Maze to Matrix: Making AI Choices Make Sense

AI has become one of the most discussed boardroom topics in recent memory. But for all the excitement, there’s a quieter, more uncomfortable truth I encounter in nearly every executive conversation:

Organizations aren’t struggling with whether to adopt AI—they’re struggling with how to make the right AI decisions in the right order.

This struggle stems from the overwhelming array of options—where leaders find themselves caught in a maze of possibilities, where each path seems promising, but carries unique risks and dependencies.

The result is often the same: duplicated investments, stalled pilots, siloed initiatives, and a creeping sense that the organization is “doing AI” without really moving the needle. And the irony is it’s rarely a technology problem. It’s a decision problem.

The Executive AI Dilemma

Executives today are under enormous pressure to “do something with AI.” Stakeholders expect rapid productivity gains. Business units want innovation. IT leaders are tasked with ensuring security, governance, and scalability.

Somewhere in the middle of those competing demands, leaders are expected to make clear, strategic choices about which AI capabilities to deploy, where to start, and how to scale.

But the challenge is the AI landscape isn’t linear. Microsoft alone offers multiple, powerful entry points—from M365 Copilot to Copilot Studio to Azure AI Foundry—each designed for very different types of problems and levels of organizational maturity.

Without a clear framework, many organizations end up starting in the wrong place:

• They pursue highly customized AI development before they’ve unlocked quick wins in productivity.
• They automate workflows before understanding how people actually work.
• Or they roll out AI tools broadly without the governance structures to support responsible scale.

It’s not that these are bad strategies—they’re excellent. It’s that the sequence and alignment of decisions matter just as much as the decisions themselves.

From “Which Tool” to “Which Journey”

One of the most common mistakes I see is treating AI adoption as a tool selection exercise, rather than a strategic journey.

AI isn’t a monolith; it lives along a spectrum—from improving individual productivity to automating organizational workflows to building transformational capabilities that fundamentally reshape how the business operates.

Each stage demands different skills and talent, investment levels, governance structures, and risk tolerance. And more importantly, each stage creates value differently. A single Copilot license might boost hundreds of employees’ productivity in a matter of days. A custom model built through Azure AI Foundry might unlock entirely new revenue streams, but only if the organization is prepared for that level of transformation.

The Power of a Decision Matrix

This is why I’ve found it essential to introduce a simple, but powerful decision matrix when working with executive teams.

The purpose isn’t to add complexity — it’s to create clarity. The matrix helps leaders step back and ask:

• What kind of impact are we truly trying to create? Productivity, automation, or transformation?
• Where is our organization genuinely ready to operate today?
• How can sequencing our decisions maximize value and minimize rework?

Once executives view their AI options through this lens, the noise starts to clear. Patterns emerge. And AI stops being an abstract ambition and starts becoming a strategic roadmap.

I won’t unpack the full framework here, but the key insight is this: different AI capabilities serve different strategic purposes. Choosing wisely and sequencing intentionally often matters more than the technology itself.

Strategic Questions for Leaders

Before any organization invests another dollar in AI, I encourage leadership teams to wrestle with a few deceptively simple questions:

1. Are we trying to boost productivity, automate processes, or fundamentally transform how we operate?
   Each of these leads to different AI starting points and investment profiles.
2. Do we have the talent and readiness to support what we’re envisioning?
   The skills needed to turn on M365 Copilot are not the same as those needed to fine-tune large language models.
3. What is our actual risk tolerance?
   Many organizations claim they want to be cutting-edge, but operate with low governance maturity. The gap between ambition and reality can quietly derail even the most promising initiatives.
4. Are we prepared for the organizational implications of success?
   Scaling AI is not just about scaling technology. It’s about scaling trust, policy, change management, and measurement.

These questions are simple to ask, but surprisingly difficult to answer honestly. And they are precisely where the smartest AI strategies begin.

Where the Most Impactful Conversations Happen

I often find that the most meaningful conversations happen when leadership teams bring their real scenarios to the table. That’s why we run both focused strategy sessions and our broader “You Already Own It” interactive benefit sessions—to help organizations cut through the noise, sequence decisions intelligently, and chart a clear AI path forward.

Every organization’s journey is different, and these discussions often reveal opportunities and risks that aren’t obvious at first glance. More importantly, they create the space for leadership teams to align vision, readiness, and the right sequencing of AI decisions.

Turn Ambition into Thoughtful AI Execution

Technology will keep evolving at a breathtaking pace, but the executive responsibility remains timeless: to make strategic decisions that create lasting value.

Steering the journey with clarity is the real work of AI leadership today. The organizations that are making real strides with AI are not necessarily the ones spending the most. They’re the ones making disciplined, well-sequenced decisions.

• They start where their organization is ready.
• They choose entry points that deliver meaningful impact quickly.
• They build maturity and confidence incrementally.
• And they align their technology choices with business outcomes— not the other way around.

The AI Decision Matrix isn’t magic—it’s a way to bring structure to that responsibility.
And in my experience, clarity in decision-making is the single most powerful accelerant to AI success.