Hey there,

AI is becoming a big deal in the business world, but leaders still struggle with aligning data science projects with business goals. Too often this leads to technical teams solving the wrong problems, wasting resources, and missing opportunities. As a result, leaders are unable to see the value of their AI investments.

The solution is for AI-driven business leaders to provide clear direction on what concrete, measurable business metrics need to be addressed by their AI and data science teams. In other words, how should technical teams measure success? Typically, 2-3 metrics that, when improved, would result in clear benefits to the business can be identified.

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For example, in customer service AI projects, key metrics may include the level of customer satisfaction or resolution time. For predictive maintenance, metrics may include machine downtime or time to fix a machine. These metrics should be sourced directly from the business unit or function leads because they are part of evaluating their performance.

It’s important to define key business metrics at the outset of the project, ideally before any significant coding is done. Waiting until later in the project lifecycle to define these metrics can lead to wasted resources and missed opportunities.
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What challenges have you faced in aligning business metrics for AI and machine learning projects? Let's start a conversation on how we can improve this critical aspect of AI-driven business leadership.
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Questions? Comments? Just hit reply.

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Yours,

Manny
​bigplasma.ai | LinkedIn​
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P.S. Have you seen my latest free training ”How Legacy Midsize Manufacturers Generate NEW Revenue with AI”? You can get instant access HERE. Would love your feedback? What’s unclear? What would you like me to add?