After leaders recognize the first mistake with AI agents - treating them as scattered efficiency tools, the next question becomes unavoidable: Where should we go deep first ? This is the most consequential decision in any AI agent strategy, and it is where many organizations quietly derail progress. Too often, the answer is driven by convenience or enthusiasm rather than strategy. Leaders choose areas that are easy to automate, politically safe, or already understaffed. While that may generate short-term relief, it rarely produces durable advantage. The right starting point is not where AI is easiest to deploy, but where it can change outcomes that truly matter to the business.

Not Every Bet Is Worth Making‍

The strongest candidates for deep AI agent deployment share a few defining traits. They sit close to the core of value creation - revenue growth, customer experience, speed to market, or risk management. Work in these areas is highly interconnected, meaning decisions, data, and execution feed into one another rather than operating in isolation. They also tend to be domains where your organization already has strengths: proprietary data, hard-earned expertise, scale, or well-developed processes. AI agents amplify what is already distinctive. They struggle to create advantage where none exists. This is why successful companies often focus on areas like marketing, product development, underwriting, or design services before tackling generic back-office tasks.

Choosing where to go deep also requires honesty about how work actually happens today. Leaders must look beyond org charts and job titles and examine tasks, decisions, and handoffs in detail. Where are people spending time on repetitive work that constrains higher-value thinking? Where do delays, rework, or inconsistent decisions erode performance? These friction points are often invisible at the executive level, yet they are precisely where AI agents can unlock compounding gains. When agents are embedded across a chain of related activities—rather than bolted onto a single step—they enable faster feedback, better decisions, and continuous improvement at a scale human teams cannot sustain alone.

This choice is uncomfortable because it forces focus. Going deep means saying no to dozens of tempting use cases and committing leadership attention to one domain long enough for real transformation to occur. It also means accepting that meaningful returns may not appear immediately, especially if growth—not just cost reduction—is the goal. But this is the tradeoff that separates activity from advantage.

Where Focus Becomes Advantage‍

Leaders who choose wisely, create a proving ground where their organization learns how to redesign work around AI agents, how to build trust, and how to govern new forms of execution. In the next phase, those lessons become transferable. The question is not whether your organization should adopt AI agents - it is whether you are prepared to place a deliberate, strategic bet on where they can matter most.

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