Everyday AI Podcast – An AI and ChatGPT Podcast

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

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More on this Episode: Episode Page
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Upcoming Episodes: Check out the upcoming Everyday AI Livestream lineup
Website: YourEverydayAI.com
Email The Show: info@youreverydayai.com
Connect with Jordan on LinkedIn

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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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

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More on this Episode: Episode Page
Join the discussion on LinkedIn: Thoughts on this? Join the convo on LinkedIn and connect with other AI leaders.

Upcoming Episodes: Check out the upcoming Everyday AI Livestream lineup
Website: YourEverydayAI.com
Email The Show: info@youreverydayai.com
Connect with Jordan on LinkedIn

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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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)

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More on this Episode: Episode Page
Join the discussion on LinkedIn: Thoughts on this? Join the convo on LinkedIn and connect with other AI leaders.

Upcoming Episodes: Check out the upcoming Everyday AI Livestream lineup
Website: YourEverydayAI.com
Email The Show: info@youreverydayai.com
Connect with Jordan on LinkedIn

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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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)

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More on this Episode: Episode Page
Join the discussion on LinkedIn: Thoughts on this? Join the convo on LinkedIn and connect with other AI leaders.

Upcoming Episodes: Check out the upcoming Everyday AI Livestream lineup
Website: YourEverydayAI.com
Email The Show: info@youreverydayai.com
Connect with Jordan on LinkedIn

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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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

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More on this Episode: Episode Page
Join the discussion on LinkedIn: Thoughts on this? Join the convo on LinkedIn and connect with other AI leaders.

Upcoming Episodes: Check out the upcoming Everyday AI Livestream lineup
Website: YourEverydayAI.com
Email The Show: info@youreverydayai.com
Connect with Jordan on LinkedIn

Topics Covered in This Episode:
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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