On April 7th, McKinsey published what they're calling The AI Transformation Manifesto, a distillation of twelve themes extracted from hundreds of large-scale AI transformations. It's drawn from the second edition of their book Rewired: How Leading Companies Win with Technology and AI, and it reads like a checklist for what separates companies that are actually transforming with AI from those that are still experimenting.
We read it carefully. The themes they've identified map closely to what we've been building at Gadoci Consulting since day one. Not because we copied McKinsey's playbook, but because anyone doing this work in the real world, with real companies, arrives at the same conclusions.
Here's what stood out.
Capabilities, not tools
McKinsey's first theme is their most important one: technology alone doesn't create advantage. Enduring capabilities do. The companies winning with AI aren't winning because they have better tools. Everyone has access to the same models. They're winning because they've built the organizational muscle to apply those tools to real problems, repeatedly.
This is the core of what we do. We don't sell AI tools. We help organizations build the capability to use them in ways that stick. Our L1/L2/L3 framework gives companies a structured path from individual productivity (L1) through workflow automation (L2) to custom AI applications (L3). Each level builds on the last, and the capability compounds over time.
Focus on what actually matters
The manifesto argues that successful companies focus AI efforts on a few key economic leverage points, not long lists of use cases. Most companies have massive backlogs of AI ideas. The ones that succeed are focused on where AI creates disproportionate impact.
This is why we built Solutioner.ai. When organizations run discovery across departments, ideas come in fast. Solutioner captures every opportunity as a solution brief, levels it as L1, L2, or L3, and gives leadership a clear view of what to prioritize based on business impact rather than technical novelty. It turns an unstructured backlog into a portfolio you can actually manage and execute against.
Business leaders drive it, not IT
McKinsey is blunt: they don't have a single success story where senior business leaders weren't in the driver's seat. IT supports the transformation. Business leaders own it.
We see this constantly. The organizations where AI gains real traction are the ones where the VP of Operations, or the Head of Marketing, or the CFO is personally invested in understanding what AI can do for their domain. Our AI Operator program is built around this reality. AI Operators are the people inside each department who combine domain expertise with AI fluency. They're not engineers. They're the curious ones who learn the tools, spot the opportunities, and carry the capability forward after we leave.
The AI Operator program doesn't create AI experts. It creates internal champions who bridge the gap between how their team works and what AI makes possible. That's the role McKinsey describes when they talk about leaders who combine deep business knowledge with AI know-how.
Speed and operationalization
The manifesto makes the case that speed is the defining organizational advantage. Companies that can move from insight to decision to action faster will outpace their competitors, because everyone has access to the same technology.
This is why we emphasize operationalization over experimentation. Pilot purgatory, where impressive demos never reach production, is one of the most common failure patterns we see. The gap between "this AI demo looks amazing" and "this AI system runs our process every day" is almost entirely operational. It's workflow integration, change management, and clear ownership. Not technology.
Our engagement model is designed to match the urgency. Discovery identifies high-impact opportunities in weeks. Solutions get leveled and prioritized through Solutioner.ai so leadership can make fast, informed decisions about where to invest. And because every solution brief captures the problem, the proposed approach, and the expected impact, teams can move from discovery to execution without the usual months of scoping and re-scoping.
Read the manifesto
McKinsey's piece is worth your time. You can read it at mckinsey.com. If you see your organization in the gap between knowing AI matters and actually operationalizing it, that's the conversation we're here to have.