From the course: AI Strategy Foundations for Data Scientists and Team Leaders
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Setting and measuring impact
From the course: AI Strategy Foundations for Data Scientists and Team Leaders
Setting and measuring impact
Think of a time you had to build an AI project. You delivered, then talked about your solution only to be met with silence. The stakeholders knew you did something, just not the impact. What went wrong? You may have set and measured impact using only technical metrics. There's a better way. Here's how to avoid those awkward silences. Define a benchmark: You need this to measure the impact of success. Define urgency and importance: You need this to create quality benchmarks. Urgency and importance affect the minimum viable solution you make. They also define the adaptability of an AI solution and its tied to AI strategy. Use business benchmarks first: Start with ones tied to revenue or cost savings. Think of what stakeholders are specifically incentivized by. For example, Rec system benchmarks would focus on increased sales or decreased cart abandonment. Use technical benchmarks last: Do this after defining business ones. Focus on technical benchmarks for your data models and the…
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