I was listening to AI leaders from Moderna, Adobe, Mayo Clinic, Manulife, and other major organizations share their transformation stories at a recent AI summit. What I heard completely contradicted almost everything being written about AI implementation.

The companies that are actually succeeding with AI aren’t the ones with the best technology or the biggest budgets. They’re the ones that figured out it’s not really about AI at all. It’s about understanding work, managing change, and getting the human dynamics right.

Let me share what’s actually working and why most AI transformation advice is leading organizations toward expensive failures.

The Backward Approach That’s Failing Everywhere

Here’s the typical AI transformation playbook that most consultants and vendors are selling:

1. Evaluate AI platforms and choose the best technology
2. Identify high-value use cases
3. Run pilots to prove ROI
4. Scale successful pilots across the organization
5. Train people on how to use the new tools

Sounds logical, right? It’s also completely wrong.

The organizations succeeding with AI are doing almost the exact opposite. They’re starting with work design, focusing on change management, and treating technology selection as one of the final steps rather than the first.

What Actually Works: The Inside-Out Approach

The companies getting real results follow a fundamentally different sequence:

• Start with workflow understanding, not technology evaluation. Ravin Jesuthasan from Mercer was adamant that every manager needs to become a work designer, constantly deconstructing processes to understand where AI can substitute, augment, or transform work.
• Shruthi Shetty from SAP takes organizations through 30-60 day workflow mapping sprints before they buy any AI tools. The foundation that makes everything else work.
• Invest 80% of your energy in change management, not technology. Don Bennion from Adobe learned this the hard way: “The tech is hard, and change management is harder.” The companies succeeding with AI are spending most of their time on adoption, culture, and human dynamics.
• Deploy org-wide immediately, don’t start with pilots. This was Bryce Challamel’s most controversial insight from Moderna. His analogy: “Imagine internet comes, and you do an internet pilot.” Utilities are effective at scale when everyone has them and can learn from each other.
• Measure human-AI collaboration, not just usage. Moderna’s AI fitness score tracks four components: message volume, frequency of use, leveraging advanced capabilities, and connecting to data. This creates a dynamic system that evolves with AI capabilities rather than static adoption metrics.

The CEO Reality Check That Changes Everything

Lexi Reese from Lanai delivered the most uncomfortable truth for executives:

“The CEO cannot shove this to a third party to have said third party describe to the CEO how AI is going to transform their business. That won’t work.”

She recommends that executive teams use AI tools for 10 hours per week for four weeks before developing their AI strategy. Most CEOs will find this unrealistic, but consider the alternative: making multi-million dollar decisions about business transformation based on other people’s interpretations of technology you don’t understand.

The CEOs who are succeeding with AI aren’t delegating the strategic thinking. They’re developing enough personal fluency to make intelligent resource allocation decisions and credibly champion adoption throughout their organizations.

The Shadow AI Signal Everyone’s Misreading

In most organizations, unsanctioned ChatGPT usage is higher than sanctioned enterprise AI usage.

Instead of treating this as a compliance problem though, smart organizations are recognizing it as market research. When people circumvent approved tools to use alternatives, they’re telling you something important about user experience, capabilities, or accessibility.

Tony Gentilcore from Glean put it perfectly:

“You can’t stop this. What happens is everybody takes some public tool and they’re just gonna copy-paste out those PDFs and upload it to that. And you’ve lost control.”

The solution isn’t more restrictions. It’s better alternatives and governance strategies that channel innovation rather than block it.

The Human Dynamics That Make or Break Everything

Every successful AI transformation I heard about required solving human challenges that have nothing to do with technology:

The anxiety challenge. People are worried about AI’s impact on their jobs, and they have every right to be. Leaders who acknowledge this reality directly rather than offering false reassurance build trust that accelerates everything else.

The ego challenge. AI initiatives attract ambitious people who see career opportunities. Don’s insight from building Adobe’s “Coalition of the Willing“: explicitly address the fact that everyone wants to advance their careers and show how collaboration is actually the fastest path to recognition.

The skills challenge. Olya Taran from Manulife recognized prompting as an emerging business skill and invested heavily in helping people become genuinely good at it. This isn’t software training – it’s developing new ways of working that require practice and feedback.

The Three-Week Validation Rule That Saves Months

Once you’ve mapped workflows and chosen initial implementations, Shruthi from SAP shared an insight that can save enormous amounts of wasted effort: validate in three weeks, not three months.

“One of the biggest companies in the world has a validation period of 3 weeks. So if they can do it in 3 weeks, smaller companies can do it in one week.”

This rapid validation is only possible when you understand your workflows well enough to know what success looks like and can measure it quickly. It prevents the “pilot purgatory” that traps so many organizations.

The Framework That Ties Everything Together

Synthesizing all these insights, here’s the actual sequence that works:

Weeks 1-8: Workflow Mapping Sprint

Map current workflows in one strategically important area. Understand how work actually gets done, where bottlenecks occur, and what manual effort could be automated or augmented.

Weeks 9-12: Executive AI Fluency Building

Leadership team commits to hands-on AI usage in their own work. Not learning about AI, but actually using it for strategic analysis, communication, and decision-making.

Weeks 13-16: Coalition Building

Identify passionate early adopters and create collaboration frameworks that channel competitive energy into collective success. Address ego dynamics explicitly.

Weeks 17-20: Solution Mapping and Procurement

Map workflow needs to available solutions from existing vendors before considering custom builds. Make technology decisions based on workflow requirements, not vendor presentations.

Weeks 21-24: Rapid Deployment and Validation

Deploy solutions quickly and validate value within three weeks. Adjust based on real usage, not theoretical benefits.

Ongoing: Measurement and Evolution

Implement dynamic measurement systems that track human-AI collaboration effectiveness and evolve as capabilities advance.

The Competitive Advantage Nobody’s Talking About

The companies following this approach aren’t just implementing AI more successfully. They’re building organizational capabilities that will be incredibly difficult for competitors to replicate.

Moderna didn’t just achieve 100% AI adoption among knowledge workers. They created an organizational muscle for continuous workflow redesign that lets them adapt quickly as AI capabilities evolve.

Mayo Clinic didn’t just deploy AI diagnostic tools. They developed validation and integration capabilities that let them safely implement new AI applications faster than organizations that haven’t solved the change management challenges.

These aren’t technology advantages. They’re cultural and operational advantages that compound over time and become more valuable as AI capabilities continue advancing.

Why This Matters More Than You Think

Most organizations are still thinking about AI as a technology implementation challenge. They’re focused on platform selection, integration architecture, and technical capabilities.

But the real competitive advantage is being built by organizations that recognize AI transformation as fundamentally a human collaboration challenge. The technology will be available to everyone. The organizational muscle for effectively leveraging AI capabilities? That’s being built right now by companies that understand what actually drives successful adoption.

The window for being an early mover on the change management side is closing. The companies investing now in workflow understanding, coalition building, and human-AI collaboration measurement will be much harder to catch later.

The Question That Determines Your Success

The question isn’t whether AI will transform your industry. It will. The question isn’t whether you need better AI tools. You probably do.

The question is whether you’ll build the organizational capabilities to leverage AI effectively, or whether you’ll join the growing number of companies with expensive AI tools that nobody uses well.

The playbook for building those capabilities exists. It’s been proven by organizations that are actually succeeding with AI transformation at scale. It just looks nothing like what most people are being told about AI implementation.

The choice is whether you’ll follow the conventional wisdom that’s failing everywhere, or learn from the organizations that figured out what actually works.

Because while everyone else is arguing about which AI platform to buy, companies like Moderna are quietly building workforces that can leverage whatever AI capabilities emerge next. And that’s a competitive advantage that money can’t buy.

Ready to stop following conventional AI transformation advice and start building real organizational capabilities? I’d love to help you design an approach that focuses on what actually drives success rather than what sounds good in presentations. Connect with me on LinkedIn to explore how to implement the playbook that’s actually working for leading organizations.