Podcast Summary: Everyday AI Podcast

Episode: "AI Without the Jargon: The Language Every Business Leader Needs in 2026"

Host: Jordan Wilson

Date: January 16, 2026

Episode Overview

This episode kicks off Volume 2 of the "Start Here" series, focusing on breaking down the complex jargon of AI into practical, essential language for business leaders. Host Jordan Wilson draws from his extensive industry experience to highlight the importance of bridging the gap between technical teams and non-technical executives. The goal: Enable everyone—not just data scientists—to confidently communicate about, implement, and benefit from AI in the workplace.

Key Discussion Points & Insights

1. The Core Challenge: Communicating About AI

• Rapid evolution of AI tech: The pace of change outpaces most people's ability to keep up—even AI professionals.
  • “The tech changes faster than literally anyone can keep up with, even someone that talks about AI every day, like myself.” (00:17)
• Corporate education gap: Many companies don’t provide sufficient AI training, making it harder to talk about and use AI meaningfully.
• Jargon as a barrier: Specialized terms create a divide between technical and business teams.

“AI is a mystery wrapped up in ever changing jargon.” (01:47)

2. Why This Series Matters (and Who It’s For)

• Targeted both at technical teams (to understand business needs) and business leaders (to grasp AI’s basic terminology and practical implications).
• Empowers listeners to be “translators” for their organizations.

3. Setting the Stage: AI's Significance and Adoption Rates

• ChatGPT as a game-changer: Reaching 100 million users in 2 months (now 800-900 million weekly active users).
• Generative AI user adoption: 40% of Americans use generative AI—faster than the early internet.
  • “It took the Internet like five times as long to get that number of people actually using it.” (05:10)

4. The Communication Model of AI in Business

• The “Prompt-Action-Outcome” Framework:

  1. Human provides a prompt.
  2. Model executes (researches, drafts, retrieves).
  3. Human observes, verifies, and uses the outcome.

• Importance of using the right model, for the right purpose, and understanding terms like context window and tokens.

“So much of the outcome is not even decided by the prompt. It's decided by you using the right model, the right mode for the right reason, for the right task.” (10:58)

5. Demystifying Core AI Concepts

a) Tokens and Context Windows (15:15)

• Tokens: How AI reads and processes input—around 4 characters each; a single word can be split into multiple tokens.
• Context window: Like a hard drive for memory; as the buffer fills, the earliest tokens (and thus, context) are dropped.
  • “A context window is like a hard drive, but the difference is it's automatically going to keep working … it's just going to forget the first things you said.” (17:35)

b) Parameters

• The neural “horsepower”—more parameters mean more capability but higher resource consumption.
• Industry trend: Smaller, specialized models are becoming more common alongside generalist models.

6. Common Jargon Every Leader Should Know

| Term | Definition | Business Relevance | |-----------|--------------------------------------------------------------------------------------|---------------------------------------------| | RAG (Retrieval Augmented Generation) | Model grounds its response with relevant fetched data—better accuracy, less hallucination. | Vital for company-specific deployments. | | Embeddings & Vector Databases | Numeric representations for similarity search and matching; enables models to "find" relevant info fast and accurately. | Powers advanced internal search and document handling. | | Chunking | Breaking text into logical segments before they’re embedded, crucial for context and accuracy. | Impacts the quality of answers. | | Back-end vs. Front-end AI | Using LLMs via developer APIs (back-end) vs. consumer interfaces (front-end, e.g., ChatGPT page). | Non-technical vs. technical user experience. | | Connectors | Simple, click-to-integrate business data sources like email or Slack into AI workflows. | Drastically simplifies integrations. | | MCP (Model Context Protocol) | Standard for models/tools to communicate—industry-wide, facilitates interoperability. | Fast, safe deployment across stack. | | Scaffolding | The workflow structure for chaining multi-step/model processes. | Key for automation and autonomy. | | Agentic Models | Newer LLMs that can reason, make decisions, use tools, and interact with external systems. | Higher autonomy, broader use cases. | | Hallucinations | Confidently wrong answers by AI; can be mitigated by context engineering and the right design. | Business risk; requires ongoing vigilance. | | Prompt Injections | Malicious or unexpected instructions embedded in inputs/web pages. | Major security concern as AI agents gain web browsing. | | Guardrails | Human and algorithmic policies to enforce safety and prevent abuses. | Mandatory for enterprise scale AI adoption. |

7. Practical Application: The AI Translation Playbook

• Bringing technical and non-technical teams together:

  • “You need to bring your technical teams together with your people leading change management... you all need to be able to speak the same language.” (09:28)

• Key Questions for Effective Adoption: (29:06)

  • What's the real problem—and its current cost?
  • Which model, data, and tools to use? Who approves them?
  • How to balance speed, evaluation, and safety during deployment?
  • What are the hallucination and security risks?
  • Are traceability, observability, and expert oversight built in?

Notable Quotes & Memorable Moments

• “Yesterday’s expert is today’s beginner. And that is how quickly things change.” (07:28)
• “[AI models] don’t technically understand our words… The model itself, it doesn’t think in words; it thinks and produces in tokens and then converts it back.” (13:24)
• “Hallucinations... it’s essentially a lie, right? Or false statement or a half truth that’s put out there very confidently.” (27:14)
• “If you know what you're doing... hallucinations are, I'm not going to say they're gone, but they're essentially gone.” (27:57)
• “You can't treat AI implementation in the same way that we've treated tech implementation for the past few decades.” (30:23)
• “You need to get a 30 day plan. You need to be able to sprint. You need to understand how you're going to deal with mistakes.” (31:01)

Key Timestamps

• [00:17] – Three reasons AI communication is difficult
• [05:10] – ChatGPT and generative AI adoption stats
• [10:58] – The prompt-action-outcome framework explained
• [15:15] – Tokens and how models understand language
• [17:35] – Context windows and why they matter
• [22:40] – RAG, embeddings, and vector databases explained
• [26:00] – Rise of agentic models; connectors, MCP, and scaffolding
• [27:14] – Hallucinations and prompt injection risks
• [29:06] – Translation playbook: core business questions for AI
• [31:01] – How to build a practical AI action plan

Actionable Takeaways

• Start a translation dialogue: Bring together your technical and business teams and use the vocabulary outlined above for clarity.
• Develop a 30-day plan: Move quickly but carefully. Measure and adapt as you go.
• Ask the right questions: Regularly challenge assumptions about cost, risk, and ROI.
• Stay current: The language and best practices of AI evolve quickly; keep resources and internal glossaries up to date.
• Emphasize traceability: Use expert-driven feedback loops for accountability and improvement.

For Complete Resources & Ongoing Updates

• Visit StartHereSeries.com for access to this and future episodes, community discussions, and the Prime Prompt Polish ChatGPT prompt engineering course.

This episode equips business leaders and teams with the foundational AI language and translation skills needed to strategically engage with AI—today and in the rapidly changing landscape of 2026.