when the first result needs refining. When AI workflows are being built live, track what triggers the workflow, where AI makes decisions versus follows rules, and how output gets validated before action is taken.

In roundtables and customer stories, when someone describes their AI implementation, listen for what problem they actually started with, what they tried first, and what they adjusted. The gap between what they planned and what worked is usually where the most useful insight lives.

In live demos, when you see agents in action, track what data the AI accesses, how it determines what action to take, and where behavior can be customized. When new capabilities are shown, listen for what’s available now versus on the roadmap.

Before Elevate: scan the sections relevant to your role and note 2-3 terms you want to understand better. Pull up the agenda and identify the sessions that connect to them. Come in with one or two questions you want to ask live.

During Elevate: use this as a lookup. When something unfamiliar comes up onsite, you’ve got a reference in your pocket.

After sessions: the terms here — assistants vs. agents, context and grounding, automation vs. AI workflows — are the building blocks that make everything else make sense. They’ll serve you well beyond the two days.

FAQs about AI at Elevate

• Do I need technical experience to follow AI sessions at Elevate?

  No. Elevate sessions are designed for practitioners across all functions and technical levels. This guide gives you the baseline fluency to follow along even if you’re new to AI concepts.

• How do I pick sessions that match my current AI knowledge level?

  Check session descriptions against this glossary. If you understand the key terms in a description, you’re ready for that session. If several terms are unfamiliar, that session is a good stretch goal — worth attending with this guide open.

• Does the pre-conference Certification Day cover these same AI concepts?

  Yes. The 2026 certifications feature a new AI-focused curriculum that includes hands-on practice with many of the concepts in this guide. The training takes place on October 27 at the Javits Center, the day before the main event.

• How do I explain what I learned at Elevate to my team when I return?

  Share this guide as a shared reference when debriefing. Focus on 3-5 terms most relevant to your team’s work and connect them to specific examples you picked up during sessions. Concrete examples land better than definitions.

• Should I prepare differently if I’m attending with a group?

  Yes. Share this guide before you arrive so everyone has common vocabulary. Then divide sessions based on role-relevant terms — you’ll cover more ground and the debrief afterward will be sharper.

Elevate Team

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