You're the CEO on an All-Hands Call. One of your team members asks, "What is our AI strategy?" The true answer? You don't know.

This moment of vulnerability might feel uncomfortable, but it's where most organizations find themselves today. The question isn't whether you need an AI strategy—how you build one works. You have at least three fundamental paths forward, each with distinct implications for your organization's future.

The Single Point of Failure Approach

The first instinct is often to hire a Chief AI Officer or create an AI department. This sounds rational—it's how we've always handled new initiatives. Find an expert, give them a budget, and let them figure it out. After all, we did this with digital transformation, cybersecurity, and data analytics.

But AI is different. Unlike previous technology adoptions, AI isn't just another tool your IT department can implement and manage. It's a fundamental shift in how work gets done. When every employee will eventually interact with AI agents to automate workflows, gather insights, and make decisions, centralizing AI expertise creates a dangerous bottleneck.

Your marketing team needs to understand how to prompt AI for campaign creation. Your finance team must learn to design agents that reconcile complex data sets. Your customer service representatives will manage AI workflows that handle escalations. This isn't a one-person job—it's an everyone job.

The Sidecar Strategy

The second approach is to create a separate entity—a sidecar organization that handles all AI initiatives. Move the people, technology, and responsibility outside your main organization entirely.

This can be the most brilliant move for large enterprises. Established companies often carry the weight of legacy systems, entrenched processes, and cultural resistance to change. A sidecar entity can move fast, experiment freely, and develop AI capabilities without being constrained by existing bureaucracy.

This approach allows you to recruit AI-native talent, establish new workflows from scratch, and prove value before integrating back into the main organization. Companies like General Motors' Cruise division or JPMorgan's AI research labs have used variations of this strategy successfully.

The risk? You may end up with an AI-powered division that can't effectively integrate with your core business, creating a permanent separation between your traditional operations and your AI capabilities.

The Practical Path: Start Where It Hurts

The third approach is the most pragmatic: identify the tedious work in your organization—the manual processes that exist because of system gaps or off-the-shelf software limitations—and start there.

Every organization has these pain points: the monthly report that requires pulling data from six different systems, the customer onboarding process that involves countless manual handoffs, and the expense approval workflow that somehow takes three weeks despite being "digitized."

Pick one high-impact workflow and use it as your AI laboratory. Don't try to boil the ocean. Instead, engage a small group of employees in designing, implementing, and refining an AI agent that solves a real problem they face daily. If you don't have internal AI capabilities or your resources are stretched thin, please bring in an external team to support your technical journey, but make sure your employees remain actively involved rather than passive observers.

This approach has several advantages. First, you're solving actual business problems, not theoretical ones. Second, you're creating a kernel of AI-literate employees who understand how to define requirements, refine prompts, and manage agent performance. Third, you're building organizational confidence in AI by demonstrating tangible value.

Most importantly, you're teaching your workforce that AI isn't something that happens to them—it's something they actively participate in shaping.

The Choice Is Yours

Your AI strategy doesn't have to be perfect from day one. In fact, it shouldn't be. The organizations that will win with AI are those that start learning now, even imperfectly.

Whether you choose the focused expertise of a dedicated hire, the freedom of a sidecar entity, or the practical approach of starting with your most significant pain points, the key is to start. Because while you're figuring out your strategy, your competitors are already building theirs.

The question isn't whether you know your AI strategy today. The question is: what are you going to do about it tomorrow?