The Wild West Inside Your Company 

Wade Wyant on August 1, 2026

The EU just gave companies an extra sixteen months to comply with its AI accountability law. If your reaction to that sentence is relief, keep reading. 

Article 86 of the EU Artificial Intelligence Act was originally set to apply on August 2, 2026. Six weeks ago, the EU formally pushed that deadline back to December 2, 2027, through a package called the Digital Omnibus on AI. The delay does not mean the law went away. It means the clock restarted with a longer fuse. 

“The law did not go away. The clock just restarted with a longer fuse.” 

 

What the Law Actually Requires 

Article 86 gives any person affected by an AI-assisted decision the legal right to demand a clear explanation of how the AI was involved in that decision and what the main factors were. The company deploying the AI has to produce that explanation. Not the vendor. Not the platform. The business that used the tool.1 

This applies to decisions that produce legal effects or significantly adverse consequences. The EU is not asking you to explain why your AI suggested a new vendor or helped you set a price. It is asking you to explain decisions that affect people directly: hiring and firing, credit scoring, insurance eligibility, healthcare, education, and access to essential services. The EU groups these use cases under Annex III, which is the law’s official register of high-risk AI applications. Think of it as the EU’s list of the situations where AI errors can do the most damage to a real person’s life. Government security and certain law enforcement functions carry exemptions. Everything else on that list does not. 

Here is something I want to give the EU credit for: I think they found the right scope. Most regulation either misses the real problem or overcorrects into something unworkable. Article 86 is targeted. The EU looked at where AI errors cause the most human damage, drew the line there, and left business strategy decisions alone. That is a balance I respect, even as an American who generally thinks European regulators overreach. 

“The EU focused on where AI errors cause the most human damage. That is the right place to draw the line.” 

 

The Audit Trail Nobody Built 

Here is the uncomfortable question the law is really asking: if you had to produce that explanation today, could you? 

Think about what the average leader does with AI. They open a browser tab. They paste something in. They get a response. They close the tab. The conversation is gone. There is no record of what was asked, what was said, what version of the model was running, or what data it drew from. The decision gets made. The trail evaporates. 

Now think about email. Email has been through the legal and regulatory wringer for decades. Every corporate attorney knows exactly how to handle a subpoena for email. Companies have retention policies, legal hold procedures, discovery tools, and accepted industry standards. Whether a company retains email for one year or seven, there are frameworks, there is precedent, and there are answers. If you are subpoenaed for email, you know what to hand over. 

Your AI has none of that. If you were subpoenaed for your employees’ AI conversations today, how would you produce them? If those conversations happened on personal free accounts, you cannot produce them at all. And even if you had enterprise accounts that automatically save conversation history, you still could not answer the questions a subpoena would require: At what point did the AI’s output shape the outcome, and at what point did the human’s own judgment take over? What weight did the AI carry in the final call? Can you show the original prompt alongside the response, in sequence, in full context? Most companies cannot answer any of those questions. 

“If you were subpoenaed for your employees’ AI conversations today, what would you hand over?” 

 

The United States Is Not Waiting 

The EU delay does not mean American companies are off the hook at home. 

Forty-five states have introduced AI-related legislation in 2026 alone, building on 145 bills enacted into law in 2025.2 

Colorado passed the first comprehensive state AI law in the United States, requiring documentation of AI decision-making processes and transparency disclosures for high-risk systems. California has multiple active AI statutes covering employment and transparency. New York City already requires annual independent bias audits for any automated tool used in hiring decisions. Illinois mandates written notice and consent before AI can analyze a video job interview. 

There is no comprehensive federal law yet. That is not a safe harbor. It is a patchwork of state requirements that vary by geography and industry, change every quarter, and are impossible to track without a governance layer already in place. The federal question is when, not if. I have never seen Washington ignore something this consequential at the state level for long. 

 

The Mistake Companies Keep Making 

When AI governance comes up, most companies do one of two things. They hand it to IT, or they table it. 

Handing it to IT is the wrong move, and here is the analogy I use: if a problem is in the brain, you do not go straight to the neurosurgeon. Sometimes what you need is a therapist. AI governance is a leadership strategy problem with technical components. IT departments are stretched, and they were never built for this kind of work. Executive ownership is not optional. 

Tabling it is worse. The leaders treating this as a future problem are the ones who will be scrambling when the December 2027 deadline arrives, or when the first subpoena does, with no documentation to show. 

 

Three Things. That Is It. 

An AI strategy you maintain. Not one you write once and forget. AI strategy is not evergreen. You have to return to it, update it, and align it to the regulatory environment as it changes. The EU delay gave you more time. It did not give you permission to stop. 

AI enablement for your people. Your team is using AI right now with no guidance on what documentation matters or what tools are approved for which purposes. That gap between what you intend and what is actually happening is where your legal exposure lives. 

A governance layer that creates an audit trail. You need to know what AI tools are operating inside your company, what they were asked, and what they produced. Right now, most companies have none of this. 

Private AI, built and governed correctly, does all three. REDEGADES.AI is a Decision Alignment Layer. It tracks what goes in and what comes out. It connects AI output to actual decisions and builds the audit trail that the US state laws already on the books demand, and that federal legislation will eventually require. The companies that build with governance baked in will be ready when that moment arrives. Everyone else will be improvising. 

“AI strategy is not evergreen. You have to return to it. Water it. Feed it. Or it dies.” 

You are not subject to EU law. But you are watching an early version of what is likely headed here. Every state that has moved on AI accountability is reading the same playbook the EU wrote. Build the governance layer now, while it is a choice. Wait, and you will build it under a deadline someone else set. 

 

1. EU Artificial Intelligence Act. “Article 86: Right to Explanation of Individual Decision-Making.” Applicability date: December 2, 2027 (as amended by the Digital Omnibus on AI, formally adopted June 29, 2026). https://artificialintelligenceact.eu/article/86/. 

2. Multistate.ai. “State AI Legislation Tracker 2026.” https://www.multistate.ai/artificial-intelligence-ai-legislation. See also: Baker Botts. “U.S. Artificial Intelligence Law Update: Navigating the Evolving State and Federal Regulatory Landscape.” January 2026. https://www.bakerbotts.com/thought-leadership/publications/2026/january/us-ai-law-update. 

3. Peter Douglas. “Understanding Right to Explanation and Automated Decision-Making in Europe’s GDPR and AI Act.” TechPolicy.Press. September 19, 2025. https://www.techpolicy.press/understanding-right-to-explanation-and-automated-decisionmaking-in-europes-gdpr-and-ai-act/. 

4. EU Digital Omnibus on AI. Council of the EU final approval: June 29, 2026. European Parliament endorsement: June 16, 2026. See: Travers Smith. “EU agrees to delay key AI Act compliance deadlines.” https://www.traverssmith.com/knowledge/knowledge-container/eu-agrees-to-delay-key-ai-act-compliance-deadlines/. 

 

AI Didn’t Fail Ford. Ford Failed Their AI. 

Wade Wyant on July 2, 2026

The automaker just gave every business leader in America the most expensive lesson in AI strategy. Here is what you need to learn from it. 

Ford had a problem. Quality was suffering. Cars were going out the door with more issues than they should have had, and the company knew it. So leadership looked at AI and thought: there is the answer. We can automate quality. We can let the machines catch what the humans missed.1 

They were not entirely wrong. But they were wrong in one critical way. And it cost them. 

Before they could teach the AI to do the job, they let the people who knew how to do the job walk out the door. Ford shed roughly 5,300 salaried positions since its 2020 employment peak, part of a broader wave that eliminated more than 20,000 white-collar jobs across Detroit’s automakers. Among those who left: the experienced engineers who had spent decades understanding exactly why a specific design choice would cause a problem three years down the road.2 

Here is the part that should stop every CEO reading this cold. The AI did not fail. The AI amplified exactly what it was given. And what it was given was weak. Without the institutional knowledge of those veteran engineers encoded in the training data, Ford’s AI tools amplified bad inputs instead of catching design flaws. The system was working fine. The fuel going into the system was gone. 

“You cannot train a machine on knowledge that no longer exists.” 

WHAT FORD ACTUALLY DID WRONG 

Charles Poon, Ford’s VP of vehicle hardware engineering, said it directly: Ford mistakenly believed it could swap in AI and still produce a high-quality product. The company underestimated the value of veteran engineers and what they carried in their heads.3 

I want to give Ford credit for saying this out loud. That kind of honesty takes guts, especially from a company as proud as Ford. But let me be equally honest about what the lesson actually is, because the wrong takeaway here is dangerous. 

The wrong takeaway is: AI failed. Do not trust it. 

The right takeaway is: AI is only as good as the intelligence you put into it. If you try to replace your best people before you have captured what they know, you have not built an AI strategy. You have built an expensive way to scale your own ignorance. 

Think about it this way. You have a master technician who has built and fixed the same engine for 30 years. He can hear a problem before it shows up in the data. He knows from experience that a particular combination of tolerances under heat stress will cause a failure six months later, even if every spec on paper looks fine. Now you hand his tools to a machine. The machine can run the same checks. But it never learned why he made certain calls. The machine is perfectly precise and completely blind at the same time. 

That is what Ford built. And they had to spend months and hundreds of hires to fix it. 

WHAT FORD HAD TO DO TO TURN IT AROUND 

Ford’s response was not to abandon AI. The response was to go get the humans back and use them to fix the AI. 

The company rehired, newly hired, or promoted 350 experienced engineers. Those engineers were not brought back to do what they had always done. They were brought back for three specific purposes: to mentor junior staff, to rebuild the data pipelines feeding Ford’s AI training systems, and to refine the automated tools that were originally supposed to replace them. Ford also built a dedicated 40-person software quality assurance team and added more than 100,000 AI-powered automated tests to catch edge cases and revalidate software changes late in development.4 

The result: Ford topped J.D. Power’s 2026 Initial Quality Study for the first time in 16 years, scoring 152 problems per 100 vehicles, ahead of Nissan and Buick. The F-150, Mustang, and Super Duty each won best in segment for the second consecutive year. The J.D. Power Initial Quality Study measures problems reported by owners in the first 90 days of ownership.5 

Read that again. The fix was not less AI. The fix was human intelligence feeding better AI. That is a very different story than the headlines are telling. 

“The fix was not less AI. The fix was human intelligence feeding better AI.” 

WHY THIS MATTERS FOR YOUR BUSINESS RIGHT NOW 

Ford is a massive company with the resources to hire 350 people to fix a mistake. Most of the leaders I work with do not have that runway. Which means if you make this same mistake at your company, you may not get the chance to correct it. 

Here is what I see happen all the time in mid-market businesses. A leader hears about AI. They get excited. They start thinking about what they can automate, what headcount they can reduce, what costs they can cut. And the first thing they do is let go of the people who carry the institutional knowledge before they have figured out how to capture it. 

That knowledge does not live in your files. It does not live in your org chart. It lives in the heads of your best people: the ones who have seen what works, what fails, and why. The ones who can answer a question before you finish asking it because they have seen that exact situation twelve times before. 

When those people leave, that knowledge walks out with them. And no amount of AI investment will get it back, because there is nothing left to train on. 

WHAT WE DO DIFFERENTLY AT REDEGADES.AI 

At REDEGADES.AI, we have never believed AI replaces people. We have always believed AI surfaces the intelligence that helps your best people make better decisions, faster. 

The first thing we do when we work with a leadership team is capture what they know. The strategy. The judgment. The pattern recognition that lives in the CEO and the C-suite and has never been written down anywhere. We build a private AI environment that reflects your organization’s thinking, not some generic model trained on the whole internet. Your intelligence. Your data. Your people’s expertise, scaled across your company.6 

That is the opposite of what Ford did. Ford tried to skip to the output without building the foundation. We start with the foundation, because we know that is the only way the output is worth anything. 

Ford’s CEO Jim Farley said publicly that AI is going to replace literally half of all white-collar workers in the US. His own company’s quality crisis now complicates that prediction. Human judgment still wins. The right application of AI just makes it sharper.7 

“Human judgment still wins. AI just makes it sharper.” 

THE QUESTION YOU SHOULD BE ASKING 

If something happened to your top three or four people tomorrow, what would your company actually know? Not what would be in your files or your systems or your processes. What pattern recognition, what judgment, what decades of hard-won expertise would walk out the door with them? 

If the answer is most of it, you have the same problem Ford had. You just have not run into the consequences yet. 

The golden window for building a real AI strategy, one where your intelligence is captured before it walks out the door, is right now. Not when you are already in the quality crisis. Not when you are making 350 emergency hires to rebuild what you let go. Now. 

Ford figured it out. They earned the top quality ranking in 16 years as proof. The question is whether you learn from their mistake, or wait until you have to make it yourself. 

Notes 

1. Alina Maria Stan, “Ford had to rehire 350 engineers after its AI got vehicle quality wrong,” The Next Web, June 26, 2026, https://thenextweb.com/news/ford-rehired-350-engineers-ai-quality-jd-power. 

2. Stan, “Ford had to rehire 350 engineers.” Ford’s salaried workforce reduction since its 2020 employment peak and the broader 20,000-job contraction across Detroit’s automakers are both reported in this article. https://thenextweb.com/news/ford-rehired-350-engineers-ai-quality-jd-power. 

3. Stan, “Ford had to rehire 350 engineers.” Charles Poon’s statement that Ford “mistakenly believed it could swap in AI and still produce a high-quality product” is quoted directly in this article. https://thenextweb.com/news/ford-rehired-350-engineers-ai-quality-jd-power. 

4. Stan, “Ford had to rehire 350 engineers.” The 350-engineer figure, their three assigned roles, the 40-person software QA team, and the 100,000-plus automated tests are all reported in this article. https://thenextweb.com/news/ford-rehired-350-engineers-ai-quality-jd-power. 

5. Stan, “Ford had to rehire 350 engineers.” Ford’s J.D. Power 2026 Initial Quality Study results — 152 problems per 100 vehicles, first-place ranking among mainstream brands for the first time in 16 years, and three segment wins for the F-150, Mustang, and Super Duty — are reported in this article. The J.D. Power Initial Quality Study measures problems reported by owners in the first 90 days of ownership. https://thenextweb.com/news/ford-rehired-350-engineers-ai-quality-jd-power. 

6. REDEGADES.AI, company methodology. For information on REDEGADES.AI’s private AI implementation approach and the Decision Alignment Layer, visit https://www.redegades.ai. 

7. Stan, “Ford had to rehire 350 engineers.” Jim Farley’s statement that AI “is going to replace literally half of all white-collar workers in the US” is quoted in this article. https://thenextweb.com/news/ford-rehired-350-engineers-ai-quality-jd-power. 

Stop Protecting Your Data from the Wrong Enemy 

Wade Wyant on July 1, 2026

Your AI paranoia is costing you more than a data breach ever would. 

There is a conversation happening in boardrooms across the country, and it is costing businesses millions of dollars in lost productivity, missed decisions, and competitive ground surrendered. It goes like this: I am not sure we should put that into AI. What if someone gets it? 

I have spent my career in cybersecurity. Top-secret clearance. Decades running businesses built around keeping data safe. And I am telling you flat out: this particular fear is almost entirely unfounded. 

Here is what is actually going on, why the fear exists, why it is mostly wrong, and where you should actually be spending your worry. 

First, a Quick History Lesson: Security by Obscurity 

Early cybersecurity thinking leaned on a concept called security by obscurity. The idea was simple. If your data was hard enough to find and hard enough to piece together, attackers would move on. Be obscure enough and you were protected. 

It worked until it did not. Big data changed everything. Suddenly your size did not protect you. Every transaction, every login, every record had value to someone. We spent years telling companies that security by obscurity was dead. You cannot hide anymore. Everyone is a target. Everyone needs real protection. 

That was true. In the traditional cybersecurity world, it still is true. But something unexpected happened with the rise of LLMs, and almost nobody is talking about it. 

The Plot Twist: Security by Obscurity Is Back, and It Is Working 

In the context of large language models, your data is so obscure, so unremarkable, and so lost in the ocean of information flooding through these systems that the probability of it being extracted, identified, and weaponized against you is effectively zero. 

A few years ago, the early AI companies absolutely wanted your data. Training on real-world conversations and inputs was how these models got smarter. ChatGPT’s early days almost certainly included data from users who never intended to contribute to a training set. 

That was then. Today the volume of information running through these models is staggering. Your quarterly financials, your pricing conversations, your strategic planning notes are a drop in the Pacific Ocean. The model is not sitting there waiting to surface your pricing strategy to your top competitor. It is processing billions of inputs from around the world. You are not interesting enough for it to notice. 

“Your data is not as interesting as your fear says it is.” 

I Asked for Proof. Nobody Had Any. 

The rumors are everywhere. I heard about a company in my industry that fed their numbers into ChatGPT and now their competitors have them. I always respond the same way: show me the specific company. Give me the person’s name. Show me the documentation. 

Nobody ever can. It is always I heard. It is always a friend of a friend. It is always a vague story with no verifiable details. 

What I believe has happened is that we have collectively constructed a paranoia that has no real evidence beneath it. And that paranoia is causing smart CEOs, people I respect enormously, to hold their companies back from one of the most powerful business tools ever created. 

Yes, You. I Am Talking Directly to You. 

Whoever you are reading this right now, I want to be honest with you. The chances that your data is of material interest to anyone training an AI model are 99.9999% against. You are not a target. The LLMs have moved way beyond the point of wanting your client list or your go-to-market strategy. We are talking about intelligence operating at a scale most of us cannot fully comprehend. 

Does that mean there is zero risk? No. Nothing is zero risk. If your data is genuinely unique, genuinely proprietary, and genuinely valuable at a level most businesses cannot imagine, then maybe you have a conversation worth having. But for the overwhelming majority of companies asking this question, the answer is: relax. 

What to Do If You Still Want Extra Protection 

If you read all that and still want a stronger wall between your data and the AI systems you use, there are real, cost-effective solutions. Private AI configurations can obscure your data before it ever reaches an LLM, so the model processes your queries without ever knowing who is behind them. Full private deployments keep everything inside your walls entirely. The Defense Department uses versions of this. Certain governments and major banks do too. 

REDEGADES.AI offers private AI solutions at a fraction of what you might expect. If peace of mind has a dollar value in your organization, this is worth exploring. Just be clear about why you want it. Not because an LLM is going to steal your strategy because it almost certainly will not. But because executive confidence, board expectations, or enterprise compliance requirements might make it the right call. 

The Real Threat Nobody in This Conversation Mentions 

Here is where I need you to lean in. 

Almost any company’s data is available right now through corporate espionage. Not through sophisticated hacking. Not through AI. Through old-fashioned human intelligence gathering that requires almost no illegal activity, carries minimal legal risk, and is happening at a frequency that would make most CEOs lose sleep if they actually looked at it. 

Corporate espionage is the threat no one wants to deal with because there is no clean software fix. It requires human vigilance, organizational discipline, and ongoing attention. It is harder than signing an enterprise AI agreement and checking a compliance box. And so most companies ignore it entirely. 

“Your biggest data risk is not in the cloud. It is probably walking past your building right now.” 

So What Should You Actually Do? 

Stop letting AI paranoia slow you down. The leaders who are winning right now are the ones asking better questions, moving faster, and getting more out of every working hour because they stopped being afraid of the tool and started mastering it. 

If you want private AI, get it. REDEGADES.AI makes it easy and affordable. If you want to protect your company’s real competitive data, start taking corporate espionage seriously. And if you are still telling yourself that feeding a question into ChatGPT is going to hand your strategy to a competitor, I respectfully ask you to re-examine that belief. 

You are not that interesting to the LLM. But your competitor thinks you are very interesting indeed. 

Bias: Build a Machine That Thinks Like You

Wade Wyant on June 30, 2026

Bias: Build a Machine That Thinks Like You 

Generic AI doesn’t know your leadership bias, but REDEGADES.AI’s bespoke and private AI is trained on your judgment, priorities, and strategy. 

Bias 

The term bias is bursting with negative connotations, but when we use the term bias, we are talking about something positive, your worldview. We are talking about the way your business operates. 

Each business has a unique way that it interacts with customers and makes itself distinctive. At REDEGADES.AI, we refer to the DNA and characteristics of a business plan as its bias. 

For example, what’s your company’s bias about employees who are underperforming in their current roles? 

a.   Retrain people rather than fire them. 

b.   Find a different role for these people in the organization. 

c.   Fire people, but give them a severance package. 

d.   Fire people without a severance package. 

Your company’s bias toward firing may not be the same as other businesses, but the impact of your bias will determine how your business makes decisions. 

One example of an easy bias is: What business methodology do you use? 

  • Scaling Up methods by Verne Harnish 
  • Jim Collins’ principles (e.g., Beyond Entrepreneurship 2.0, Flywheel) 
  • Great Game of Business by Jack Stack 
  • Entrepreneurial Operating System (EOS) 
  • Patrick Lencioni’s principles (e.g., The Five Dysfunctions of a Team, Death by Meeting) 

Some companies use one business methodology while others combine positive aspects from a few different ones. To provide the most advantages, your AI system needs to know which business methodology you use. 

For example, if you enter the book Beyond Entrepreneurship 2.0 into AI as a bias, then AI will make suggestions that align with the principles in this book. 

Inputting bias into AI will keep your business trending the way you want it to go, but what way do you want it to go? It’s important to take time to discover what aspects of the business are critically important to you. An outside company, such as REDEGADES.AI, can help you define how you want to manage your business and help you with the process of self-discovery. 

Once you have an idea of how your business should operate, you can train AI around that bias. However, you must use some moderation because you don’t want AI to be an exact copy of you. AI should be aligned with your thinking, but it should still challenge you and give you an appropriate level of counterbalance. 

As we developed this bidirectional communication plan or rhythm with AI, we noticed that we needed to introduce a bias and a mindset into AI. (When REDEGADES.AI refers to bias, we are not talking about bias against people. We are referring to a worldview where you insert your values and ideals as a company.) 

To insert the bias and mindset into AI, we asked ourselves, “What’s the history of the organization?” Many times, a company’s history will dictate where we go next. AI needs to know where a company has succeeded and failed, so it can refine its output to best meet the strengths and weaknesses of the company. When AI knows a company’s history, it becomes a much more compatible partner. 

What information does AI need to know about the structure of your business? 

  • Business history 
  • Core values 
  • Business culture 
  • Frameworks from business leaders, such as Jim Collins 
  • Company’s purpose 
  • Brand promise 
  • One-page plan 
  • BHAG (big, hairy, audacious goal) 
  • Flywheel 

Once AI understands the structure of your company, your company’s past strategies should be inputted into its system. However, this old information should be weighted so AI knows how much emphasis to put on it. Inputting old strategies is for historical context only. In the future, when AI makes recommendations and processes data, it will have this background information. However, the data should be weighted so AI will realize that this information does not have to be part of the current recommendation. 

AI must also know information about the leadership team, such as who has strong leadership skills and who doesn’t. Plus, it needs to know about the challenges team members have. That’s the bias we are building in. If you don’t add a bias, the answers from AI are too generic. 

Bias also involves industry trends and customer preferences. When AI has this data or bias, it can make better recommendations. AI must have this information about people’s preferences, strengths, and weaknesses because it is dealing with humans. It’s important to give AI the necessary data so it can work more effectively. To prove my point, if you think about the bulk of a CEO’s job, it’s dealing with humans and their problems. 

How Can AI Achieve Better Results for You and Understand Your Bias? 

  • Through reinforcement and corrective feedback 
  • Use of RAG, retrieval-augmented generation 
  • Inputting business documents into the RAG 
  • Updating and structuring information inputted into the RAG 

The Day You Build a Machine That Thinks Like You 

There’s a moment, and every CEO knows it, when you whisper to yourself, “If I could just clone myself…” We say it in the late-night hours when the inbox has mutated into a monster. We say it during those meetings where half the room speaks in circles while the other half stops speaking altogether. The whispered desire to clone yourself turns into a shout when the decisions pile up. Whether it’s strategic, financial, or people decisions, the company waits for us to think, decide, and move. 

For the longest time, that wish to clone ourselves was nothing more than a fantasy. A joke we made in the break room. A wistful sigh on the drive home. Now that AI has crossed the threshold, that desire can become a reality. 

How does this become a reality? AI becomes an amazing asset to your company when you build a machine that thinks more like YOU than any employee ever could, because it can ingest every doctrine, every preference, every bias, and every pattern that makes your leadership yours. How do you achieve an AI that thinks like you? Train it on the right things, weight the data correctly, and feed the right data in the right order. 

With this type of AI model, you’ll get a machine that doesn’t think generically or merely offer “best practices.” In other words, it won’t be a machine that thinks like Google Search with a necktie on. Instead, it will be a machine that thinks like you. 

The Myth of “Bigger is Better” 

When AI first hit mainstream consciousness, everyone chased the biggest models they could find. They wanted more parameters, tokens, and power. All of these are very important things, but a private AI system that thinks like you provides the biggest win. 

The best AI model doesn’t try to think like the universe. Instead, it tries to think like you, in your universe. 

Most people assume they have to use ChatGPT or Claude straight out of the box. They don’t realize customization is nearly limitless and far more powerful than people know. 

An AI model trained on the right doctrinal inputs becomes your most valuable technological asset. Why? It’s because alignment matters, especially when it’s aligned to your business. 

Doctrine: The Soul of Your AI 

All leaders have doctrine, even if they’ve never written it down. Some CEOs are “rip the Band-Aid off” people when it comes to performance problems. Others will nurture an underperformer for eighteen months, hoping potential finally becomes reality. Some think in EOS language or in Scaling Up language while others speak the “my gut never lies” language. Doctrine drives every meaningful leadership decision. 

Your doctrine (your real doctrine, not the one you claim) drives how you lead, how you make decisions, and how you run a company. 

If you don’t capture your doctrine, your AI will default to the doctrine of the internet, which is the doctrine of “everyone else.” Once again, you’ll be stuck with something that thinks for you instead of with you. 

Private AI models enable you to build doctrine directly into your AI. You can encode: 

  • how you hire and fire, 
  • how you handle risk, 
  • how you think about money, 
  • how you weight certain people’s opinions, 
  • how you challenge yourself, 
  • what your values are, and 
  • what you never compromise on. 

Encoding your doctrine into AI isn’t optional. Rather, your doctrine is the soul of your AI. This is how the machine starts thinking like you. 

Custom AI Learns Your Patterns 

Most leaders think of AI as a giant machine that pulls answers out of a magic cloud. Custom AI is an entirely different model focused on alignment with your business philosophy. Custom AI tries to understand you, and that’s why it’s powerful. What makes your business unique, your DNA, lives in the tiny corners of your decisions, not in the “standard operating procedures.” 

A private AI model learns your unique way of thinking, and it begins to understand: 

“Why did you pick that?” 

“Why do you always avoid this?” 

“Why do you trust that?” 

In other words, AI models can be trained to learn your bias. This is why REDEGADES.AI encourages leaders to begin implementing AI models with the leadership team. Instead of wishfully thinking, “If I could only clone myself,” why not do this now? Start with the C-suite. 

Your AI should be trained first and foremost on your way of thinking, before it ever touches the rest of the company. This is the essential first step. 

The Step Most Leaders Miss 

Here’s the thing most leaders don’t see coming. Once your leadership team each builds a great AI, you have a new problem. Every person in the room is now more confident than ever, armed by a machine that told them they were right, reasoning from different data. You walk into your most expensive meetings with a room full of better-armed arguments. Decisions don’t stick. Disagreements get sharper. And you, as CEO, can no longer tell a real strategic debate from two people fighting over whose AI had better inputs. 

Building AI that thinks like you is the foundation. It is not the finish line. The next step is making sure all of that individual intelligence connects into one shared brain. When every leader on your team is drawing from the same sandbox, the same context, the same company DNA, the false disagreements disappear. What’s left is the real conflict, the kind worth having. That’s what REDEGADES.AI calls the Decision Alignment Layer, and it’s where individual AI becomes organizational intelligence. 

You Just Bought a One-Way Ticket. Now What?  

Wade Wyant on June 27, 2026

AI has unlocked a gold rush of software development inside your business. Before you celebrate, answer four questions. 

THE DAM JUST BROKE 

For twenty years, your company did the smart thing. Every time a leader walked in with a problem that needed a software solution, the conversation went roughly the same way. How much will it cost? How long will it take? Who maintains it? The answers were always: more than you want to spend, longer than you want to wait, and somebody we probably have to hire. So you put the problem on the shelf and moved on. 

That shelf got very full. Marc Andreessen, co-founder of Andreessen Horowitz, has argued publicly that the world had far more demand for software than anyone ever realized. The reason we did not see it was simple: the friction of building software had trained everyone to stop asking. Leaders are smart people. They learned to skip the question before it became a headache.1 

Then AI showed up and changed the math completely. 

Today, over 26 percent of all production code is being written by AI, up from 22 percent just one quarter ago.2 What used to take a development team three months now takes an afternoon. What used to require a programmer now requires someone willing to describe a problem in plain English. The shelf is being cleared out at a pace nobody predicted. 

“We did not go from walking to driving. We went straight from walking to flying.” 

Two terms have emerged to describe this new reality. Low code refers to development platforms that reduce manual programming through visual tools and templates. Vibe coding, a term coined by AI researcher Andrej Karpathy in February 2025, takes it further: you describe what you want, and the AI builds it. You are not programming. You are directing. The technical barrier, the one that kept that shelf full for twenty years, is gone. 

THIS IS THE BEST NEWS IN BUSINESS. MOSTLY. 

At REDEGADES.AI, we watch this transformation play out inside our clients every week. Problems that sat unsolved for years are getting fixed in days. Workflows that required three humans and a spreadsheet are being automated. The productivity unlock is real, and we encourage every company we work with to move fast. Build aggressively. The companies that do are pulling ahead of the ones that are still waiting for a pricing page.3 

But here is the part of the story that does not get enough airtime, and I am going to give it some, because I think ignoring it is going to be expensive for a lot of companies in the next two years. 

Flying somewhere on a one-way ticket is great. Right up until you need to come back. 

A significant portion of the software being built right now with AI tools is exactly that: a one-way ticket. It solves the problem in front of you today. It does what you need it to do right now. And then it lands somewhere, and the question of what happens next has not been fully answered. According to a recent survey of technology leaders, 75 percent expect moderate to severe technical debt by 2026 as a direct result of rapid AI-assisted development.4 That debt is already accumulating. The question is whether you see it before it comes due. 

THE FOUR QUESTIONS YOUR IT TEAM IS HOPING YOU WON’T ASK 

Here is the honest version of where most companies are right now. They are building fast, deploying aggressively, and solving problems they could not touch two years ago. All of that is good. What is not good is that four critical questions are going unanswered. Most leaders do not ask them because they feel like IT questions. They are not. They are business continuity questions. 

The first question is: where does this software actually live? 

A tool built inside a chat interface, a link shared with the team, a deployment dropped into a temporary environment: none of those are infrastructure. They are convenient right now. Licensing terms change. Platforms evolve. The tool your sales team relies on every morning may not exist in the same form twelve months from now, and if nobody owns it, nobody will fix it when it breaks. 

The second question is: is it secure? 

AI can write functional code very quickly. What it does not automatically produce is hardened, production-grade code. Research from Veracode found that between 40 and 45 percent of AI-generated code contains vulnerabilities mapping to the OWASP Top 10, the industry’s standard list of critical security risks.5 AI-generated code has a 2.7 times higher vulnerability density than human-written code. And 58 percent of developers admit they trust AI output without testing it. That is not a developer problem. That is a leadership problem. 

The third question is: who maintains it? 

Software built to solve a problem today will eventually run into a change, whether that change comes from inside your business or from a shift in the underlying technology powering the build. When that happens, who fixes it? This is not rhetorical. It needs a real owner, a real answer, and a real plan, before the break happens, not after. 

The fourth question is: who troubleshoots it when something goes wrong? 

This still requires a human in the loop with enough context to know what they are looking at. If everyone who understood what the tool was supposed to do has moved on to the next project, that context is gone. Gone, as in, the tool sits broken while someone tries to re-explain its entire purpose to an AI, hoping to reconstruct something that should have been documented from the start. 

“75 percent of tech leaders expect moderate to severe technical debt by 2026. The clock is already running.” 

THE GOOD NEWS: THIS IS FIXABLE RIGHT NOW 

None of these questions are reasons to slow down. Let me be clear about that. The era of being able to build software fast, at low cost, without a team of developers, is one of the most significant competitive advantages in the history of business. The companies that lean into it are going to look very different from the ones that wait. I am not here to pump the brakes. 

I am here to say: build fast, and build with a plan. 

That means deploying every tool you build into a real environment you own or control. It means running a basic security review before anything touches your data or your customers. It means assigning an owner, not just a builder. And it means documenting what the thing is supposed to do while someone still knows. 

This does not require a large IT team. It does not require a budget overhaul. It requires a checklist and the discipline to use it. At REDEGADES.AI, we help companies build that infrastructure around their AI development so the velocity stays high and the debt does not pile up quietly in the background. 

THE RETURN FLIGHT IS PART OF THE TRIP 

I thought about Y2K when I was putting this together. We spent years worrying about what would happen when the calendar rolled over to the year 2000, and when it did, the lights stayed on. This could be a Y2K situation. Maybe AI tools improve fast enough that maintenance and troubleshooting become trivially easy and the four questions answer themselves. That is possible. 

But the cautionary part of a cautionary tale is not that the outcome is certain. It is that the outcome is avoidable. Build fast. Solve every problem on that shelf. Clear it out. Just make sure someone has thought about the return flight before the plane takes off. 

The shelf is empty. The plane is in the air. Book the return. 

____________________________________________________________ 

CITATIONS 

1  Marc Andreessen, The Joe Rogan Experience, Episode #2234, November 26, 2024, https://open.spotify.com/episode/2JDW5u8BlKHM5C5wInOT4u. See also: Marc Andreessen, “Why AI Will Save the World,” Andreessen Horowitz, June 6, 2023, https://a16z.com/ai-will-save-the-world/. 

2  “AI Coding Assistant Statistics & Trends [2025],” Second Talent, https://www.secondtalent.com/resources/ai-coding-assistant-statistics/. AI-authored code share figure as of mid-2025. 

3  REDEGADES.AI client observation. 

4  “Vibe Coding Hit 84% Adoption. 45% Has Vulnerabilities,” Pixelmojo, https://www.pixelmojo.io/blogs/vibe-coding-technical-debt-crisis-2026-2027. Technical debt projection figure drawn from survey of technology leaders, 2025-2026. 

5  Veracode, “AI-Generated Code Security Risks: What Developers Must Know,” https://www.veracode.com/blog/ai-generated-code-security-risks/. OWASP Top 10 vulnerability mapping figure and 2.7x vulnerability density comparison. Developer trust figure: Faros AI, “The AI Productivity Paradox Research Report,” https://www.faros.ai/blog/ai-software-engineering. 

From 2D to 3D: Custom AI for All, Not Just One-on-One  AI Usage 

Wade Wyant on June 27, 2026

Most leaders today are still living in a two-dimensional AI world. Leaders work with AI in a flat, transactional space, a simple exchange between a person and a tool. You ask a question; it gives an answer. Productivity rises, but perspective doesn’t.  That’s where the revolution begins.

The real power of AI isn’t in what it can do for you as an individual; it’s in what it can do for us as an organization. Moving from 2D to 3D means teaching AI to think like your company, not just like your best prompt engineer. The goal is to transform a single-user interaction into a collective intelligence system. This makes sure AI learns from every voice in your business, weights those inputs appropriately, and synthesizes them into decisions that move the company forward.

The Problem with 2D AI

The two-dimensional AI model is seductive because it’s easy, fast, impressive, and agrees with your input unless properly trained. You type a question into ChatGPT, and in seconds it gives you something useful. Perhaps it’s an email, a summary, or a list of ideas. This may give you a rush of endorphins and increase productivity, but it’s still a flat 2D model. As I tell CEOs, in the 2D world, AI reflects your bias back to you. It agrees with your assumptions. It becomes a mirror, not a multiplier. The real danger is that it can make you more efficient at being wrong.

In a 2D interaction, AI is a tool. Unless trained, AI has no context for your business, your customers, or your leadership DNA. In this instance, what AI doesn’t know can hurt you. What is AI missing just out of the box? This amazing tech doesn’t know which insights matter most, which biases are intentional, or which trade-offs define your culture. So,while it’s helpful for one person, it doesn’t scale across the organization.

Every department ends up building its own siloed use of AI. Marketing builds prompts for branding, and finance builds prompts for analysis. HR builds prompts for policy, and the fractured use of AI extends across the organization. Everyone’s “using AI,” but no one’s connected by it. Sadly, that doesn’t transform the company. Instead, these siloed uses of AI fragment it.

The 3D Shift: From Productivity to Perspective

When we talk about moving to 3D AI, we’re talking about turning individual productivity into organizational perspective. The leap from 2D to 3D AI is the leap from me to we.

In a 3D model, AI captures the wisdom, data, and bias of the entire leadership team, not just the loudest or most technical voices. It integrates the quiet insights, the front-line observations, and the executive strategy into a single system that understands the whole business. AI becomes what I call a living intelligence system.

This is where AI begins to “think with you,” not just “work for you.” At this point, AI can give you contextualized answers, not just generic ones, because it understands your cultur eand your language. The biggest perk is that AI understands your intent. When your leadership team asks AI questions, it responds as if the company itself were answering. That’s the moment AI becomes three-dimensional.

How We Got Here

When we built Redegades, we weren’t trying to create another AI company. We were trying to solve a leadership problem. I saw what was happening inside mid-sized organizations across the United States. People were excited about AI, but the excitement was scattered. Each leader was experimenting alone. Some had brilliant results while others were frustrated. The difference wasn’t their intelligence, but their structure.

So, we started with one premise: AI will only ever be as smart as the system it represents. If the system is flat, then AI will be flat. If the system is dimensional, capturing data, voices, and context, then AI will become dimensional. The solution involved a different perspective, not just more prompts.

We began working with CEOs to structure their organizational data: leadership meeting notes, team insights, key documents, customer patterns, and feedback loops. Once we organized that data into a structured, retrievable format using a custom RAG (retrieval-augmented generation) system, then AI began to behave differently.  AI wasn’t answering like ChatGPT anymore. Instead, AI was answering like the organization.

Custom AI: Thinking Like Your Company

Most people think “custom AI” means hiring coders to build a proprietary model. However, that’s not what we mean at Redegades. The model isn’t the secret sauce. We believe the real power is in the data. You don’t need to build a new brain. You just need to teach the existing one who you are.

Your company’s custom AI is trained on your data. AI ingests your policies, your processes, your playbooks, your transcripts, and your culture. At Redegades, we believe in designing AIto understand your bias, your strategy, and your vocabulary. That’s why I say, “ChatGPT is generic. Your company isn’t.” Generic AI gives you generic answers. Custom AI gives you leadership-aligned answers.

When an organization moves from 2D to 3D, it stops asking “What can AI do for us?” and starts asking “What can AI learn from us?”  That’s the inversion point. That’s the moment when AI becomes a multiplier of leadership instead of a mirror of convenience.

Capturing Every Voice

The heart of the 3D system is voice because intelligence is born from conversation. In every business, there are voices that dominate and voices that disappear. The CEO speaks loudly, but the strategist speaks clearly. A practical voice comes from the operations manager. Yet, the person who sees the customer every daily, often the one with the sharpest insights, stays quiet. AI gives you the chance to capture all these voices.  As I tell clients, the quiet voices in your company often hold the loudest truths.

From Meetings to Models

Every meeting your team has is filled with data that is waiting to become useful intelligence. Think about all the hours of conversation, insights, decisions, and emotional cues. In the 2D world, this is all lost the moment the meeting ends. However, in the 3D world, the valuable information is captured, transcribed, analyzed, and structured.

Your AI can summarize key points, identify recurring themes, track who contributes what, and connect decisions to outcomes. Over time, it builds a real-time leadership knowledge base, a digital model of how your company thinks, learns, and decides. That model becomes the foundation of your co-CEO system. AI becomes a living brain that grows with you.        From Flat Tools to Living Systems

In the 2D world, AI is an assistant. In the 3D world, AI is an advisor. A 2D assistant responds when spoken to. A 3D advisor observes, remembers, and anticipates. It connects dots you didn’t even know were related.

That’s why I say the shift from 2D to 3D isn’t about technology. The real shift is about leadership. It requires humility to admit that your perspective is only one dimension of the truth. It requires discipline to capture every other dimension around you. When leaders make that shift, their organizations transform. AI stops being an experiment and starts being a culture.

The Flywheel Effect

The most powerful outcome of 3D AI is momentum. Once your intelligence system is structured (data, feedback, and voice all connected), it begins to accelerate itself. Each interaction provides new data for training. Each correction improves future results, and each decision adds context. That’s the process for companies moving from using AI to becoming AI-driven.

As I often remind leaders, in the 3D world, AI isn’t a project. It’s a participant. Your co-CEO doesn’t clock out at 5 p.m. It keeps learning, adjusting, and building the flywheel. The organization begins to operate as one connected, thinking entity. Leadership, data, and AI all spin in sync.

Why It Matters Now

Because we’re in the first era where leadership itself is being digitized, the shift to 3D implementation of AI is necessary to gain the competitive edge. If you stay in 2D, you’ll soon find yourself competing with companies that think in 3D, and that’s not a fight you can win.

A 3D company learns faster, executes faster, and scales smarter. Instead of relying on memory, 3D companies rely on a connected and trained AI. With a 3D version, your company doesn’t debate assumptions; it uses AI to analyze evidence. Connecting AI and the company into a 3D model allows AI to keep working even when you’re not. AI doesn’t wait for meetings; it makes progress continuously. That’s what happens when you move from isolated intelligence to collective intelligence. You stop playing defense and start shaping the future.

The difference between 2D and 3D is a philosophy, not a feature. Two-dimensional AI is transactional, but 3D is transformational. In 2D AI, an individual works with AI alone, but in a 3D model, a collective group of people are giving and receiving feedback.  Two-dimensional AI gives you answers, but three-dimensional AI gives you awareness.

Most companies are still living in two dimensions, where everything is flat and efficient, but fragile. The future belongs to those willing to build the third dimension. In that third dimension lies the greatest competitive advantage of all: a company that truly thinks for itself.

Why AI Should Think Like You

Wade Wyant on June 27, 2026

Why AI Should Think Like You 

Most companies are building AI systems that are incredibly intelligent, but they remain strangely disconnected from how their leaders actually think. Executives today are experimenting with tools like ChatGPT, Copilot, or Gemini, hoping they will unlock faster decisions, sharper insights, and better strategy. Yet many of these systems feel generic. They produce good answers, but not your answers. They analyze data, but not through your lens. The result is AI that is powerful but oddly impersonal. With generic AI, your AI system is more like a consultant who just arrived than a trusted advisor who understands your business. 

The real breakthrough for leaders will not come from simply using AI more often. It will come from building an AI system that thinks the way you think. 

The Hidden Problem With “Generic” AI 

Most AI systems are trained on massive amounts of public information, such as articles, websites, books, and datasets from across the internet. This gives them broad knowledge, but it also means they approach problems from a very generalized perspective. 

That works well for answering questions like “What are the benefits of supply chain diversification?” or “What are common marketing strategies for SaaS companies?” Yet, executives rarely make decisions in a generic environment. 

Your company has its own risk tolerance, and your leadership team has its own culture. The strategy for your business reflects years of experience, intuition, and lessons learned. 

When AI lacks this context, its recommendations can feel technically correct but strategically off. It might suggest ideas that contradict how your business operates or overlook the subtle dynamics inside your organization. 

This is why many AI experiments stall. The technology is impressive, but the advice feels detached from reality. 

Leadership Thinking Is a Strategic Asset 

Every successful company develops a unique decision-making pattern over time. Some leaders prioritize aggressive growth. Others emphasize operational efficiency. Some value experimentation and risk-taking, while others build businesses on discipline and predictability. These patterns are not random. They are the accumulated wisdom of leadership. 

They come from years of experience, market lessons, strategic frameworks, company culture, and leadership instincts. 

In traditional organizations, this knowledge lives inside people’s heads. When leaders leave, retire, or move on, much of that thinking leaves with them. One of the most powerful uses of AI is the ability to capture and digitize that leadership intelligence. Instead of being lost or diluted, the strategic thinking of the organization becomes part of the system itself. 

The Idea of a “Digital Leadership Mind” 

Imagine an AI system that does not just answer questions. Rather, it answers them the way your leadership team would. For example, when evaluating an acquisition, it understands your company’s acquisition philosophy. When reviewing strategy, it reflects the frameworks your organization believes in. When analyzing risk, it considers the tolerance level your leadership has historically used. This concept is sometimes described as creating a digital version of leadership thinking. 

Rather than replacing executives, the AI becomes a thought partner, an always-available advisor trained on how your organization thinks. Some leaders jokingly describe this as cloning themselves. AI is the closest technology we have ever had to making that possible. 

Why Bias Is Not a Bad Word in Business 

In the world of AI ethics, the word bias often carries negative connotations. But in business strategy, bias can be extremely valuable. 

Every company operates with a set of strategic biases: how aggressive you are in pricing, how quickly you enter new markets, how much risk you tolerate, how you balance growth versus profitability. 

These biases shape the identity of your business. Without them, decisions become generic. Generic decisions rarely produce exceptional companies. 

When AI is trained on your leadership thinking, such as your frameworks, priorities, and strategic philosophy, it begins to operate within those same boundaries. It does not simply provide an answer. It provides an answer aligned with how your organization thinks. This is where AI becomes more than a tool. It becomes a strategic extension of leadership. 

Capturing the Intelligence Already Inside Your Company 

One of the biggest missed opportunities in business is how much knowledge disappears after meetings. Leadership teams gather in rooms every week, and ideas are debated while insights are shared. In these meetings, important strategies are formed. Then the meeting ends, and most of that thinking vanishes. Even with notes and slides, the full richness of the discussion is rarely captured. 

Modern AI systems can record and analyze these conversations, identifying patterns, ideas, and insights that might otherwise be lost. Over time, this creates a living knowledge base of how the company thinks and operates. Instead of leadership intelligence fading over time, it compounds. The more conversations the system learns from, the better it becomes at understanding the organization. 

The Difference Between Public AI and Custom AI 

This is where the distinction between public AI tools and custom AI systems becomes critical. Public AI tools are incredibly useful, but they operate with a generalized worldview. 

Custom AI systems are trained on your organization’s leadership thinking, internal data, industry context, and strategic frameworks. In other words, they understand your company the way an experienced executive would. Many organizations begin their AI journey using public tools, which is a great starting point. Yet, the real strategic advantage often comes from building systems that are uniquely aligned with how the business operates. When that happens, AI stops feeling like an external service and starts functioning as part of the leadership team. 

The Competitive Advantage of Digitized Leadership 

Businesses have always tried to scale leadership thinking. Consultants write playbooks, and companies build training programs. Leaders even mentor future executives. AI introduces a new possibility: scaling leadership intelligence directly through technology. 

When leadership thinking becomes digitized, new employees learn faster, decisions become more consistent, insights become easier to access, and institutional knowledge is preserved. 

Perhaps most importantly, the organization becomes less dependent on a single individual. The knowledge that once lived in one leader’s head becomes accessible to the entire company. 

The Next Problem: When Every Leader’s AI Thinks Like Them 

There is a step beyond this that most companies have not thought through yet, and it is the one that will separate the companies that lead from the ones that stall. 

Teaching AI to think like your organization is the right move. The problem surfaces when every leader on your team does it. Your CFO builds an AI that thinks like her. Your CMO builds one that thinks like him. Your COO and your head of sales both do the same. Each one of them is sharper, faster, and more certain than ever. Each walks into your next leadership meeting armed by a machine that has validated their thinking, sharpened their arguments, and made their position airtight. 

You have not built a smarter leadership team. You have built a team of smarter individuals who agree less. 

The meetings get harder. The debates get louder. The decisions that should take twenty minutes take three rounds. And the CEO can no longer tell a genuine strategic disagreement from two smart people who simply drew their conclusions from different inputs. That is not a people problem. It is an alignment problem. 

“AI does not just make your leaders more productive. When each one is running their own, it quietly privatizes their conviction. The technology that was supposed to get everyone on the same page is building better cases for staying off it.” 

The answer is not to pull back on individual AI. The answer is to add a layer. A layer that sits on top of the tools each leader is already using and aligns the intelligence so that when the room comes together, everyone is working from the same foundation. The debate that follows is real, not manufactured by mismatched inputs. The decisions that come out of it stick, because the whole team was reasoning from the same place. 

AI that thinks like you is the first move. AI that aligns your whole team is the one that changes the game. 

The Future of AI in the Executive Suite 

The future of AI in business will not simply be about automation. Instead, it will be about amplification. Amplifying the thinking of leadership teams, the insights buried inside the organization, and the decisions that shape the next decade. 

In that future, the most successful AI systems will not be the ones with the largest datasets or the most impressive interfaces. They will be the ones that understand the organization using them, and that keep the organization aligned as it grows. The companies that win will not just ask AI for answers. They will teach AI how they think, align that thinking across the full leadership team, and then let it help them think even better.