In the conversations we have with higher education organizations, there is a recognition that the dawn of a new era in artificial intelligence (AI), automation, and personalization is upon us. Technological progress is widening the gap between the data-driven world we live in and the age-old traditional methods of fundraising.

The recent, rapid advances in artificial intelligence (AI) have created hype that can pose quandaries for advancement leaders and board members who are looking to explore the potential opportunities for their higher education institutions. This blog aims to provide some clarification around different types of AI and how different applications can provide value to higher education advancement shops.

If you work with marketing at a fundraising organization, zero-party data and first-party data can be your best friend. Zero-party and first-party data are the most robust data you can safely obtain and both provide great quality without compromising privacy. 

This Blog was not Witten by ChatGPT

Technology adoption is always a challenge, especially for enterprise organizations. Disrupting a workplace with new technology, particularly when it involves solutions that are frequently used by employees, can leave people feeling confused, or with a lack of direction.

Digitization is the strategic issue of our time and when done right, digital transformation helps connect people, processes, and technology. Digital possibilities and strategy have mutual influence on each other in helping shape overall organizational strategy and digital priorities.

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Tthe nonprofit industry has yet to seize the opportunity that has already been realized by so many private, for profit companies. One of the key differentiators in the data that is available to nonprofits vs for profit companies, is that for profit industries have access to technology that captures data alongside the evolution of the behaviour of their customers. In other words they generate their datasets.

Scoring systems used in nonprofit institutions, while they do work...

In order to make AI easy, you have to limit the types of data that the machine is allowed to consider when making decisions. This works well because many non-profit databases have holes in them, so building something based on realistic data-sets requires the use of fewer types of data.

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