A great number of AI use cases can be achieved with off-the-shelf AI tools, some of which may already be built into the software your organization uses on the day-to-day. Building more custom AI solutions is a big undertaking — and not a one and done undertaking at that; like any digital system, they require maintenance. Whenever possible, organizations should explore how to leverage what already exists, only embarking on a custom solution when tackling something truly unique to your organization and its mission. This module will shed light on how to make that decision between building vs. buying an off-the-shelf solution.
PJMF has not developed content for this module yet, but let us know you’re interested, and we’ll keep you in the loop if/when it’s released.
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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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How do we think about the data we have or still need, the quality of that data, and the infrastructure required to support a safe and successful implementation?
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From budgeting to building the right team to assessing potential risks, how do we go about planning an AI project?
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With an impactful solution in mind and an AI project plan in hand, how do we craft a strong grant proposal to secure the necessary funding?
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The Patrick J. McGovern Foundation (PJMF) is a philanthropic organization dedicated to advancing artificial intelligence and data science solutions to create a thriving, equitable, and sustainable future for all. PJMF works in partnership with public, private, and social institutions to drive progress on our most pressing challenges, including digital health, climate change, broad digital access, and data maturity in the social sector.
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