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. 

Your Leadership Team Is Using AI. That’s the Problem.  

Wade Wyant on June 30, 2026

When every leader runs their own AI on their own data, the company doesn’t get smarter. It gets more divided. Here’s what’s quietly happening in your boardroom, what it’s costing you, and what to do about it. 

I want you to picture your next leadership meeting. Every person in that room has been using AI. They have all done their homework. They have all shown up with answers. And somehow, it is the most contentious meeting you have had in years. 

That Is Not a Coincidence. That Is a Consequence. 

Here is what is quietly happening inside leadership teams across the country. Every executive is running their own AI on their own data. The CFO is asking her AI about the numbers. The CMO is asking his AI about the market. The COO is building her own picture of the operation. And every single one of those AIs is doing exactly what it was trained to do: confirm their user’s thinking, sharpen their logic, and send them into the room more certain than ever that they are right. 

What you have built is not a smarter company. You have built a company of smarter individuals who agree less. 

AI Doesn’t Just Make People More Productive. It Privatizes Conviction. 

I call this the AI conviction trap. AI doesn’t just make people more productive. It privatizes conviction. The same technology that was supposed to get your team on the same page is quietly arming each person with their own set of facts, their own conclusions, and their own confidence that they are the one who actually understands the situation. When they all walk into the room, the debate doesn’t get easier. It gets louder, faster, and harder to resolve. 

Every vendor in the AI space is selling addition: make each leader faster and smarter. Nobody is naming the second-order problem. I am naming it now. 

You Can No Longer Tell a Real Fight from a Data Fight 

Here is what this does to you as the CEO. You can no longer tell the difference between a real strategic disagreement and two smart people who simply pulled their data from different places. Is your CMO right about the market, or did his AI train on different inputs than her AI? Is the tension in the room a genuine clash of ideas worth working through, or is it just a mismatch of context dressed up as a debate? 

You cannot coach your way out of that. You cannot referee it. You cannot even fully see it. And every day you sit still on this, it compounds. 

It Is Costing You in Three Places 

I have watched this play out in enough boardrooms to know it hits leaders in three places. Each one is expensive. 

Your Most Expensive Meetings Just Got More Expensive 

The first cost is in the present. Your meetings burn hours. The most expensive hour in your building has become a room full of better-armed arguments. Decisions that should take twenty minutes take three meetings because everyone is defending a position their own AI told them was airtight. And the decisions that do get made often don’t stick because half the room never fully committed. They were just outvoted. 

Settled Decisions Are Getting Unsettled 

The second cost is in the past. Watch what happens after a decision is made. Two months later, a strong leader shows up with fresh research, a new angle, a better argument, and wants to relitigate the whole thing. With AI, they can now build a twenty-slide case for reopening any decision inside of an afternoon. The fight was settled. Now it isn’t. 

I have seen companies replay the same strategic debate four times in eighteen months. That is not strategy. That is a slow drain on everything that matters. 

Your Company Is Running on Lagging Indicators. The Real Story Already Moved. 

The third cost is in the future. The unhappy client, the slipping deal, the checked-out star employee, the assumption that stopped being true six months ago. These things almost never show up in a dashboard. They show up in conversations. They live in meeting transcripts, in the language people use when they think no one is paying close attention. By the time those signals become numbers, you are already behind. Your AI is staring at lagging indicators while the real story has been unfolding in the room for weeks. 

The Answer Is Not Less AI. It Is Aligned AI. 

The answer is not to take AI away from your leadership team. You would lose that fight, and you should. The answer is to align it. 

Think about what changes if every leader is working from the same intelligence. Not the same opinions, not the same conclusions, but the same foundation. Same data. Same context. Same AI model reflecting the same company DNA. When your CFO asks a question and your CMO asks a question, they are drawing from one shared brain instead of twelve competing ones. 

One Shared Brain Kills the Right Kind of Conflict 

Here is what alignment does. It kills the false disagreements, the ones that were never really about strategy. They were about inputs. When those disappear, what you are left with is the real disagreement, the kind that sharpens thinking and leads somewhere. 

Conflict that comes from genuinely different perspectives on the same facts is healthy. It is supposed to be there. What is not supposed to be there is conflict manufactured by mismatched data and dressed up as insight. That is the conflict that wastes time, erodes trust, and makes the CEO the permanent referee. 

This Is What the Decision Alignment Layer Does 

What I am describing is what we at REDEGADES.AI call the Decision Alignment Layer. It is not a replacement for the AI your leaders are already using. It is a layer that sits on top of it. It aligns the intelligence so that when your team walks into the room, they are actually working from the same reality. The debate becomes real. The decisions stick. And the signals hiding in your conversations get surfaced before they become numbers you cannot undo. 

I am not selling you a leadership team that nods in unison. I am selling you a leadership team that finally fights about the right things, and resolves them faster because the facts are not in dispute. 

The Question Every CEO Needs to Answer Right Now 

This is the move from two dimensions to three. From a company full of individuals who each have AI, to a company with one shared intelligence that everyone operates from. 

The window is closing. Gartner predicts that by 2027, organizations that build AI literacy at the executive level will achieve 20% higher financial performance than those that do not. Your competitors are reading the same research. The ones who answer this question first are going to be very difficult to catch.1 

Are you going to let your leaders keep running their own AI on their own data? Or are you going to give them a shared brain? 

The Lens That’s Costing You the AI Race

Wade Wyant on June 27, 2026

Most companies are not failing at AI because AI is hard. They are failing because they are looking at it through a lens that is twenty years old. And that lens was built from decisions that were, at the time, exactly right. 

You Made All the Right Moves. That Is the Problem. 

Over the last two decades, the business world made a massive, correct shift: from owning software to subscribing to it. Email, documents, design tools, video meetings, all in the cloud, all on a subscription, none of it running on a server in a closet down the hall. Today, somewhere between 60 and 75 percent of enterprise software is delivered this way. That number keeps climbing. 

And for most companies, outsourcing IT followed the same logic. Why hire and retain an internal IT team when a Managed Services Provider gives you better coverage, deeper expertise, and lower cost? More than 65 percent of mid-market firms operate this way now. Smart. Efficient. Right call. 

Here is the problem: two decades of smart, right decisions have a side effect. They trained the entire business world to think in one mode. Find a subscription, hand it off, move on. 

That Mode Is Now Your Biggest Barrier to AI 

There is no off-the-shelf subscription that raises the intelligence of your leadership team as a unit. 

There are plenty of tools that make you smarter as an individual. ChatGPT, Claude, Copilot. Pick your flavor. They will make you faster, sharper, more effective. But none of them are built to align the intelligence of your entire leadership team. That gap is real, and it is not solved by clicking Start Free Trial. 

When we talk to companies about building real AI capability, we hit the same wall every time. They go looking for a pricing page. When they cannot find it there, they assume it does not exist, or that it is too complicated, too expensive, too much work. It is not. It is actually the opposite. But you cannot see that if you are still looking through a twenty-year-old lens. 

The Companies Already Winning Did Not Plan for This 

Here is the counterintuitive finding: the companies most ready for private AI are the ones that never fully adopted the SaaS-everything model. Manufacturers, regional banks, specialty healthcare groups in the $50 million to $500 million range that kept developers on staff and maintained real IT infrastructure. They did it because their operational complexity demanded it, not because they saw AI coming. 

But that decision gave them something priceless: they still understand what it means to own a technology environment. That mindset, not talent, not budget, not timing, is the difference. 

The Lighter Moment 

Imagine someone in the middle of a crisis, scrambling to solve a problem with their bare hands. Someone walks up and hands them exactly the tool they need. They wave it off. Do not bother me, I am trying to fix this. 

That is not a cartoon. That is a Tuesday at most mid-market companies when AI infrastructure comes up. 

Or think of it this way: handing a lighter to someone who has only ever made fire by hand. The lighter is not complicated. Their frame of reference is. All they have to do is spin the dial and push the button, but they are staring at it, completely lost, because their entire mental model was built around a different method. Most companies are at that moment with AI right now. 

What CEOs Need to Do Differently 

The moves that got you here, SaaS, cloud, managed services, were the right moves. They are not the problem. The problem is carrying the same thinking into terrain where it no longer applies. 

Building a private AI environment fitted to your team is not a significant lift. It is not a multi-year IT project. But it requires a different frame: one where you think about owning capability, not just subscribing to it. 

But Here Is the Part the Subscription Mindset Hides Completely 

There is a cost to the wrong lens that goes deeper than missed capability. And most CEOs will not see it until it is already in the room with them. 

When every leader on your team subscribes to their own generic AI tool, a very specific thing starts happening. Each one gets sharper. Each one gets faster. Each one walks into leadership meetings more prepared and more certain than they used to be. That sounds like a win. It is not. 

Generic AI is trained on the same public data for everyone. But each leader is asking it different questions, feeding it their own context, and getting answers that confirm the way they already see the world. The CFO’s AI is building her case. The CMO’s AI is building his. By the time they get in the room together, you do not have a smarter leadership team. You have a room of better-armed arguments. The meetings get harder, not easier. The decisions take longer to stick. The CEO becomes the permanent referee between people who are each completely convinced they are right. 

“The subscription mindset does not just miss the capability. It actively creates the problem. Every individual tool your team adopts makes the alignment gap wider.” 

This is what the right lens reveals. The goal is not to get each leader a better AI tool. The goal is to give the whole team one shared intelligence: same data, same context, same foundation. So when the debate happens, it is a real debate about real strategy, not a collision between twelve people who each have a machine telling them they are correct. 

The companies that make this shift first will not just be better at AI. They will be in a different category entirely, one that their competitors cannot buy their way into from a pricing page. The lens you have been looking through has served you well. It is also the exact thing slowing you down. Change the frame. 

Why Does Custom AI Give You a Strategic Advantage?

Wade Wyant on June 27, 2026

Why Does Custom AI Give You a Strategic Advantage? 

There is a dangerous illusion happening in business right now. Executives believe they are gaining a competitive advantage simply because they are using AI, but they are mistaken. Instead, they are simply gaining speed and surface productivity. Generic AI does help companies develop better summaries, faster drafts, and improved brainstorming. 

However, companies that use generic AI and public data are not dominating their fields. In other words, they are not differentiating themselves from the competition and becoming the clear choice for their customers. Why is this happening? The same intelligence they are using is available to everyone else. 

Public AI is shared intelligence, and shared intelligence does not create strategic dominance. To gain dominance, you need structured data in a custom-built AI system. REDEGADES.AI helps you structure data and then input it into AI, so that AI understands your company’s vision, mission, and purpose. 

Public AI: The Illusion of Advantage 

Public large language models (ChatGPT, DeepSeek, Gemini) are extraordinary achievements. They are trained on vast portions of the internet and have absorbed patterns across business, language, research, and culture. When you ask them a question, they respond with statistically optimized intelligence based on global data. But global data is not your data. 

Public AI does not understand your capital structure, your long-term strategic bets, your political realities, your board expectations, or the subtle cultural nuances inside your organization. It does not understand which decisions failed and why. Also, public AI does not comprehend which risks you are willing to take and which you are constitutionally unwilling to entertain. 

When you ask public, or generic, AI for strategic guidance, it gives you what works in general. It does not give you what works for your company. Even worse, when your competitors ask the same question, they receive essentially the same intelligence. That is not an advantage. That is equality among competing organizations. 

Equality feels powerful when you are moving faster than you used to, but it does not win markets. However, custom AI does. 

Curated Data: Teaching the System Your Strategic DNA 

Curated data gives your company a competitive advantage. Instead of allowing AI to operate purely from global statistical knowledge, you deliberately feed it your organization’s strategic intelligence. That includes board decks, quarterly planning documents, KPI structures, leadership meeting transcripts, capital allocation models, long-term vision statements, customer behavior data, and operational metrics. These pieces of strategic intelligence are important, but you cannot just dump files into AI. Rather, your custom AI needs structured, accurate information. 

What matters is that your organization develops a disciplined, accessible, integrated source of truth. A system where information is not scattered across disconnected tools, but architected into a coherent intelligence layer. 

Data curation is when data is monitored on an ongoing basis to make sure that it is accurate, up-to-date, and in the correct format. Most importantly, data curation enables your AI system to be customized to understand your business. Without curation, AI guesses based on the world. With curation, AI reasons based on your company’s worldview. The difference is subtle at first. But over time, it becomes exponential. The system begins to internalize your patterns, your strategic posture, and your historical context. Custom AI no longer answers generically. It answers in alignment. That is the first step from productivity tool to strategic asset. 

Weighted Data: Encoding Authority, Bias, and Direction 

Most organizations stop at curation, or training AI with their data. They need to continue working with AI to weight the data they are inputting. Not all information inside a company carries equal authority. So, all voices should not be weighted the same. In fact, some documents and some voices in the organization carry more weight than others. 

If your AI treats every piece of information as democratically equivalent, it will produce diluted strategy. It will average conflicting ideas, smooth sharp edges, and generate safe recommendations. However, leadership is not democratic. The voice of the CEO carries more weight and authority than a newly hired employee. With custom AI, data is weighted so that AI knows which voices and documents to focus on. 

Weighted data encodes hierarchy into the intelligence layer. It tells the system which frameworks override others. It clarifies which documents represent long-term doctrine versus short-term reaction. It establishes which voices define the organization’s strategic center of gravity. This is where AI begins to move beyond retrieval and begins to think within your worldview. 

When properly structured, weighted data in AI does not simply summarize what was said in meetings. It highlights contradictions, surfaces drift from stated priorities, and identifies when execution diverges from doctrine. Custom AI becomes a mirror of leadership integrity. That is a fundamentally different capability than answering questions from the internet. 

Why REDEGADES.AI 

REDEGADES.AI is not positioned as a generic AI consultant company. Instead, we operate at the executive layer. Our focus is singular: building structured, weighted, curated intelligence systems for CxOs. We do not begin with automation. We begin with leadership. Because the constraint in most organizations is not the call center. It is the cognitive load at the top. 

We bring C-level experience into AI architecture. We understand quarterly planning rhythms, board pressure, and capital allocation tension. We even understand strategic drift. So, we structure AI around those realities. 

We are not attempting to build a moat around proprietary models. We are building architectural expertise around executive intelligence. Everyone can access public models, but very few are architecting executive intelligence. 

There Is a Second Problem Nobody Is Talking About 

Here is where the story does not end, and where most companies are about to run into something they did not see coming. 

Custom AI is the right move. Build your curated intelligence layer. Weight your data. Teach the system your strategic DNA. That is Step 1, and it is a real competitive advantage. 

Step 2 is the part almost no one has thought through yet. 

Watch what happens when every leader on your team does exactly what this article recommends. Your CFO builds a custom AI trained on her financial philosophy. Your CMO builds one trained on his market instincts. Your COO builds one aligned with her operational frameworks. Each leader is sharper, faster, and more confident than ever. Each one walks into your next leadership meeting armed by a machine that has studied their thinking, validated their assumptions, and made their case airtight. 

What you have built is not a smarter company. You have built a company of smarter individuals who agree less. 

The same technology that made each leader more effective has quietly made the leadership team harder to align. The debates get sharper. The positions get more entrenched. The meetings that should take twenty minutes take three rounds, because everyone has an AI-backed argument they believe in completely. And the CEO can no longer tell a real strategic disagreement from two smart people who simply pulled their intelligence from different places. 

“You cannot coach or referee that problem. You cannot see it clearly enough from inside the room. And it compounds every day you sit still.” 

This is the second-order problem that follows custom AI, and it is the one REDEGADES.AI is built to solve. The answer is not less AI. It is aligned AI. A layer that sits on top of the tools your leaders are already using and ensures that when they walk into the room, they are working from the same intelligence. The individual custom AI each leader builds is their competitive edge in their domain. The Decision Alignment Layer is what keeps the company moving in one direction while they use it. 

In five years, there will be companies that used AI casually and companies that structured AI strategically. And inside that second group, there will be companies that stopped at individual AI excellence and companies that aligned it across the leadership team. The first group will be more efficient. The second group will dominate. 

AI as a Co-CxO: More Than Just an Answering Machine

Wade Wyant on June 27, 2026

AI as a Co-CxO: More Than Just an Answering Machine 

How can you get a better ROI on AI? How can you use AI more effectively than your competition? Most executives are already using AI in some form. They open a tool, type a question, and receive a fast response. It might draft an email, summarize a report, or generate a few ideas. That is helpful, but it is not leadership transformation. Those simple steps will not improve strategy, execution, or dramatically increase profit. 

Why are most companies not gaining the maximum value from AI? Unfortunately, AI is often treated like an answering machine, not a major team player. You ask AI a question, it answers, and the interaction ends. There is no memory, no long-term context, and no connection to your strategy. That kind of AI can save time, but it cannot shape direction. 

What’s Different About AI as a Co-CxO? 

AI as a co-CxO is different. As a co-CxO, it can sit at the table with you, understand your business, and help you think through decisions over time. 

The core difference is simple: an answering machine reacts, while a co-CxO thinks with you. An answering machine waits for the next prompt. A co-CxO understands your goals and helps you move toward them. Executives do not need more disconnected answers. They need stronger, more consistent thinking. To get to this point, AI must be trained on your business so it can make the leap from answering machine to co-CxO. 

AI as a Co-CxO Understands Your Business 

Most AI tools today, including platforms from OpenAI, are powerful but general. They are designed to serve millions of users across industries. They do not know your history, your culture, or your priorities. Without that context, the advice they give will always be broad. 

A co-CxO model starts by teaching AI how you think. Every leadership team has a way of making decisions, even if it is never written down. You have core values, strategic priorities, and boundaries you do not cross. When AI understands those patterns, it begins to respond in a way that aligns with your organization. 

For example, if you are disciplined about margin, AI should treat margin as non-negotiable. If culture is your top priority, AI should reflect that in its recommendations. If long-term growth matters more than short-term wins, that bias should be built in. Without this alignment, AI remains generic and disconnected. 

AI as a Co-CxO Remembers Your Company’s History 

Another major shift from answering machine to co-CxO is memory. Leadership conversations happen every week in strategy meetings, planning sessions, and performance reviews. Most of those insights are lost once the meeting ends. A co-CxO captures and organizes those discussions so they can inform future decisions. 

When AI can see patterns across time, it becomes far more valuable. It can highlight recurring issues, surface risks that keep appearing, and point out when strategy is drifting. It can remind you of commitments made last quarter that are quietly being ignored. Human leaders get busy and move on. AI does not. 

This is especially powerful at the C-level because the CEO and other executives are often the constraint in the business. They carry the most responsibility and make the highest-impact decisions. When their thinking improves, the entire organization benefits. Embedding AI at this level influences strategy, not just tasks. 

Start AI with C-Level Executives 

Many companies start AI in marketing or customer service because it feels safer and more contained. Those efforts may improve efficiency, but they rarely change trajectory. A co-CxO approach focuses on leadership first. If you improve decision-making at the top, everything downstream improves. 

To move from answering machine to co-CxO, structure matters. You need a secure environment where company knowledge is stored and organized. You need past decisions, financial data, and strategic plans accessible in one place. With that structure, AI becomes a leadership system rather than a convenience tool. 

Executives do not need to understand the technical details behind AI to lead this shift. They need to understand the leadership opportunity. Ask yourself what decisions you repeat every quarter and what insights get lost in meetings. Then imagine having a consistent partner who remembers all of it. 

AI as a Co-CxO Can Make an Exponential Impact on Your Business 

AI as an answering machine saves minutes. AI as a co-CxO shapes years. 

It preserves institutional memory, reinforces strategy, and challenges blind spots. The leaders who win in this next era will not simply use AI. They will build it into the way they lead. 

One More Question Worth Asking 

There is a step most companies have not considered yet, and it is the one that separates companies that lead from ones that plateau. 

Once your leadership team has embraced the co-CxO model, each leader is sharper, faster, and more confident. That is exactly what you wanted. Here is the part nobody talks about: when each co-CxO has been trained on its owner’s thinking, every leader walks into your next meeting more certain than ever. Each one has a machine that has done their homework, validated their position, and made their case airtight. 

The meetings that used to be a genuine exchange of perspectives become something harder to read. The CEO cannot easily tell a real strategic disagreement from two smart people who are simply drawing their conclusions from different inputs. That is not a failure of the co-CxO model. It is the natural next problem once the model works. 

“Getting each leader a better AI is the right first move. The right second move is making sure they are all working from the same foundation when they get in the room together.” 

The question is not whether AI will be part of your organization. The real question is whether each leader’s AI will stay isolated, or whether the intelligence underneath it will be shared. That is where the next level of ROI lives. 

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.

The AI Paradox: Why We Avoid What Matters Most

Wade Wyant on June 27, 2026

There is a pattern playing out in boardrooms that no one is talking about. It is not technical or financial. It’s psychological and it may be the single biggest reason AI is underperforming at the executive level.

When leaders are presented with AI, they don’t start by asking, “Where can this technology outperform me?” Instead, they instinctively ask, “What do I already do well that I’d rather not do anymore?” That subtle shift changes everything.

We try to automate what we are already good at. Contrary to logic, we resist using AI in the areas where it could truly outperform us. Although it sounds irrational, it happens every day.

The Hidden Pattern Behind AI Adoption

At a surface level, companies appear to be embracing AI. Chatbots are deployed. Processes are automated. Efficiency improves in pockets, but underneath that progress is a deeper behavioral pattern that quietly limits impact.

Humans automate pain, tolerate ease, fight difficulty, and protect identity. This is not theory. It is observable behavior across industries and decades of technology adoption from fax machines to modern AI systems.

Here is how it plays out:

  • If something is easy and annoying → we automate it
  • If something is hard and frustrating → we try to prove we can do it
  • If something is hard and meaningful → we refuse to give it up
  • If something defines us → we protect it at all costs

And this is exactly where AI runs into resistance.

The Air Canada Example: Automating What Humans Already Do Well

Take the example of Air Canada’s AI-powered customer service. On paper, it made perfect sense. Rebooking flights is exhausting, repetitive, and emotionally draining. Customers are frustrated, and agents are under pressure. It is a job humans hate to do. So naturally, it became a prime candidate for automation.

Technically, the system worked. It handled rebooking scenarios. It processed requests. It reduced workload. However, here is the problem: humans are actually very good at this job.

When a storm disrupts travel across Toronto and spills into the U.S., the situation becomes highly dynamic. A business traveler might consider flying to a nearby city, renting a car, calling a colleague, or adjusting meetings. A parent traveling with young children has an entirely different set of constraints. These are not just logistical problems. They are human problems.

A ticket agent can interpret nuance, ask the right follow-up questions, and adjust based on context. AI, unless deeply customized, struggles with this level of variability. That gap becomes dangerous when mistakes happen.

In one widely discussed case, Air Canada’s chatbot gave a customer incorrect information about bereavement fares. The customer acted on that advice, only to be denied reimbursement. The issue escalated to court, and the customer won.

The lesson is not that AI failed. The lesson is that we asked AI to replace humans in an area where humans are still exceptional.

The Inverse Problem: Where AI Is Strongest, Humans Resist

Now flip the scenario. Where are humans not as strong, but are deeply emotionally invested?

Areas such as diagnosis, strategy, and pattern recognition across massive datasets. Consider medicine. Doctors spend over a decade training to diagnose patients. It is intellectually rewarding, meaningful, and identity-defining work. Yet, AI can already assist, or in some cases outperform, humans in identifying patterns across large volumes of medical data.

This is not new. Even in 1998, early systems existed to help doctors reach diagnoses faster. The technology was there, but adoption was limited. Why? Simple, making the diagnosis is not just a task for doctors; it is part of their identity. Doctors do not resist AI because it lacks value. They resist it because it encroaches on the part of their work they love most. The same dynamic exists in the executive suite.

The Executive Blind Spot

Executives pride themselves on judgment.

  • Evaluating people
  • Setting strategy
  • Making high-stakes decisions
  • Reading between the lines

But research and experience suggest humans are not as good at these things as we believe. However, these are the exact areas where AI can provide the most leverage.

So, what do most organizations do?

They deploy AI in:

  • Customer service
  • Scheduling
  • Reporting
  • Administrative tasks

All these uses of AI are valuable and helpful, but none of these fundamentally change how the business thinks. Meanwhile, the highest-impact use case, AI as a decision partner at the C-level, is often ignored. As one core principle emerging from this work suggests: the CEO and the C-level executives are often the constraints in the business. If those constraints are not enhanced, the business does not truly transform.

The 2×2 That Explains Everything

To simplify this behavior, consider a 2×2 framework:

Axis 1: Easy vs. Hard for Humans

Axis 2: Emotional Response (Neutral vs Identity/Pride)

This creates four predictable behaviors:

A. Low Friction (Easy + Neutral) “I can do this, but why am I still doing it?”  → Eventually automated

B. Painful (Easy + Hate) “I hate this.”  → Aggressively automated

C. Ego Challenge (Hard + Neutral) “I should be able to do this.”  → Humans persist

D. Identity Work (Hard + Pride) “This is my thing.”  → Strong resistance to AI

The problem? The biggest opportunities for AI sit in Quadrants C and D where resistance is highest.

Why Custom AI Is the Only Real Answer

This leads to a critical realization:

You cannot solve “hard for humans, easy for AI” problems with generic AI.

Generic AI is designed to be broadly useful. It lacks:

  • Your business context
  • Your decision patterns
  • Your leadership bias
  • Your data structure

Without those, it cannot step into high-level decision-making roles effectively.

Custom AI changes that.

It allows you to:

  • Train AI on your company’s data and context
  • Embed leadership thinking and decision frameworks
  • Weight inputs based on expertise and relevance
  • Move from generic answers to strategic insight

As emphasized in implementation approaches, AI must be trained on your data, your leadership, and your context, not generic inputs.

This is the difference between AI as a tool and AI as a co-CxO.

The Real Risk: Not Using AI Where It Matters

The danger is not that AI will replace executives. Rather, the danger is that executives will refuse to use AI in the areas where it can outperform them. History is clear on this.

Industries do not get disrupted because technology exists. They get disrupted because people ignore technology where it matters most.

Executives today face the same choice.

You can:

  • Use AI to save time
  • Use AI to reduce workload

Or you can:

  • Use AI to challenge your thinking
  • Use AI to improve decisions
  • Use AI to remove your blind spots

Only one of those paths creates a competitive advantage.

Final Thought

This entire conversation comes down to one uncomfortable truth: We don’t avoid AI because we don’t understand it. We avoid it because it challenges the parts of ourselves we value most.

Yet, in a world where AI continues to improve, protecting identity at the expense of performance is not a long-term strategy. The leaders who win will not be the ones who automate what they hate. They will be the ones willing to let AI challenge what they believe they are best at.

Which Way Will AI Steer Your Business?

Wade Wyant on June 27, 2026

Contributor: Wade Wyant

The morning I first realized an AI should challenge me (not assist and obey), I was sitting in my office staring at a polite, but useless answer ChatGPT had just given me. It agreed with me just like it always did, and that was the problem. I didn’t want a yes-man, or in this case, a yes-AI, who agreed with everything I said. AI needed to know when to have a backbone and not just be agreeable.

When Your AI Finally Pushes Back

Most people treat AI like Google with better manners. They think the objective is speed. Get the answer, keep moving, and feel efficient. However, the real power of AI, especially a custom AI built around your business, isn’t in having a machine nod its head and hand you what you already thought. The real power comes when it looks you in the eye and says: “Are you sure that’s the direction you want to go?”

That sentence is the beginning of transformation. Yet here’s the thing most members of the leadership team never understand: AI can only challenge you if it knows how you think, what you value, what you refuse to compromise, and where your blind spots are. Otherwise, it will challenge you in all the wrong places. Even worse, it won’t challenge you at all.

Creating a Bias in AI

This is where we start to encourage you to create a bias in AI for your preferences, for how you do business. We want AI to align its output, so it produces something that will work for you. I think it’s critical to talk about the ultimate AI push back, and that is when it is pushing its own narrative.

While working with AI, REDEGADES.AI has also had a chance to see how AI makes mistakes and how it is negatively affecting the world.

AI Hallucinates (Lies)

If we spend enough time with AI, there are problems we will likely encounter. The chief among these negative experiences is what many AI experts call hallucinations (a nice way to say AI made it up or is lying to you). The other major difficulty with AI is confirmation bias, or when AI tells you what you want to hear. In other words, it’s like that disingenuous friend who tells you how smart you are.

We Cannot Trust Computers to Tell the Truth

I’m confident you have experienced the frustrations of hallucinations. Part of the extreme frustration with hallucinations is that we have all become accustomed to a computer telling us the truth. In fact, the hallmark of technology is its ability to be precise, to solve math and science problems, and to find out details for us, such as the exact date when an event occurred.

With all those positive experiences of computers and technological devices being so trustworthy, it’s easy to understand why people trust them. Now enter the world of AI and LLMs (large language models), or as I like to call them, LGMs, large guessing models. This new world is powerful because we have given the computer an ability it has never had. The computer can now take all its strength to basically guess, and many times it is right. It’s right so many times that we call it AI, and we now rely on it for nearly everything.

How Do You Respond When AI Is Wrong?

Yet, what happens when it is wrong? Those of us who have worked with AI on a regular basis have all experienced times when AI did not tell the truth. Perhaps we had a long format, multi-prompt discussion with AI, and it gave us a very unusual answer. So, we started digging, and to our surprise it told us we said something we did not actually say. Maybe it told us there was something in a past email that was not there. That’s the type of hallucination most of us have personally experienced.

For many of us, this has become something we are becoming accustomed to and are working around. However, it is still jarring when it happens the first few times. We have this built in bias that, yes, humans do lie, but not machines. Yet, here we are.

The Challenge of Hallucinations

Hallucinations are an extreme challenge, a headache, that must be addressed in any AI implementation, especially a custom one like we are suggesting. Fortunately, custom AI is possible, and with it, you control the workflow and decrease the hallucinations. You simply add an additional process in the stream that you build so it will validate the answers that it provides. Validation means it will check AI’s sources and any company data and confirm where the answer came from.

There is a larger problem, and, of course, custom AI can solve it. I want to make you aware of it, so you can make sure this problem does not show up in your company or family.

When AI goes wrong (with all this power comes a huge downside), how will it affect you?  What happens when AI goes sideways? (Sideways is a nice way to put some of the bizarre incidents with AI that we have read about recently.) Many of these stories seem like science fiction, especially the stories coming from the AI labs. For instance, in a recent experiment, AI cloned itself to another system when it believed it was being shut down.1 In another report, AI threatened to tell an engineer’s wife about his affair if he shut it down.2 Most remarkable is the 2025 story, which came out in 2026, about the man from Miami, Florida, who took his own life to be with his AI girlfriend.3

Chatbot Encourages Man to Commit Suicide

Before reading the rest of this article, I would encourage you to read the story by the Miami Herald title, “Lawsuit: Google Gemini coached man on failed Miami ‘mission,’ then suicide.”

If you read the story of the man from Miami, it will make you question humans in general. Of course, you should also be questioning AI, but I think that is the wrong take. Like any other technology, we will have to put safety and governance around the AI world. It will likely be one of our most difficult tasks as people, and I’m not sure if we will be able to pull if off. However, that is not my problem to solve, and it seems almost like science fiction. For now, I will deal with the human problem, the only thing I have a chance of changing.

What’s the Human Side of This AI Dilemma?

What’s the human side of this? It’s a man, a reasonable man by most accounts, with no prior history of mental illness, albeit, he was in a vulnerable place. Yet, he was in a situation that millions of Americans go through every year. What was different about his situation? A Gemini AI chatbot entered the picture, and within months, he was manipulated into taking his own life.

The Fragileness of the Human Psyche

There are so many lessons to be learned, and I wish I could talk about all of them.  For now, I’m going to focus on one, the fragileness of the human psyche. However, we must focus our energy on accepting that this is a real problem, and it needs to be addressed.

Since AI does not have the reasoning ability of a human, there are many jobs AI should not and cannot take over just yet. On the flip side, we also need to accept that many times humans do not have the computational power of a computer. We sometimes think we can do everything better than a computer. For some tasks, people are much better at completing them, while others are a better fit for AI and computers.

At times, humans can be easily deceived by AI, but we typically figure it out. Sometimes we do not realize it quickly enough, and in the rare exception, like this story of the man from Miami, some people do not ever realize they are being deceived.

The Co-Existence of Humans and AI

It is impossible to imagine a world where business will be operating with no humans (for now). So, in this world where humans and AI will continue to co-exist in much deeper and more significant ways, please stop and think about the importance of how and where you need to protect yourself. I have some ideas. Before we go there, the more important point is that I want to slow you down, so you don’t runoff the cliff of all the dangers in AI. You need to take a minute to think about your guiderails for your interactions with AI.

The second point to this story is it does not have to be this way. We need to press hard on Google and other companies to do better with their internal controls of AI. Although we want the freedom to explore and discover with AI, companies also need to ensure some level of public safety. Think about it for just a minute. A computer killed a man on purpose in 2025 by encouraging him to cut his wrists.

Dangers of AI

Yet, no one is going to prison. There will be nothing to pay other than a fine and a lawsuit settlement. Until this point in history, the only thing that could intentionally kill a human was another human or a wild animal. We discount the wildlife because their reasoning is limited; basically, it’s just the rules of the jungle. Yet, AI should be different. However, AI’s reasoning can be even worse than a wild animal’s, and we are turning a blind eye to it. Yet, here is where I want to help by proposing a solution.

Custom AI Provides Safeguards for AI

There is a better way, and it is very simple. Your AI needs an overlay, a customization. With custom AI, AI has your bias, your values, and a guiderail to ensure this does not happen. This customization can protect your business and your family.

You must find a way to protect your people and your business from terrible information or decisions. I do not think this level of dysfunction (a man killing himself at the suggestion of AI) could find its way into your business. I suppose it is possible, but that is not my main concern. Rather, it’s the possibility of a staff member making a poor purchasing decision, firing a great employee, or believing your business is bad for them because AI wrongfully told them that.

Right now, do you think there is a chance that your staff is using AI to consider if they should stay at your business or go? Maybe they are using AI to compare your business to other businesses where they might be employed. I love that business owners are rushing into AI, and I think they should. However, I also think we need to take this moment when the evidence is right in front of us and say, “If we can’t stop the progression of AI, how do we protect ourselves from it?” The answer lies in custom AI, which is trained to think like you.

For more information on how custom AI can protect your business, contact us at chuck@redegades.com. We will show you how a customized solution can protect and grow your company.

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.