Strategy For and With AI

Executives intent on exploiting AI to enhance processes or products tend to focus on having a strategy for AI. But creating strategy with AI can matter as much or even more. What does strategy with AI mean? Like any corporate strategy, it expresses what enterprise leaders deliberately seek to emphasize over a given time frame. It articulates how and why the organization expects to succeed in its chosen market. These aspirations might involve, for example, superior customer experience and satisfaction, increased growth or profitability, greater market share, or agile fast-followership. Whatever the specific strategy, virtually all organizations create corresponding measures to characterize and communicate desirable strategic outcomes. In a machine learning era, enterprise strategy is defined by the key performance indicators (KPIs) leaders choose to optimize. Those are the measures organizations use to create value, accountability, and competitive advantage. AI can help determine what KPIs are measured, how they are measured, and how best to prioritize them. Indeed, world-class organizations can no longer meaningfully discuss optimizing strategic KPIs without embracing machine learning capabilities. Because metrics drive strategy, determining the optimal "metrics mix" for key enterprise stakeholders becomes an executive imperative. Achieving KPI outcomes (and suggesting new KPIs) is what smart machines must do - and must learn to do. Of course, AI and machine learning are both a means to an end. The true strategic opportunity of these technologies is the chance to rethink and redefine how the enterprise optimizes value for itself and its customers.