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/. 

 

Five Things Co-CxO Actually Delivers 

Wade Wyant on July 18, 2026

By Wade Wyant, Founder, REDEGADES.AI 

When leaders ask me what Co-CxO actually does for a business, I don’t lead with technology. I lead with the five things every CEO is measured on: time, money, growth, competition, and return on investment. If your AI investment doesn’t move those five numbers, it doesn’t matter how good it looks in a demo. 

Here’s what Co-CxO actually delivers. 

The Hours Worth Reclaiming 

Your leadership team is burning time on work that a well-trained AI should handle. Research, analysis, briefings, prep work before every meeting. These are high-effort, low-leverage activities that eat into the hours your executives should spend on decisions only they can make. 

Co-CxO handles that preparation automatically. It knows your business, your data, and your strategic priorities. It doesn’t search the internet for generic answers. It reaches into your company’s own information and comes back with something actually useful. Your executives get hours back every week. They don’t spend those hours differently. They spend them better. 

The Price of Not Knowing 

Every leadership team makes bad decisions. That isn’t a criticism; it’s reality. The question is how often, and how expensive. Bad decisions carry a real price tag: the wrong hire, the missed market signal, the overconfident bet on a product no one wanted. Most of those decisions could have been caught earlier if someone had surfaced the right information at the right moment. 

Co-CxO is built to do exactly that. When your AI is trained on your company’s own data, your strategy, your history, and your decision patterns, it can flag what your leadership team isn’t seeing. It’s not a crystal ball. But it’s a second mind that doesn’t have an ego, doesn’t get tired, and doesn’t have a reason to tell you what you want to hear. 

Think of it this way: your AI investment is a risk management engine. Every bad decision it helps you avoid is money protected. Every blind spot caught early is a better decision waiting to happen. 

“The most expensive decisions are the ones nobody saw coming. Co-CxO is built to see them first.” 

Scale Without Starting Over 

The traditional answer to growth is hiring. You need more capacity, so you add more people. More senior people, more expensive people, people who then need onboarding and management before they contribute anything. It works, eventually. But it’s slow, expensive, and fragile. 

Co-CxO changes that equation. One of our clients navigated a 4.5x growth sprint. They did add to their leadership team as they scaled — that kind of growth requires it. But Co-CxO meant every new leader could contribute faster, stay aligned with where the company was headed, and build on the institutional knowledge already in the system rather than starting from scratch. Growth happened. The team didn’t have to reinvent itself every time it got bigger. 

That’s what I mean when I say AI is a leadership multiplier. Not a task robot. A multiplier. The difference matters more than most leaders realize until they’ve seen it work. 

The Leaders Who See It Coming 

Most leadership teams are looking at lagging indicators. Revenue that already happened. Problems that already landed. Data that describes yesterday. That’s not strategy. That’s a rearview mirror. 

Co-CxO analyzes your data and your trajectory to give your leadership team a predictive edge. It reads signals in your business before they become numbers you have to explain in a board meeting. It connects dots your team doesn’t have time to connect manually. The leaders who move now will spend the next several years reacting less and leading more. The ones who wait will keep playing catch-up with competitors who already made the move. 

The Investment That Compounds 

Here’s something about software that most people don’t say out loud: it depreciates. You buy it, implement it, and over time it gets stale. The vendor updates it. You retrain your team. The edge you had in year one is table stakes by year three. 

Co-CxO works the opposite way. The longer it runs inside your business, the smarter it gets. Every meeting it captures, every decision it learns, every strategy document it ingests makes the model more valuable. It compounds. It doesn’t depreciate; it appreciates. That isn’t a feature. That’s a fundamentally different relationship with your technology investment. 

“Unlike software that depreciates, Co-CxO compounds. The longer it runs, the smarter it gets — and the greater the return.” 

Five outcomes. One platform. All of it built around the way your leadership team actually thinks, decides, and leads. That’s the promise of Co-CxO. Not more technology. Better outcomes from the team you already have. 

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? 

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. 

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. 

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.

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.