Everyday AI Podcast – Ep 705: How to Train Your Team on AI: The 7 Steps to Educate Your Organization on LLMs Host: Jordan Wilson
Date: February 3, 2026

Episode Overview

In this episode of Everyday AI, host Jordan Wilson breaks down "the seven steps to educate your organization on using large language models (LLMs)" as part of the podcast's "Start Here" series. Drawing on years of AI consulting experience, recent industry statistics, and practical common sense, Jordan outlines a focused roadmap for leaders, managers, and teams struggling to move from AI theory and hype to effective, organization-wide adoption. The core message: it's not just about buying AI tools, but about comprehensive, culture-driven education and workflow redesign.

Key Discussion Points & Insights

The Widening Gap in AI Adoption (00:16–03:40)

• AI Investment ≠ AI Maturity
  • Companies are pouring money into AI (buying tools like Microsoft Copilot and ChatGPT Enterprise) but skipping employee education.
  • “The gap that I’m talking about is alarming. According to a McKinsey study, 92% of companies plan higher AI investment. Only 1% called their deployments mature.” — Jordan Wilson [02:53]
• Lack of Mandated Training
  • Only 30% of large enterprises will mandate AI training by the end of 2026.
  • Many organizations pay lip service to AI, but few integrate it meaningfully.

The 7 Essential Steps to Training Teams on AI

Step 0: Take the Free Prime Prompt Polish Course (04:12)

• A recommended foundation for anyone starting their AI journey.
• “Step zero would be take our free prime prompt polish course...about hour and a half, two hours.” [04:35]

Step 1: Leadership Must Go First (04:56–08:30)

• Not a Top-Down Mandate, But Leading by Example
  • AI only gets embedded when CEOs and senior leaders use it daily.
  • “The CEO must use it daily.” [05:15]
  • Example: Jim Kavanaugh, CEO of WWT, insists on AI-first work.
• Cultural Moments & Internal Spotlights
  • Celebrate wins, all-hands demos, and internal showcases to build momentum.

Step 2: Fix Broken Workflows Before Adding AI (08:31–11:07)

• AI Doesn't Fix Inefficiency
  • “Before you throw makeup on an ugly pig, it's still an ugly pig.” [08:35]
  • AI accelerates flaws in existing workflows; optimize first, tech second.
• Redesign for AI-First
  • Don’t layer AI onto outdated processes; instead, rethink workflows for automation and data flow.

Step 3: Pick One AI Platform and Commit (11:08–15:42)

• Focus on a Single Operating System
  • Avoid “shiny object syndrome” and “AI sprawl” by standardizing on a primary AI toolset (GPT, Gemini, or Claude).
  • “You should all be learning and sandboxing in the same AI operating system...because what happens when you start using...ChatGPT for this, Gemini for this...then you're gonna use these ten other tools as well.” [13:34]
• Start Where Your Data Lives
  • Align with your cloud provider’s AI stack for smoother integration.
• Shadow IT & Security Risks
  • Employees use AI informally (“second computer AI”) if no official solution is adopted.

Step 4: Train in Three Layers: Literacy, Domain, Data (15:43–19:54)

• Not Just Upskilling: Rebuilding & Unlearning
  • “Sprinkling the word upskill and reskill. That’s a great way to fail... You have to unlearn and rebuild.” [16:13]
• Three Layers of Training
  • 1. AI Literacy (Org-wide basics)
  • 2. Domain-Specific by Role or Department
  • 3. Understanding Data & Procedures
    • Garbage data in, garbage outputs out; data practices matter even more with AI.

Step 5: Document Your Procedures, Not Just Your Data (19:55–22:34)

• Capturing Human Intelligence
  • Start documenting unique workflows, tacit knowledge, and decision trees (“Deborah’s brain”).
  • “Start seeing how AI and Deborah can better coexist...that is the last mile problem of AI implementation.” [20:53]
• Going Beyond Structured Data
  • Focus on curating the ‘how’ and ‘why’ behind decisions, in addition to what is being done.

Step 6: Mandate Hands-On Practice With Real Outputs (22:35–24:55)

• Practice > Theory
  • Organize regular, intentional practice sessions (e.g., “Friday lunch” hackathons).
  • “Employees need hands on keyboard...not just on their phone. You need to be sharing, you need to be workshopping.” [23:00]
• Measure Real Output
  • Don’t just track tool usage—instead, focus on what’s being produced and how it improves work.

Step 7: Go From Operator to Orchestrator (24:56–28:08)

• Orchestration is the Future
  • The goal is not just using AI, but designing processes in which humans oversee and orchestrate networks of AI agents.
  • “How do I get myself out of that? Just orchestrating it, right? It's being done automatically for me...I don’t have to do anything with it. That's the big push and that is step seven.” [27:27]
• Shift in Job Roles
  • Future jobs will be about overseeing, validating, and refining AI-driven processes, not manual execution.

Notable Quotes & Memorable Moments

• On the AI Maturity Gap:
  “92% of companies plan higher AI investment. Only 1% called their deployments mature.” — Jordan Wilson [02:53]

• On Leadership:
  “When I say leadership must go first, I’m saying the CEO must use it daily...If your top people are not actually using it, it's not actually going to work.” — Jordan Wilson [05:15]

• On AI as a Bandaid:
  “AI isn’t going to fix your broken processes...it’ll make things worse.” — Jordan Wilson [09:04]

• On Tool Sprawl:
  “Not focusing on a single AI operating system leads to that shiny AI object syndrome.” — Jordan Wilson [13:55]

• On Training:
  “You can’t just think of upskilling and reskilling. You have to unlearn and rebuild.” — Jordan Wilson [16:13]

• On Company Knowledge:
  “Start documenting what Deborah does. Start documenting what Deborah knows. Start seeing how AI and Deborah can better coexist. Because that is…the last mile problem of AI implementation.” — Jordan Wilson [20:25]

• On Orchestration:
  “How do I get myself out from being the operator...? How do I get myself out of that? Just orchestrating it, right? It's being done automatically for me.” — Jordan Wilson [27:14]

Timestamps for Key Segments

• 00:16 – AI investment outpacing employee education
• 02:53 – Alarming industry stats about AI maturity
• 04:12 – The “Start Here” approach and suggested free course
• 04:56 – Step 1: Leadership must go first
• 08:31 – Step 2: Fix broken workflows before adding AI
• 11:08 – Step 3: Pick one AI platform and commit
• 15:43 – Step 4: Training in three layers (literacy, domain, data)
• 19:55 – Step 5: Document procedures, not just data
• 22:35 – Step 6: Mandate hands-on practice with real outputs
• 24:56 – Step 7: From operator to orchestrator

Actionable Takeaways

• Leaders: Use AI daily; set the example.
• Managers: Audit and fix your team’s workflows before introducing tools.
• Teams: Train together, first at a basic literacy level, then in specialized contexts.
• Everyone: Document not just what you do, but how and why you do it.
• Organizations: Make hands-on practice standard and focus on output, not usage statistics.
• Future-Proofing: Shift your mindset to overseeing and orchestrating AI—this will define knowledge work in years to come.

For more details, community access, and resources:
Start Here Series
Prime Prompt Polish Course
Everyday AI Newsletter

This episode is essential listening for anyone tasked with, or interested in, bringing AI-enabled workflows into their workplace. Jordan’s actionable framework, grounded in real-world consulting and teaching, serves as a step-by-step handbook for the AI transformation every organization will need.