Increasingly, technology and business leaders look to AI project managers to make the execution (and success) of their AI projects more predictable. Executives and decision makers want AI projects to mature so they are more like the software development projects that have been with us for a generation. But, any AI project manager hoping to deliver on those expectations knows that success in AI projects requires an end-to-end thinking rarely found today.
A winning approach to an AI project needs to go beyond just thinking about goals and expected outcomes. It requires a holistic approach that encompasses:
Identifying data sources that support algorithms
Adopting the right tools
Implementing quality testing practices
Executing ongoing monitoring and optimization
Software development and AI projects share many similarities. Both have high costs, risks, and promised benefits. Both require finding and securing:
Hard-to-find specialized talent
Expensive, complex infrastructure
Defined bus...
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