Everyday AI Podcast – An AI and ChatGPT Podcast

The most expensive AI mistake of 2026 won't show up on any invoice. 💸

It'll show up two years from now when you can't get your data out, your competitors are eating your lunch, or your team is stuck maintaining software no one actually wanted to build.

Because in 2026, AI isn't one decision anymore.

It's four.

The model. The workflows. Your data. Your business software.

Each layer has its own build, buy, partner, or wait choice.

And most companies are making all four without realizing it.

Today on Everyday AI, we're breaking down the framework that puts those choices back in your hands.

Build, Buy, Partner, or Wait: The 4-Layer AI Stack Decision Framework for 2026 (Start Here Series, Vol 25) An Everyday AI Chat with Jordan Wilson

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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:
Build vs. Buy vs. Partner vs. Wait in AI
Four-Layer AI Stack Decision Framework
Evolution of AI Agentic Workflows in 2026
Buy vs. Build Decision Obsolescence
When to Build Proprietary AI Solutions
Prepackaged AI Workflows for Small Businesses
Data Ownership and Integration Strategies
Vendor Lock-In and Technical Debt Risks
Partnering in Regulated or Critical Workflows
Waiting for Stable AI Categories
Three-Week AI Adoption Blueprint
Capability Gap and ROI in AI Investments

Timestamps:

00:00 Buy vs. build AI question
04:19 Start here podcast series intro
09:52 AI companies offering consulting services
11:27 AI skills and vertical integration
14:19 Evaluating AI adoption strategies
18:53 Building proprietary processes
20:28 Streamlining organizational workflows
23:48 Importance of strategic partnerships
27:34 Deciding on software investments
32:26 Evaluating tech capabilities and gaps
35:59 Implementing AI Workflows Step-by-Step
38:09 Accessing the start here series

Keywords: 
build vs buy AI, build or buy AI, build, buy, partner or wait, AI stack decision framework, four layer AI stack, AI implementation strategy, AI decision making, technical debt, vendor lock-in, agentic AI, AI agents, AI workflows, enterprise AI adoption, prepackaged agentic workflows, Microsoft Copilot, Google Gemini, OpenAI, Anthropic, domain assistants, specialized agents, model context protocol, large language models, custom AI solutions, proprietary data, workflow automation, data integration, business software AI integration, regulated workflows, audit heavy workflows, AI-powered business software, SAP autonomous enterprise, Codex, model portability, AI category stability, AI talent, agentic engineering, proprietary processes, competitive advantage, ownership map, workflow differentiation, capability gap, learning curve, risk management, OpenClaw, open source AI, modular AI skills, training gap, internal context, audit and score, governance, operational risk, partnership with AI vendors, regulated industries AI, SMB AI adoption, AI-driven business transformation, ROI, rate of innovation
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Until a few months ago, open source AI was kinda a hobby project. 

Now, it's tearing corporate boardrooms apart. 

Why? 

Over the past 6ish months, the gap between frontier closed AI and open sourced AI has shrunk to pretty much nothing. And with the surge of always on agents driving open models, their development and release schedule is on pace with the frontier labs. 

So if your team isn't paying attention to -- and running test cases through -- open AI models, there's a good chance you'll either be overpaying or playing catch up soon. 

We walk you through the 101 and what you need to know when it comes to open source AI in this Start Here Series special. 

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More on this Episode: Episode Page
Today's Episode 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:
Open Source AI vs Closed Models Shift
Chinese Model Distillation & Legal Impacts
Enterprise AI Cost Triage Strategies
Google Gemma 4 Local Model Capabilities
Frontier Model Performance Gap Closing
24/7 Agentic AI Systems Overview
API Pricing War: DeepSeek vs US Vendors
Legal Protection Tradeoffs for Open Source AI
AI Workflow Triage: Task-Specific Models
Future Trends: Local and Specialized LLMs

Timestamps:

00:00 Introducing the Firefly AI assistant
03:33 Open source AI cost benefits
09:25 AI model performance differences
10:19 Open source model improvements
15:28 Advancements in local AI capabilities
17:04 Impact of Google's Gemma four
22:15 Introducing Adobe's Firefly AI Assistant
24:19 Adobe Firefly AI assistant beta launch
29:26 Choosing the right AI tools
32:00 Shifting workloads to open source
33:31 Using open-source and closed models
36:47 The future of open models

Keywords: 
open source AI, open source models, local AI models, local models, closed source AI, closed models, proprietary AI, proprietary models, AI agents, agentic AI, AI workflow triage, cheap API, AI API costs, model distillation, Chinese open source models, China AI models, US AI models,
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Salesforce's cofounder essential questioned: why should you login to Salesforce anymore? 🤔

He wasn't signaling the AI-driven SaaSpocalypse was picking up steam. 

Instead: he's talking about going headless. 

What's that? It's a future where Salesforce -- any potentially many other household software giants -- stop making software interfaces for humans and start designing for AI agents instead. 

So will this be a short-lived trend? Or, will the future of work not really involve a ton of humans clicking around? 

Join us as we dissect the latest. 

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More on this Episode: Episode Page
Today's Episode 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:
Headless Software Definition and Evolution
AI Agents Versus Traditional Software Interfaces
Salesforce Headless 360 and MCP Protocols
OpenAI Workspace Agents Features and Impact
Google Vertex AI Rebranding to Gemini Agents
Model Context Protocol (MCP) and A2A Integration
Per Seat Software Pricing Disruption
Enterprise Procurement for Agent-Ready Software
Agentic Commerce and Automated Bot Traffic Trends
Strategies for Auditing and Migrating Vendors

Timestamps:

00:00 Shift to AI-first software development
05:39 Benefits of headless software
07:29 Headless software development insights
11:28 Salesforce launches headless 360 platform
14:15 The rise of headless software
19:10 AI model connectivity in 2026
23:24 AI's impact on software pricing
26:22 Discussing token maxing in business
28:25 AI agents impacting human commerce
32:16 Evaluating software and vendor choices
35:46 Competitive advantage in software pricing

Keywords: 
headless software, interface-less software, headless software trend, software for AI agents, agent-first platforms,
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Info hunting and juggling sound familiar? 

It’s the downfall of almost any business leader. Where is that email from Emily? Why can’t I find last quarter’s budget in Drive? Oh, and Keenen needs an answer back on that research project. Oh shoot, I swear Caleb confirmed the expenses in one of these Slack channels. 

You’re off an information rabbit hole and by the time you find that Slack message, you already forgot what Emily’s email said. 

Hit home? Well, as AI models expand to Coworking and Scheduled agents, we have a new best friend that doesn’t really have a name. 

(Until we randomly named it. Lolz) 

Scheduled Agentic Context Carry. You need to know what it is, why it’s important, and how to use it. 

We’ll dive in. 

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More on this Episode: Episode Page
Today's Episode 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:
Scheduled Agentic Context Carry (SACC) Explained
AI Agents: Features vs. Benefits Paradigm
Co-Working and Scheduled AI Workflow Shift
Persistent Context and Memory in AI Agents
Large Language Models’ 1,000,000 Token Context Windows
Workflow Automation: Eliminating Human-AI Duct Tape
Multi-App Integration and Cross-Platform Context
Three Steps to Deploy Scheduled Agentic Context Carry
Chain of Thought Iteration with Scheduled Agents
Autonomous Agent Limitations and Future Bridge

Timestamps:

00:00 Explaining SACC and AI benefits
03:43 Introducing the Start Here series
06:26 Rise of AI in enterprises
11:55 AI agents learning industry trends
15:08 Agent capabilities in AI systems
16:47 Explaining complex trends simply
20:13 Streamlining tasks with AI agents
24:18 Understanding AI and context windows
27:43 Understanding prompt engineering basics
30:51 Debugging and reviewing schedules
33:08 Building automated workflows this quarter
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You think proper AI implementation is a technical problem for your company to solve? 🤔

Wrong. 

It’s actually about people. 

Maybe it’s because of the fast-pace nature of AI, and the fact there’s literally dozens of new AI tech drops each week that promise to change how we work, but the defecto response to showing ROI on AI always defaults to the technical side. 
Yet, studies show building an AI-native organization is WAY more about change management than anything else. 

We break it down, and show you the 5 moves the Top 5% are making to get it right. 

AI Change Management That Works: 5 Moves The Top 5% Make — An Everyday AI Chat with Jordan Wilson

Newsletter: Sign up for our free daily newsletter
More on this Episode: Episode Page
Today's Episode 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 Change Management vs. Technical Problem
AI Adoption’s People and Process Gap
Top 5% Change Management Playbook Steps
Budget Split: Funding People Over Tools
Dangers of AI Upskilling and Reskilling
Rebuilding AI-Native SOPs from Scratch
Weekly AI Enablement Rituals for Teams
Grading AI Behavioral Change, Not Tool Use
Enterprise ROI Gap in AI Adoption
Case Studies: Moderna, BBVA, JPMorgan AI Transformation

Timestamps:

00:00 AI adoption and change management challenges
04:39 AI change management challenges
08:57 AI's impact on workplace dynamics
12:51 Prioritizing Team Discussions and Processes
15:11 Investing in AI vs. People
18:25 Building AI-native processes
20:59 Embracing AI in daily tasks
24:05 Weekly AI strategy meetings
28:13 AI performance in big tech reviews
31:11 Adapting to AI in the workplace
34:21 Joining the Start Here series

Keywords: 
AI change management, AI adoption, change management strategies, AI transformation, enterprise AI ROI, 
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