It's a mistake to make 'using AI' the strategy itself. Fundamental business drivers like customer lifetime value (LTV), retention, and engagement remain unchanged. AI is a powerful new method for influencing these timeless metrics, but it is not a replacement for a sound business strategy focused on customer value.

For companies adopting AI reactively, governance frameworks are more than risk mitigation. They enforce strategic discipline by requiring clear business objectives, performance metrics, and resource tracking, preventing wasteful spending on duplicative tools and unfocused initiatives.

Treat AI initiatives as two separate strategic pillars. Create one roadmap focused on internal efficiency gains and cost reduction (productivity). Maintain a separate roadmap for developing new, revenue-generating customer experiences (growth). This prevents conflating internal tools with external products.

Demanding a direct, line-item ROI for foundational AI initiatives is like asking for the ROI on Wi-Fi—it's the wrong question. Instead of getting bogged down in impossible calculations, leaders should focus on measuring the business outcomes enabled by the technology, such as innovation speed or new product creation. Obsess on outcomes, not direct financial return.

Creating an "AI initiative" can be a mistake, as it encourages tool usage for its own sake. A better approach is to set the expectation that team members will deliver the best possible outcome, knowing AI exists, shifting the focus from process to high-quality results.

Successful AI strategy development begins by asking executives about their primary business challenges, such as R&D costs or time-to-market. Only after identifying these core problems should AI solutions be mapped to them. This ensures AI initiatives are directly tied to tangible value creation.

Instead of abstract productivity metrics, define your AI goal in terms of concrete headcount avoidance. Sensei's objective is to achieve the output of a 700-person company with half the staff by using AI to bridge the gap. This makes the ROI tangible and aligns AI investment with scalable, capital-efficient growth.

To move beyond FOMO-driven investment, AI21 Labs' CMO advises measuring AI's business impact across three pillars: its ability to scale growth, its power to improve decisions through faster analysis, and its capacity to help organizations avoid and plan for risks.

To prove AI's value, start with a simple spreadsheet for your team to track every use case. Log the tool, intent, and whether it saved time or money. This grassroots data collection reveals trends and quantifies savings, which then informs more intentional, top-down business goals.