3.1. Developing AI Strategies Aligned with Business Objectives
💡 First Principle: An AI strategy is a portfolio of bets, not a technology roadmap. Each bet should trace to a business objective, be sourced the cheapest reliable way, and be reviewed regularly against evidence, with the discipline to scale winners, pause the uncertain, and stop the rest. Strategy is as much about what you decline to do as what you fund.
Why care? Because AI budgets rarely fail from a lack of ideas. They fail from too many disconnected ideas, built expensively, kept alive by sunk cost. Executives remember the outcome of the portfolio, not the elegance of individual models.
The best mental model is a venture capital portfolio. A good investor screens many opportunities against clear criteria, invests in stages (small checks first, larger checks after evidence), decides when to build a company versus acquire one, and writes off investments that are not working without emotion. Each AI initiative is a portfolio company with milestones, and the strategist is the investment committee.
⚠️ Common Misconception: "Building custom is the default route to advantage," "a successful POC should always scale," "if AI can do it, AI should do it," and "moving to AI or between AI platforms is a simple swap." Each belief skips a decision gate: sourcing, prioritization, appropriateness, or transition planning.
