Key Takeaways
- Most CEOs run a three-layer AI stack: personal productivity (ChatGPT, Claude), execution (Copilot, Gemini), and company-specific tools built on proprietary data.
- Multihoming is the norm. Organizations are using ChatGPT and Claude side by side, choosing based on the task.
- Data readiness determines AI readiness. The organizations pulling ahead audited their foundations first and resisted the urge to consolidate too early.
Every week, I hear some version of the same question from executives across industries: “What AI tools do other CEOs use?” It comes from dental practices, banks, manufacturers, and even professional services firms. While the context changes, the anxiety underneath it does not.
Three Layers, One Leadership Reality
At the top of the stack, you have the personal productivity layer. This is where ChatGPT and Claude live. CEOs use these for synthesizing information, drafting board materials, preparing for conversations, and doing the kind of deep analytical thinking that used to take hours. ChatGPT tends to be the go-to for broad brainstorming and writing, while Claude has carved out a strong niche for long-form reasoning, document analysis, and complex strategy work.
What I find interesting is that many organizations don’t choose between these two. They use both, selecting them based on task. This “multihoming” approach makes sense given how quickly the models are evolving and how differently they perform across use cases.
The second layer focuses on execution, and this is where Microsoft 365 Copilot and Google Gemini come into play. Even CEOs who love Claude or ChatGPT for strategic thinking tend to live their actual workday inside Outlook, Teams, Gmail, or Meet. The AI embedded in those platforms handles the day-to-day workflow because that is where the permissions, the calendar, and the institutional memory already sit.
Finally, there is the company-specific layer, which is the most underdeveloped and can prove to be the most valuable. This is where internal retrieval-augmented generation (RAG) systems, role-based agents, and guardrailed workflows come into play. Think finance Q&A bots, pipeline review tools, customer insight summaries, and operating review assistants. These are built on proprietary data, which means they carry real competitive advantage when done well. They also carry real risk when done poorly, which is exactly why legal, finance, and security teams need to be part of the design conversation from day one.
Fragmentation Is Normal. Stagnation Is Not.
The stack most organizations are running today is fragmented, and that is okay. We are still in an early chapter. What matters is being intentional about where each tool sits, what data it touches, and who is responsible for governing it.
What This Means for Your Organization
If you are a CEO or senior leader trying to get your arms around this, here is where I would focus your thinking.
Start by auditing what your team is actually using today. Shadow AI is real. Employees are already experimenting, often with tools that have not been vetted by IT or legal. Understanding the unofficial stack is as important as designing the official one.
Next, be honest about your data readiness at each layer. Personal productivity tools can tolerate some ambiguity. Company-specific agents cannot. If you are feeding an agent outdated vendor data or inconsistent CRM records, the outputs will reflect that, and the consequences can range from embarrassing to genuinely harmful.
Finally, resist the urge to consolidate too quickly. The market is still maturing rapidly. Maintaining optionality across vendors while building strong internal data foundations gives you the flexibility to adapt as the models and the use cases evolve.
The CEO AI stack is not a product decision. It is an organizational strategy. And like any strategy, it only works if the foundations underneath it are sound.
That’s the conversation I have most often with leaders. Rather than which tool to pick, we focus on whether their data, their governance, and their people are ready to make any tool work. The executives who are asking that question today are the ones who will have something real to show for their AI investments a year from now.
If you are wrestling with where to start or where you are stuck, I would love to hear what you are seeing in your organization. These are early days, and the best thinking about this is still being figured out together.
