Everyday AI Podcast – An AI and ChatGPT Podcast

Latest Episodes:

Ep 853: The State of the AI Race. Who will win in 2026: OpenAI, Microsoft, Google Or Anthropic (Start Here Series Vol 12) Sep 01, 2026

Show notes

Who will win the AI race in 2026? 🏇
Will Google finally catch OpenAI in users?
Has Claude surpassed ChatGPT in capabilities?
And.... how can your company avoid the AI battles and just pick its lane?
So many AI questions. We've got your AI answers.
As part of our ongoing 'Start Here Series' we tackle one of the most important questions for most enterprises: Who will win the AI race and how do you decide?
The State of the AI Race. Who will win in 2026: OpenAI, Microsoft, Google Or Anthropic -- An Everyday AI Chat with Jordan Wilson

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Topics Covered in This Episode:

AI Race 2026: OpenAI, Microsoft, Google, Anthropic
Chatbot Era Ending: Rise of Agentic Systems
OpenAI vs Google vs Anthropic User Stats
Large Language Model Benchmark Comparisons
Enterprise AI Adoption Trends
Agentic Workflow Automation: Business Impacts
Microsoft Copilot and Governance Features
Anthropic Claude’s Premium Intelligence Integration

Timestamps:

00:00 "2026 AI Race Predictions"

04:01 "AI's Daily Driver Era Arrives"

08:31 AI Revenue and IPO Trends

10:10 "Revenue Success with Fewer Users"

13:42 AI Integration and Market Impact

19:49 Microsoft's Role in OpenAI Investments

21:00 "Microsoft vs Google: AI Investments"

26:42 "Anthropic's Focus on Specialized AI"

30:34 Microsoft Copilot's AI Evolution

31:28 Microsoft Copilot: CEO's Hands-On Vision

35:13 AI Coding Models Market Trends

38:44 "Modular AI for Enterprise Success"

42:06 "Everyday AI: Subscribe Now"

Keywords:

AI race, state of AI 2026, OpenAI, Microsoft, Google, Anthropic, enterprise AI, consumer AI, agentic systems, AI workflow, large language models, multimodal AI, Gemini, ChatGPT, Copilot, Claude, Claude Opus, Claude SONNET, AI operating system, AI agent,

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Ep 852: Measuring AI ROI: Why you’re doing it wrong and the 7 Steps to fix it (Start Here Series Vol 11) Aug 31, 2026

Show notes

Why can't most companies show their ROI on GenAI?
Because their implementation is backwards.
If you're using the same digital transformation playbook that you used for the social media and cloud eras, you're in trouble.
On this 'Start Here Series' episode, we break down what your company is doing wrong and the 7 Step process to properly calculate ROI on your AI efforts.
Measuring AI ROI: Why you’re doing it wrong and the 7 Steps to fix it -- An Everyday AI Chat with Jordan Wilson

Topics Covered in This Episode:

Proving and Measuring AI ROI in Companies
OpenAI’s GDP Benchmark: AI vs Experts
Debunking MIT’s Viral Zero ROI Study
Quantitative AI ROI Data from Business Studies
Invisible Productivity: AI Savings Pocketed by Workers
Limitations of Pre-AI Job Roles and Metrics
ROI Calculation: Time Saved and Cost Reduction
Five Main Reasons AI ROI Isn’t Measured
Seven-Step AI ROI Measurement Blueprint
Importance of Ongoing AI Model Retesting
AI ROI: Training, Education, and Implementation Gaps

Timestamps:

00:00 "AI ROI: Fixing the Debate"

04:50 "AI ROI Debate is Pointless"

08:03 AI Implementation Yields Positive ROI

12:13 AI Reshaping Work Structures

15:08 AI ROI Challenges

20:11 "Baseline Assessment Before AI Implementation"

23:49 "AI Operating System Discussion"

24:35 "Standardized Testing for AI Models"

28:26 "Rethink AI ROI Urgency"

31:37 "Everyday AI: Subscribe & Explore"

Keywords:

measuring AI ROI, AI return on investment, generative AI ROI, GenAI ROI measurement, ROI on artificial intelligence, AI productivity, time saved with AI, cost reduction with AI, AI-driven revenue increase, AI risk avoidance, AI implementation, baseline assessment of AI, BASE framework, pre-AI baseline, digital transformation and AI, quality metrics in AI, cost per task AI, error rates AI, throughput AI, AI utilization rate, AI pilot evaluation, AI business impact, AI model benchmarks,

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Ep 851: From AI Chatbot to Autonomous Coworkers: How Consumer AI Has Changed and What's Next (Start Here Series Vol 10) Aug 28, 2026

Show notes

If your entire company was using ChatGPT in 2022.... good chance you ended up in some trouble. 😬
Now if you're company is NOT using AI in 2026.... you'll definitely be in trouble.
But a different kind.
That's how quickly AI has changed the business landscape. What was once a megaviral party trick in 2022 is not the lifeblood of the American Enterprise.
So.... how the heck did it happen so fast?
In our 10th volume of the 'Start Here Series,' we break it all down.
From AI Chatbot to Autonomous Coworkers: How Consumer AI Has Changed and What's Next - An Everyday AI Chat with Jordan Wilson

Topics Covered in This Episode:

Five Phases of AI-Enabled Work
From Chatbots to Autonomous AI Coworkers
Large Language Models Evolution Timeline
Business Chat AI and Team Integration
Agentic AI Era—Models with Reasoning
Desktop AI Agents and Local File Access
Security and Governance for Desktop AI
Scheduling Layer in AI Agents
Key AI Agent Products and Comparisons
Actionable Strategies for AI Implementation

Timestamps:

00:00 From Chatbot to Autonomous Coworker

03:14 AI's Desktop Agent Revolution

06:47 AI Phases and Overlap Explained

10:55 "Early AI Models Were Ineffective"

13:51 AI Transforming Work by 2025

17:14 "Shift to New Era AI"

21:20 "AI Agents Controlling Computers"

24:11 "OpenAI's Multi-Agent Claude Launcher"

28:21 "Future of AI in Business"

30:21 "Proactive AI Revolutionizing Work"

Keywords:

AI chatbot, autonomous coworker, consumer AI, five phases of AI-enabled work, AI agents, desktop AI, agentic era, business chat, scheduling AI, scheduled tasks, large language models, generative AI, ChatGPT, November 2022 AI launch, Microsoft Copilot, Anthropic Claude, Google Gemini, reasoning models, agentic browsers, AI coworkers, virtual AI agents, Codex, Claude Cowork, Claude Code,

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Ep 850: Agent Risk, Security, and AI Sprawl in 2026: Why AI That Acts Changes Everything (Start Here Series Vol 9) Aug 27, 2026

Show notes

The perfect AI storm happened, and no one has noticed yet. 😬
And when everyone finally realizes, they'll be too busy focused on the shiny new AI capabilities to see the straight up agentic nightmare looming right in front of them.
I'll be honest though.
↳ Yeah, there's a dark side to AI agents.
↳ Yeah, it's worse than you think.
↳ Yeah, we're gonna talk about.
Agent Risk, Security, and AI Sprawl in 2026: Why AI That Acts Changes Everything – An Everyday AI Chat with Jordan Wilson (Start Here Series Volume 9)

Topics Covered in This Episode:

Evolution of AI Agent Risk (2022-2026)
Agentic AI Security Threats Explained
Types of Agent Sprawl & Dark AI
Key Differences: Chatbot vs Agent Risk
Major AI Labs' Risk Response Strategies
Agent Actions, Outputs, and Enterprise Risk
Monday Morning Playbook for AI Security
Agent Skill Marketplace & Supply Chain Risk

Timestamps:

00:00 AI Risks: A Rapid Evolution

06:22 "Applying Buzzwords to Practice"

07:31 AI Evolution: Dumb to Proactive

13:23 "Expanding Blast Radius with Tools"

14:04 "AI Actions: Enterprise Nightmare"

17:16 "Start Here: AI Basics"

22:50 "Agentic Web and Business Value"

24:09 "Advances Driving AI Agent Efficiency"

27:30 AI Labs: Balancing Risk and Research

32:04 "Autonomous AI Model Concerns"

33:43 "Managing Autonomous Agent Risks"

37:08 "Foundations First for Future Success"

41:00 "Understanding Risks and Opportunities"

Keywords:

AI risk, agent risk, security risk, AI sprawl, AI agents, agentic AI, AI model actions, business AI risk, corporate AI security, agent risk mental model, OpenAI, Google, Anthropic, Microsoft, governance, audit logs, isolation protocols, agentic capabilities, smart AI models, proactive AI, autonomous agents, agent hallucination, rogue AI agents, prompt injection, malware agents, spyware agents, ransomware agents, agent input risk, tools risk, outputs to actions, enterprise AI nightmare, shadow AI,

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Ep 849: AI Agents in 2026 Explained: What They Are and When You Should Use Them (Start Here Series Vol 8) Aug 26, 2026

Show notes

AI Agents. AI Agents everywhere. 🪐
For what seems like an eternity, we've been hearing about how AI agents were going to do everything. (In a good and bad way)
But it never really happened.
Until now.
Today, AI agents are more important than ever. But... the questions are still legit plentiful.
↳ What actually is an AI agent?
↳ How do they work?
↳ And what are the best ways to implement them?
We'll tackle those questions and more, LIVE.
AI Agents in 2026 Explained: What They Are and When You Should Use Them -- An Everyday AI Chat with Jordan Wilson (Starter Series, Vol 8)

Topics Covered in This Episode:

AI Agents 2026: Definition & Overview
AI Agents vs Traditional AI Chatbots
Current State of AI Agents in Enterprises
AI Agent Adoption Statistics & Market Trends
Key Differences: Workflow Automation vs AI Agents
Types of AI Agents: Task, Process, Decision
Autonomous vs Passive AI Agents Explained
Real-World Risks: Security & Shadow AI Tools
AI Agent Use Case Decision Framework
Best Practices: Starting AI Agent Pilots
Measuring AI Agent ROI & Performance
Building Effective Agent Ecosystems

Timestamps:

00:00 "Join Our Inner Circle"

05:29 "Understanding AI Agents"

08:41 "Autonomous Agents as Workers"

11:04 Agentic AI: Decisions and Challenges

17:00 "Shadow AI Poses Growing Risks"

18:00 "Agent Creativity and Loopholes"

22:04 "Agentic Transactions and AI Workflows"

25:27 "Getting Started with AI Agents"

27:20 "Future Success in AI Agents"

Keywords:

AI agents, autonomous agents, agentic AI, multi-agent systems, AI agent definition, agentic models, autonomous systems, AI chatbots, AI-powered workflows, decision frameworks for AI agents, enterprise AI adoption, agent guardrails, AI task automation, passive agents, bounded autonomy, agent observability, agent traceability, agentic first workflow, AI delegation, sub-agents, tool-using AI,

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Ep 848: Context Engineering: How to Get Expert-Level Outputs From AI Chatbots (Start Here Series Vol 7) Aug 25, 2026

Show notes

How did prompt engineering die so quickly? ☠️
And what the heck does context engineering even mean?
One of the trickiest things about LLMs is they're changing daily, yet they're the engines that drive business results.
But if the engine is constantly changing, then you also have to change how you drive and the roads you take.
That's why we're tackling context engineering in this installment of our Start Here Series, the essential beginners guide to understanding AI basics and growing your skills.
Context Engineering: How to Get Expert-Level Outputs From AI Chatbots -- An Everyday AI Chat with Jordan Wilson

Evolution from Prompt to Context Engineering
Why Prompt Engineering Is Now Obsolete
Defining Context Engineering in AI Chatbots
Six-Part Framework for Context Engineering
Four Layer System for Structuring AI Context
Building Reusable Context Vaults and Skills
Connecting Business Data to AI Models
Techniques to Achieve Expert-Level AI Outputs
Importance of Context Windows in Large Language Models
Context Engineering Best Practices and Scalability

Timestamps:

00:00 "Access AI Community & Tools"

03:08 "Mastering Context in AI"

07:23 "Smart Models Require Less Precision"

12:01 "Context Engineering Beats Prompt Engineering"

15:49 "AI Context: Six Key Blocks"

16:47 "Building Context for Better Results"

19:53 "AI: Training, Not Easy Button"

25:17 "Chain of Thought Prompting Decline"

29:11 "Show, Don't Tell Techniques"

32:13 "Context, Reuse, and Scalable Systems"

33:19 "AI Chatbots: Memory and Skills"

Keywords:

context engineering, AI chatbots, expert level outputs, prompt engineering, large language models, business context, AI models, custom instructions, data access, context window, prime prompt polish, reusable context vaults, context vaults, skills file, memory enabled models, ChatGPT, Claude, Google Gemini, Microsoft Copilot, connectors, apps, searchable index, business data, personalized AI, context clues, reference material, examples, procedures, evaluation rubric, chain of thought prompting, generative AI, nondeterministic behavior, show don’t tell technique, few shot examples, rubric first technique, grading criteria, output quality, scalable AI systems,

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Ep 847: How to Train Your Team on AI: The 7 Steps to Educate Your Organization on LLMs (Start Here Series Vol 6) Aug 24, 2026

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

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 documentation, unstructured data, domain-specific AI training, AI upskilling, AI reskilling, hands-on AI practice, AI hackathon, change management, cultural adoption, AI leadership, CEO AI usage, champion teams, department AI training, AI ROI, measurable AI outputs, AI orchestration, operator to orchestrator, expert-driven workflows, AI agent, AI mindset shift, AI native workplace, AI best practices, AI adoption gaps, enterprise AI maturity

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Ep 846: AI Hallucinations: What they are, why they happen, and the right way to reduce the risk (Start Here Series Vol 5) Aug 21, 2026

Show notes

Let's talk about the AI elephant in the room: hallucinations. 🐘
Maybe hallucinations are the reason your company has been hesitant on AI.
But here's the thing, y'all. If you know what you're doing, hallucinations are largely manageable.
But first, you gotta understand what they are, how they happen, and how to reduce the risk.
Let's get started cutting down hallucinations together.
AI Hallucinations: What they are, why they happen, and the right way to reduce the risk (Start Here Series Vol 5) -- An Everyday AI Chat with Jordan Wilson

AI Hallucinations Definition and Causes
Large Language Models' Hallucination Mechanisms
Hallucination Types: Fabricated Claims & Sources
Model Improvements Reducing Hallucination Rate
Context Window Impact on AI Accuracy
AI Hallucinations in Legal and Enterprise Settings
Four-Layer Method for Minimizing Hallucinations
Custom Instructions and Retrieval-Augmented Generation
Expert-Driven Verification and Agent Safety Practices

Timestamps:

00:00 "Modulate's Velma: Smarter AI Insights"

03:18 "Reducing AI Hallucinations Explained"

08:36 "Minimizing AI Hallucinations with Skill"

12:30 "Model Retention and Recall Decline"

13:23 AI Advances: Improved Accuracy and Recall

19:24 "AI Hallucinations and Their Causes"

21:07 "Customizing AI Behavior Effectively"

24:47 "Connecting Data to Reduce Hallucinations"

28:47 "AI Oversight and Expert Input"

30:56 "Reducing AI Hallucinations Simplified"

Keywords:

AI hallucinations, large language models, next token prediction, AI error, human error, fabricated claims, reinforcement learning with human feedback, context window, context engineering,

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Ep 845: Human-AI Collaboration: Best practices for working alongside AI (Start Here Series Vol 4) Aug 20, 2026

Show notes

Spending more time fixing your AI outputs then you're saving?
You're not alone.
The trap?
You're in operator mode. Falling for the industry status quo like upskilling and human-in-the-loop.
The real winners in the AI race?
Companies that have changed the human-AI relationship.
How?
Join us for Volume 4 of our Start Here Series as we uncover what you need to know.
Human-AI Collaboration: Best practices for working alongside AI -- An Everyday AI Chat with Jordan Wilson

Human-AI Collaboration Best Practices 2026
Shift from Operator to Orchestrator Roles
Human-in-the-Loop Limitations Explained
Expert-Driven AI Review Loops vs. Generic Oversight
Orchestrating AI Agents for Business Productivity
Building Reusable AI Context and Skills
Elevating AI Champions on Team
Human Strengths vs. AI Strengths in Workflows
Avoiding Augmentation Debt and Workflow Pitfalls
Mindset Shifts for Effective AI Management

Timestamps:

00:00 "Everyday AI: Start Here"

03:23 "AI Shift: Operator to Orchestrator"

06:35 "Unlearn to Harness AI"

11:15 "AI Surpassing Human Collaboration"

15:11 Expert-Driven AI Process Loops

18:10 "Expert Collaboration Boosts AI ROI"

23:59 "Outsmarting AI Through Expertise"

26:30 "Navigating AI Success Strategies"

31:19 "Embrace AI, Elevate Your Team"

32:18 "Embrace AI, Elevate Humanity"

Keywords:

Human-AI collaboration, AI best practices, working alongside AI, human-AI relationship, AI orchestration, AI orchestrator, shift from operator to orchestrator, agentic workflows, AI agents, digital agents, expert-driven loops, expert oversight, senior partners with AI, context engineering, AI processes, context vaults, AI skills files, company data, chain of thought review, large language models, AI-powered workflows, AI expertise, AI in business, AI productivity, AI risk management, human in the loop, upskilling, reskilling, unlearning, AI mindset shift, augmented intelligence, multi-agent systems, AI automation, organization AI strategy, context quality, AI champion, domain experts, AI team integration, competitive advantage with AI, process redesign for AI, AI-powered decision making, accountability in AI, empathy in AI, ambiguous decision-making, novel judgment.

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Ep 844: AI as an Operating System: LLMs Are the Internet Now (Start Here Series Vol 3) Aug 19, 2026

Show notes

One of the biggest mistakes in AI?
Thinking that your company's AI use is noteworthy. Or, even a competitive advantage.
It's not.
We break it down in Volume 3 of our 'Start Here Series.'
AI as an Operating System: LLMs Are the Internet Now -- An Everyday AI Chat with Jordan Wilson

AI As An Operating System Explained
Large Language Models Replace Traditional Apps
AI Integration in Knowledge Work Platforms
Choosing the Right AI Operating System
Microsoft Copilot vs. Google Gemini vs. Claude vs. ChatGPT
Agentic Browsers Powering Autonomous Workflows
Model Context Protocol (MCP) for AI Agents
Orchestration Layer and Agent Collaboration
ChatGPT Apps Merging AI and Internet
Enterprise Data Integration with AI Tools
Context Switching Reduction Through AI Agents
Strategic AI Adoption and Platform Redundancy

Timestamps:

00:00 "AI: A New Operating System"

03:58 "AI Transforming Work Interfaces"

06:41 "Collaborating in AI-Native Workspaces"

12:25 Anthropic's Innovations in AI Tools

13:46 "OpenAI's Strategy and Market Focus"

18:02 "Cognitive Evolution Through AI"

20:57 "Agentic Browsers: Key 2025 Advancement"

25:12 Improving Content Through Data Insights

26:42 "Anthropic's MCP: The AI Connector"

32:19 "AI Tools for Productivity Integration"

34:20 "AI: Unlocking Context and Efficiency"

36:32 AI Governance and System Portability

39:35 "AI Operating System Insights"

Keywords:

AI operating system, large language models, LLMs, AI as infrastructure, enterprise AI, AI adoption, agentic workflows, AI agents, orchestration layer, Copilot, Microsoft 365 Copilot, Google Gemini, Gemini business, Gemini enterprise, Anthropic Claude, Claude cowork, MCP, model context protocol, OpenAI, ChatGPT, ChatGPT apps, ChatGPT business, ChatGPT enterprise, AI native, dynamic data integration, productivity with AI, collaboration tools, agentic browsers, autonomous AI agents, context window, memory and personalization, expert-driven loops, app hop tax, context switching, AI integration in business, AI tools for teams, AI platform selection, data governance, modular AI workflows, permissions and audit logs, backup and redundancy in AI, competitive advantage with AI,

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