As your organization begins to double down on a potentially impactful AI-based solution, having a strong level of data readiness becomes critical to applying AI in an effective, impactful, and responsible way. Having good data readiness is important for any effective application of AI, both custom and off-the-shelf, but if you’re working towards a custom tool, it will be especially critical. Data readiness means thinking about what data exists and what is still needed, the quality of that data, and what infrastructure needs to be in place to support a safe and successful implementation.

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Learn through our self-paced group online course

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Read a case study

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Let us know if you’re interested in a live session

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Additional Resources

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Other Exploration Modules

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Problem Definition

How can we ensure that we're pursuing solutions that are deeply rooted in the problem at hand, taking a people-first rather than a tech-first approach?

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Build Vs. Buy

How do we navigate the decision to build something custom vs. leverage an off-the-shelf solution?

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AI Project Planning

From budgeting to building the right team to assessing potential risks, how do we go about planning an AI project?

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About PJMF

The Patrick J. McGovern Foundation (PJMF) is a global, 21st century philanthropy committed to bridging the frontiers of artificial intelligence, data science, and social impact.

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