By Christopher Brewer
For-profit business leaders are excited about generative AI’s potential to drive efficiency and improve output. In the for-profit world, investment in the technology is booming, and many business analysts compare AI’s transformative potential to the innovations that sparked the industrial revolution.
On the nonprofit side, the mood is more cautious optimism than feverish excitement. Leaders are aware of AI’s transformative potential, and with high administrative costs and lower fundraising expectations this year, they’re looking for ways to increase efficiency and automate more tasks.
But while for-profit companies are leveraging AI to boost output, nonprofit managers are exploring how the technology can increase impact. Data is the key to creating impact as a nonprofit leader because you can’t manage what you don’t know, and you can’t change what you don’t understand.
AI can help managers address both challenges. Here’s a closer look at some of the ways AI can transform your organization by democratizing data to help your teams manage resources more effectively, expand internal and external collaboration, and operate more efficiently and securely.
Magnifying Impact In Finance and Beyond
For nonprofit finance teams, generative AI can increase financial transparency and improve financial health and monitoring capabilities. One of the most exciting aspects of AI is that its transformative capabilities apply not only to the organization but also to constituent parts. Here’s an overview of several ways AI can improve organizational and individual performance.
- Data democratization drives serendipity: AI pushes information from the center to the edge of the organization, giving people who aren’t finance professionals a way to leverage finance data by asking questions in plain language. A program manager in Tunisia doesn’t need to directly access financial information but can interact with it using natural language and fine-tune questions to gain insight that improves service delivery. This increases opportunities for serendipity, i.e., fortunate discoveries in the data that — when combined with local cultural context — enable users in the field like the program manager to serve constituents better.
- Access to internal and external data demonstrates results: AI lets users interrogate protected internal data as well as external data so they can demonstrate impact in context. For example, say a program that delivers reproductive health services to Syrian refugees reduces pregnancy rates by 35% in that population. Program managers who can query protected internal data plus external public data can demonstrate that their program performs 15% better.
Nonprofit managers are rightly concerned about protecting constituent information with firewalls due to the risk (AI can help with that too — more about that below). But protected access to a combination of public and private data via AI helps organizations demonstrate effectiveness and provides insight to improve impact.
- Insight from frontline users improves planning, analysis and reporting: Today, tasks like scenario planning, forecasting and deep analysis are typically done by sophisticated users who are often located far away from the people the organization serves. Data democratization gives end users in the field these same capabilities. With interactive scenario analysis using information that is accessible at the edge of the organization, frontline users can also add their unique perspectives. This creates a two-way conversation, with data flowing from the center to the edge and back to improve planning, analysis and reporting functions across the entire organization.
- Advanced pattern detection capabilities improve security and reduce fraud: Many nonprofits are data rich but information poor because they lack the tools to detect patterns. By combining centralized data and insights at the edge, AI can detect patterns that humans can’t recognize, flagging potential fraud. AI also enhances transparency by monitoring activities that would otherwise go undetected because they are incidental to the finance or fraud detection team. AI excels at modeling human behavior to detect suspicious activities, allowing teams to address risks quickly to safeguard the organization and protect the people it serves.
- Internal and external collaboration increases efficiency and transparency: AI makes service delivery more efficient by enabling collaboration across internal and external teams. Connecting teams across functional areas enables seamless internal collaboration. And, by tapping into global data networks, AI can help staff members work together to address emerging crises, serving as middleware that supports multiple organizations. One real-world example of this occurred when nonprofits responding to an earthquake in Nepal diverted resources in transit to another continent. AI-driven transparency enabled lifesaving teamwork across multiple organizations.
- Optimizing schedules and professional development boosts employee productivity: AI is already making inroads as a personal assistant, and the impact on employee productivity will be significant as the technology evolves. AI that monitors daily activities, prioritizes key tasks and highlights professional development opportunities can enhance employee performance. Compared to rules-based process automation tools, a generative AI personal assistant is highly capable and personalized, and AI assistants can communicate to facilitate team member collaboration.
Proceeding With Caution: Where To Start
AI isn’t new to nonprofits. Fundraisers already use marketing AI elements like predictive modeling. Like their revenue-driving counterparts in for-profit organizations, fundraising and communications teams are excited about the possibility of crafting individual messages at scale. Nonprofit leaders also see AI’s potential to improve service delivery, which could drive positive change throughout the organization.
Finance teams, while optimistic about AI’s potential, are understandably proceeding with caution because the information they handle is extremely sensitive. Before implementing AI at scale, here are two lower-risk steps for finance teams to get started:
- Map out data governance practices and policies. In finance, data passes through the financial management system, ERP, data links and data warehouses, and the possibility of unauthorized access is not only a risk for the organization but also for the people it serves. For that reason, leaders will need to put policies and procedures in place and deploy sound data governance practices to manage risk before they are confident enough to fully embrace AI.
- Explore security use cases as a point of entry. Using AI-powered tools to protect sensitive data and monitor for fraud risk can deliver immediate value with relatively low effort, helping to gain buy-in for wider AI adoption.
When finance teams follow these steps, they’ll find that better security is only the beginning. Organizations that provide advanced AI tools to employees will also find it easier to recruit the next generation of team members and keep pace with peer organizations that use AI to deliver services and demonstrate results more efficiently. In this way, AI will transform nonprofits by magnifying impact.
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Christopher Brewer is nonprofit strategic industry architect at Unit4 (https://www.unit4.com/)
