Everyday AI Podcast – An AI and ChatGPT Podcast

You're polluting the world with AI Workslop and you don't even know it. 🗑️

In a world were everything is free and fake -- or, AI -- it's easy to just throw unlimited spaghetti at the wall and see what sticks. 

But there's a downside in just blindly rubber stamping those generic outputs from LLMs. And it's worse than the workslop epidemic. It's losing trust. 

So, how can your company survive and thrive in an AI world where everything is fake? 

Tune in and find out. 

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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 Trust Crisis and Consumer Skepticism
Deepfakes, Fraud, and AI-generated Content
Workslop: Rise of Generic AI Outputs
Human Expertise vs. Fully Automated AI
AI Content Detection and Liar’s Dividend
Elevating Human Oversight in AI Workflows
Expert-Driven Loops vs. Human-in-the-Loop
Auditing Business Outputs for AI Workslop
Domain Expertise in AI Context Engineering
Roadmap to Fight AI Workslop with Humans

Timestamps:

00:00 "Navigating AI-Driven Distrust"
04:01 AI, Jobs, and Fake Realities
06:35 "AI vs Expert Content Quality"
10:09 AI-Driven Online Interaction Surge
14:41 "Trust Fading in Imperfect Brands"
16:31 "AI Literacy: Bridging the Gap"
19:18 "Elevating Expertise in AI Workflows"
22:58 "Context Engineering for Domain Expertise"
28:11 AI's Impact on Blog Quality
29:21 "Fighting AI Work Slop"
32:40 "Everyday AI: Join & Explore"

Keywords: 
AI-generated content, everything is fake, AI workslop, work slop, AI slop, trust crisis, deepfakes, synthetic media, fake landing pages, fake customer service, AI-enabled fraud, voice cloning, agentic AI, discourse bots, domain expertise, human expertise, context engineering, content detection,
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Pop quiz: If your company's agents go off the rails, can you confidently name the human at fault in 10 seconds? 
Prolly not. 
Because lately, companies have spent more energy on going fast and breaking things instead of governance. 
Ready for the ironic part? People think AI governance slows you down. But, it actually speeds you up. 
Join us as we give you the 101 on AI Governance and the 5 AI Rules Every Company Needs to Follow. 
AI Governance in Plain English: 5 AI Rules Every Company Needs to follow -- 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:
AI Governance Explained in Plain English
Why AI Governance Matters for Companies
Deloitte AI Governance Statistics Breakdown
Challenges in Governing Rapidly Evolving AI
Real-World Lawsuits from Ungoverned AI
U.S. vs. EU AI Governance Laws Overview
Five Essential AI Governance Rules
Shadow AI and AI Tool Inventory Risks
Classifying AI Tools by Risk Level
Assigning Clear Ownership of AI Systems
Writing Playbooks Versus Policy Documents
Governance as an AI Scaling Engine
Importance of Monthly AI Governance Reviews

Timestamps:

00:00 "AI Governance Gap Insights"
05:56 "AI Governance Challenges Today"
09:03 "AI Governance Matters More Now"
11:57 AI Governance Showdown Looms
16:23 Optimizing AI Use in Organizations
19:21 "Classify and Assess Risk Levels"
21:32 "Clear Accountability for Crashes"
25:14 "AI Use Responsibility Retained"
29:42 "AI Governance: Stay Adaptive Daily"
31:09 "AI Adoption Made Easy"

Keywords: 
AI governance, AI rules, artificial intelligence governance, AI risk management, operational AI governance, agentic AI, autonomous AI, AI use cases, AI adoption, AI inventory, shadow AI, shadow AI breaches, AI sprawl, AI tools, AI transformation, AI policy, AI playbooks, risk classification, AI risk tiers, unacceptable risk, high risk AI, minimal risk AI, limited risk AI, human review in AI, AI agent crash, clear ownership in AI, cross functional committee, AI responsibility, domain expertise, AI user, data breaches, ROI on AI, AI impact tracking, AI training, scaling AI governance
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40,000+ AI-linked job cuts in two weeks. 😬

But AI is also supposed to create millions of new jobs.

So are we watching traditional work slowly die while the replacements don't exist yet?

And did we really spend decades building expertise just to babysit AI agents that are smarter and faster than us?

Join us live to find out.

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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-Driven Job Cuts and Layoffs Analysis
The AI Labor Shift: Timeline Predictions
Big Tech AI Infrastructure Investments
AI as Layoff Scapegoat and "AI Washing"
Capability Gap: AI Use vs. Potential
Roles Most at Risk in AI Labor Shift
Codified vs. Tacit Knowledge Job Impact
Three-Step Survival Guide for AI Careers
Domain Expertise Plus AI Skills Premium
New AI Job Roles and Emerging Opportunities

Timestamps:

00:00 "AI Jobs, Investments, and Displacement"
06:18 AI's Impact on Jobs
08:20 "AI Layoffs and Overhiring"
11:36 Driving AI Value with Section
16:00 AI's Workforce Impact Gap
18:25 "AI Impact on Work Dynamics"
20:25 AI-Driven Workflows Mainstream by 2030
24:25 "AI Impact on Job Security"
27:47 "Emerging AI Roles and Expertise"
31:17 "Surviving the AI Labor Shift"
33:38 "Boost AI Adoption with Section"

Keywords: 
AI labor shift, AI job displacement, AI and employment, AI job loss, AI job creation, traditional employment decline, future of work, AI workforce transformation, AI layoffs, tech layoffs,
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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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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 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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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

Newsletter: Sign up for our free daily newsletter
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:
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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