Everyday AI Podcast – An AI and ChatGPT Podcast

Ep 847: How to Train Your Team on AI: The 7 Steps to Educate Your Organization on LLMs (Start Here Series Vol 6)

August 24, 2026

AI Summary

5 min read

In 2026, 92% of companies plan to increase their AI investment, but only 1% describe their deployments as mature, according to a McKinsey study. Meanwhile, Forrester predicts that by the end of the year, fewer than one in three large enterprises will even mandate AI training. That gap—between spending on tools and actually teaching people how to use them—is the problem Jordan Wilson addresses in this episode of the Everyday AI Podcast. He lays out seven steps for training a team on large language models, grounded in the psychology of mindset shifts and organizational change rather than just tool tutorials.

Why most AI training fails before it starts

Wilson argues that the most common corporate approach to AI is backward. Companies buy thousands of enterprise seats for Microsoft 365 Copilot or ChatGPT Enterprise, and CEOs fill all-hands meetings with buzzwords, but they skip the most basic step: teaching employees how to actually use the model they just paid for. The result is what he calls a growing divide between "the haves and the have-nots," where roughly 90% of companies fall into the latter camp.

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What you'll learn

1 (00:00) **Introduction: The AI Training Gap** - Jordan Wilson introduces the episode, explaining that despite massive corporate AI investment, most companies fail to train their employees on how to actually use LLMs.
2 (05:18) **Step 1: Leadership Must Go First** - The CEO and top executives must use AI daily, not just mandate it from the top down.
3 (08:01) **Step 2: Fix Broken Workflows Before Adding AI** - Adding AI to a broken process makes things worse, not better.
4 (09:42) **Step 3: Pick One AI Platform and Commit** - Choose one of the three main AI operating systems (ChatGPT, Claude, or Gemini) and move most critical processes into it.
5 (15:07) **Step 4: Train in Three Layers** - Move beyond "upskilling" to rebuilding and unlearning, because AI is smarter than humans at most knowledge work.
6 (18:46) **Step 5: Document Your Procedures, Not Just Your Data** - Capture the human intelligence and decision-making processes of your best employees.
7 (21:37) **Step 6: Mandate Hands-On Practice with Real Outputs** - Employees need structured, hands-on keyboard time to build and share AI solutions.

+ Full timestamped outline available in the app

Guests on this episode

Jordan Wilson

Show Notes

Like 99% of companies are pushing AI. 🚀

But like 0.01% are actually training their people on it. 🤦

Don't worry. We'll go over the essentials on how to get started training your team or getting your company trained on best practices for using AI in your day-to-day.

First step to solving the AI implementation gap starts here.

How to Train Your Team on AI: The 7 Steps to Educate Your Organization on LLMs -- An Everyday AI Chat with Jordan Wilson

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Topics Covered in This Episode:

AI Training Adoption Gap in Organizations
Seven Steps to Train Teams on AI
Importance of Leadership in AI Adoption
Fixing Broken Workflows Before AI Integration
Choosing and Committing to One AI Platform
Training Employees in Three AI Knowledge Layers
Documenting Procedures for AI Implementation
Mandating Hands-On AI Practice and Outputs
Transitioning Teams from AI Operators to Orchestrators

Timestamps:

00:00 "AI Spending, Training Gap Looms"

05:48 "Embracing AI-First Leadership"

08:43 "Choosing Between AI Copilots"

10:22 "Unified AI Operating System Strategy"

14:46 Partnering for Generative AI Success

18:40 "Document Processes for AI Workplaces"

19:58 "AI OS for Decision Trees"

24:56 "Future Jobs: Orchestrating Tasks"

27:33 "Training Teams on AI"

Keywords:

AI training, large language models, corporate AI adoption, AI literacy, AI education, team AI training, employee AI training, organization AI strategy, AI workflows, workflow optimization, AI platform selection, AI operating systems, Microsoft 365 Copilot, ChatGPT Enterprise, Google Gemini, Anthropic Claude, shadow IT, AI tool sprawl, data documentation, procedure d