How It Works

Before you spend another dollar, you need a point of view. How does AI fit into your business model? Where in your value chain does it create real advantage? Where does it create risk? What will you do with AI and what will you deliberately not do?

Just as important: how do your customers view AI? Where does it help their trust in you and where does it hurt it? A medical practice deploying AI has different credibility considerations than a home services company. Your strategy has to account for that.

Most founders have never articulated an AI strategy. They've bought tools. They've run experiments. But they've never sat down and defined a point of view that every downstream decision can align to. That's where the advisory work begins.

Once strategy is defined, the next problem is sequencing. You have dozens of possible AI applications. Which ones do you pursue first, second, and third?

The 3x3 Revenue Matrix from ROI and AI provides the framework. It organizes your options by business impact and implementation complexity, then identifies the sequence that puts AI closest to revenue first. When AI touches revenue before it touches anything else, it produces returns that fund everything that follows. AI becomes self-funding. That changes the entire risk profile of the investment.

Prioritization is the discipline that turns strategy into a plan. Without it, you get random experiments. With it, you get compounding returns.

With strategy defined and priorities sequenced, the third dimension is building and deploying. Selecting tools. Designing workflows. Integrating AI into existing operations. Choosing the right combination of internal builds and external providers.

Two books address this dimension because implementation has two layers. Enterprise and AI covers the discipline of implementing AI across the whole organization. Revenue and AI covers the specific implementation that matters most: putting AI where it touches revenue first.

The CEO's job in this dimension is not to do the implementation personally. It is to make the decisions that keep implementation aligned to strategy and sequenced to the plan. The advisory relationship ensures those decisions get made correctly and on time.

This is where most AI initiatives die. The tools are built. The systems are deployed. And the organization doesn't use them. Managers don't enforce adoption. Employees quietly revert to old workflows. The management team nods in meetings and resists in practice.

Adoption is not a training problem. It is a leadership problem. The CEO sets the direction. The management team carries it into the organization. Managers translate it into daily operations. Employees adopt when they see their leaders leading, and they resist when they see their leaders hedging.

Leadership and AI addresses the CEO's role in leading the transformation. Management and AI addresses the management layer's role in driving adoption through the organization. Two books because adoption requires leadership at the top and management in the middle.

This is the dimension that no AI consultant can help you with. Organizational transformation is a leadership discipline. It requires someone who has spent decades working at that altitude. That is Brett's deepest expertise, and it is the reason the Integral CEO Program exists.