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How will Deep Learning and AI Help Nonprofits Do More Good? Featured

How will Deep Learning and AI Help Nonprofits Do More Good? Photo by Michael D Beckwith on Unsplash

Organizations in all industries are moving towards prescriptive levels of analytics. The enormous progression we’ve seen in Business Intelligence (BI) tools in recent years is helping businesses make more mature decisions, using not only historical data, but current and predictive models to look to the future and steer entrepreneurial developments and decisions.

BI systems powered by artificial intelligence (AI) can transform reams of data into simple, accurate reports, making it easier to steer an organization successfully. AI consumes big data, organizes it and breaks it down into actionable insights nonprofits can rely on for decision-making. The technology can recognize and analyze trends and irregularities from previous projects, and proactively “translate” them into useful future recommendations and strategies.

Because AI’s intelligence comes from its ability to give real-time insights to enhance prescriptive models, organizations are assured that the next decision will be better than the previous. As organizations continue generating more and more data, the power of AI will drive decision-making and program impact in the future. Of course, deep learning and AI won’t magically end poverty and hunger, but it can provide an effective tool for mining data to do more good.

Deep Learning and AI in Practice

If we look at the expectations of AI today, its capabilities offer a logical progression to further enhance what BI already does in new and previously unattainable ways. When applied to BI, AI becomes “deep learning,” a subsection of machine learning that structures algorithms in layers to create an “artificial neural network” that can learn and make intelligent decisions on its own. Essentially, deep learning allows machines to do what comes naturally to humans – learn by example. For deep learning to be truly useful, large amounts of multi-layered data are required and must be linked to other information and data sets to allow the AI to analyze it to learn new things, make predictions, and ultimately propose solutions.

Let’s consider a non-profit organization tracking monetary donations. A BI system could show geographically where donations came from by reviewing donation receipts and creating a virtual map of donor locations. Through exception reporting, which highlights instances in which performance deviated significantly from expectations, it would be immediately evident if a region is falling below planned goals. Using BI, finance teams today can determine where a deviation is coming from and its cause by drilling down by region to identify – and subsequently rectify – regions with donation programs seeing low yields.

This level of analysis will soon be done automatically by BI or corporate performance management (CPM) systems. Finance teams will benefit from not only knowing that there’s a regional problem but also the reason for that problem. For example, BI might quickly tell you that 80% of the deviations come from campaign ABC, where the promised funds have not been received. Furthermore, with access to the correct data, the system could help determine which fundraising tactics were most effective in each geography and make suggestions on a tactical plan for next years’ efforts.

One of the most exciting aspects of BI technology is that analysis is constantly taking place -- whenever new data is collected, deep learning algorithms can automatically check for deviations, conduct the necessary analyses, and proactively flag any issues. This is the future of work, and it’s almost here. Artificial intelligence is improving and accelerating the way we work more than ever before. It isn’t replacing jobs but augmenting them, allowing nonprofits to work more creatively and provide aid to more who are in need.

Matthias Thurner, CTO, Unit4 Prevero

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