Shelly Palmer - How to Talk About AI in a Meeting

When someone says, “Google it,” everyone understands the request instantly. AI, however, isn’t as straightforward—terms evolve rapidly, and definitions often vary. This ambiguity leads to confusion, misalignment, and inefficient meetings.

A simple fix: Start AI-focused discussions by defining key terms upfront. A quick two-minute clarification—“Since AI is new territory for many of us, let’s define some key terms before we get started”—can dramatically improve alignment and productivity.

Here’s an executive-ready glossary of essential AI terms to ensure you and your team are speaking the same language.

P.S. Today’s article was inspired by real life events. Any resemblance to actual meetings you’ve attended is purely intentional. If you've ever found yourself trapped in a jargon-filled AI discussion where it turned out that no one was talking about the same thing, you are not alone. To share your story—just reply to this email. Your secret will be safe with me.

Large Language Models (LLMs)

LLMs are advanced AI models trained on vast datasets to understand and generate human-like text. They serve as the foundation for many AI applications, from chatbots to research assistants. Below are the top 10 LLMs listed in order of popularity:

1. OpenAI GPT-4.5 / GPT-4o – OpenAI’s latest iterations in the GPT series, known for their advanced conversational abilities and text generation.
2. Anthropic Claude – Designed with a focus on safety and interpretability, Claude is tailored for enterprise applications requiring reliable AI interactions.
3. Meta Llama – Meta’s open-source LLM, Llama, is recognized for its accessibility and adaptability across various tasks.
4. Amazon (Nova) – Amazon’s LLM, Nova, is integrated into its cloud services, offering scalable AI solutions for diverse applications.
5. Google Gemini – Google’s Gemini series is optimized for multimodal inputs, seamlessly integrating text, images, and code.
6. DeepSeek V3 – A general-purpose LLM known for its robust natural language processing and understanding capabilities.
7. Mistral – An open-source model praised for its efficiency and performance despite a smaller parameter size.
8. Cohere Command R – Cohere’s LLM focuses on retrieval-augmented generation, enhancing its ability to provide accurate and contextually relevant responses.
9. xAI Grok – Developed by xAI, Grok is integrated with platforms like X (formerly Twitter) to enhance user interactions.
10. Stability AI’s Stable LM – An open-source LLM offering a balance between performance and accessibility for various AI applications.

Reasoning Engines

Reasoning engines are AI models specifically designed to solve complex problems, break down logic steps, and offer structured solutions beyond simple text prediction. These models are optimized for strategic decision-making, mathematical reasoning, and structured inference. Examples include:

• OpenAI o3 – Optimized for problem-solving, advanced reasoning, and structured decision-making tasks.
• DeepSeek R1 – A retrieval-augmented reasoning engine designed to enhance structured problem-solving by integrating real-time data retrieval with logical inference.
• Google DeepMind AlphaGeometry – Designed to solve advanced mathematical and geometric problems.

Diffusion Models

Diffusion models generate images and videos by refining visual noise over multiple steps. They power many of today’s AI-driven creative tools. Examples include:

• OpenAI DALL-E 3 – Generates detailed and stylized images from text prompts.
• Midjourney – Specializes in artistic and high-resolution image generation.
• Stable Diffusion – Open-source model used for custom AI image generation.
• Veo 2 (Google) and Sora (OpenAI) – Both generate AI-driven video, with Veo 2 focusing on professional applications and Sora optimized for dynamic scene generation.

Agent

An AI agent is software that performs specific tasks autonomously, often incorporating large language models (LLMs) and specialized reasoning. Agents act as digital workers, handling tasks like retrieving information, scheduling, generating documents, and monitoring systems. Examples include:

• Customer Support Agents (Google Gemini, Amazon Bedrock, OpenAI ChatGPT) – Handle inquiries, retrieve documentation, and provide responses without human intervention.
• Document Processing Agents (Amazon Bedrock, Anthropic Claude) – Extract, categorize, and summarize documents for compliance, finance, or HR teams.
• Marketing Agents (Google Vertex AI, OpenAI DALL-E) – Generate ad copy, emails, or visual assets tailored to audience segments.

Agentic Systems

An agentic system is a coordinated group of AI agents working together toward complex goals. Unlike standalone agents, these systems autonomously make decisions and execute workflows at scale. Examples include:

• Customer Experience System (Google Vertex AI) – Combines chatbots, predictive analytics, and personalization agents to enhance customer interactions.
• Compliance & Risk Management System (Amazon Bedrock, Anthropic Claude) – Integrates document review and policy enforcement agents for regulatory oversight.
• Marketing Automation System (OpenAI, Google Vertex AI) – Uses segmentation, campaign management, and content generation agents to execute full-scale marketing initiatives.

Deep Research Tools

Deep research tools are AI-powered systems designed to autonomously gather, synthesize, and analyze vast amounts of information. These systems are often prebuilt agentic frameworks that go beyond simple search or summarization by conducting comprehensive data-driven research. Examples include:

• OpenAI Deep Research – Uses real-time internet access to gather, verify, and synthesize information for decision-making.
• Perplexity AI – AI-powered research assistant that retrieves, synthesizes, and presents well-structured responses from multiple sources.
• xAI Grok Research – A deep research tool integrated into X (formerly Twitter), designed to retrieve, synthesize, and analyze real-time information from public data sources, enhancing contextual awareness and decision-making.
• Elicit – An AI tool designed for academic and business research, automating literature reviews and structured data extraction.
• Google Gemini Advanced – An AI assistant optimized for interactive, research-driven conversations and contextual analysis.
• IBM Watson Discovery – Enterprise-grade AI research tool that extracts and organizes data insights from large-scale unstructured sources.

Low-Code & No-Code AI

These platforms enable users to build AI-powered workflows without extensive programming knowledge.

• Low-Code: Requires minimal coding, allowing for customization and rapid deployment.
  Examples: Zapier, Microsoft Power Automate, Google AppSheet
• No-Code: Uses drag-and-drop interfaces, making AI accessible to non-technical users.
  Examples: Canva (AI-powered design), Wix (website creation), Airtable (database automation)

AI Glossary For Today’s Meeting

• Large Language Models (LLMs) – AI models trained to understand and generate human-like text.
• Reasoning Engines – AI designed for structured problem-solving and logical inference.
• Diffusion Models – AI that generates images and videos by refining visual noise over multiple steps.
• Agents – Autonomous AI systems that execute specific tasks based on objectives.
• Agentic Systems – Groups of AI agents working together to automate complex workflows.
• Deep Research Tools – AI-powered systems that retrieve, synthesize, and analyze large amounts of information.
• Low-Code AI – Platforms requiring minimal coding to build AI-powered workflows.
• No-Code AI – Drag-and-drop platforms that allow non-technical users to build AI applications.

These terms will continue to evolve, just as AI itself does. While we may not yet have an AI equivalent of “Google it,” taking a moment to align on definitions in meetings will ensure clarity, better decisions, and stronger business outcomes.

Author’s note: This is not a sponsored post. I am the author of this article and it expresses my own opinions. I am not, nor is my company, receiving compensation for it. This work was created with the assistance of various generative AI models.

About Shelly Palmer

[email protected]

Shelly Palmer is the Professor of Advanced Media in Residence at Syracuse University’s S.I. Newhouse School of Public Communications and CEO of The Palmer Group, a consulting practice that helps Fortune 500 companies with technology, media and marketing. Named LinkedIn’s “Top Voice in Technology,” he covers tech and business for Good Day New York, is a regular commentator on CNN and writes a popular daily business blog. He's a bestselling author, and the creator of the popular, free online course, Generative AI for Execs. Follow @shellypalmer or visit shellypalmer.com.