Empowering Nonprofit Organizations with AI and Large Language Models: Make AI work for your nonprofit

Guest Author: Chris Howard, advanced technology services manager, Morefield

Artificial intelligence and large language models (LLMs) are proving their power to transform workplaces by automating routine tasks, providing data-driven insights, and creating personalized content.

AI can also streamline processes so nonprofits can better serve people.

Applying AI in the workplace is as essential today as learning to use search engines was in the past. Instead of displacing jobs, AI can keep talent by reducing repetitive tasks that sap morale.

Operational efficiency

AI can save time on essential but time-consuming tasks, including:

Manage emails: Automation can organize the emails flooding nonprofit mailboxes daily. The finance director who processes invoices can set a program to watch for certain subject lines, open the mail, save and rename the document, and move it to the action folder.

Taking automation one step further, AI can open the PDF, discover the amounts due and their due dates, and put them into the accounting package. By running automatically, AI reduces the risk of important emails getting lost in the daily deluge.

Schedule meetings: With tools commonly available from Microsoft and Google, a “Book time with me” button at the bottom of the sender’s email lets the reader do just that. The feature can be programmed to accept meetings at certain times and leave designated breaks between them.

Streamline communications: For nonprofits working with clients and vendors, AI can conduct “sentiment analysis,” reviewing the tone and emotional context of requests for service to see if the sender is happy or agitated. Based on its reading, the AI can determine whether a request should rise to the top of the pile.

If done correctly, chatbots on an organizational website can save staff time by handling many inquiries at a time and providing personalized responses. However, a chatbot can legally be considered to be acting in the interests and at the behest of the company, so any advice it gives must be carefully programmed to align with company policies.

LLMs also boost communications through the power to summarize large amounts of text. They can’t replace a thoughtful reading by a human, but they can provide a refresher when needed at a moment’s notice. In a world of virtual meetings, LLMs can neatly sum them up and assign to-dos to help ensure follow-through.

Data management

AI can tame data management, saving time and resources through its ability to ingest raw info and provide summaries. To keep data safe, avoid using free AI and customize a system to the organization’s needs instead.

AI can analyze data and insights to spot trends. It can analyze donor data to tailor and personalize communication and engagement strategies and enhance and automate the organization’s reporting and compliance—but again, avoid using free AI. Paid models keep sensitive information in-house, under your control, and inaccessible to outsiders.

Enhancing program delivery

AI can enhance program delivery, design, and modifications for nonprofits through personalized outreach. It does the job by helping to draft personalized emails, send follow-up reminders, collect and analyze feedback, and draft initial images and text for social media that you can—and should—revise with your organization’s voice and flair.

Ethical and risk considerations

The same high ethical standards that nonprofits adhere to daily still apply to AI.

Responsible AI applications start with using AI models that are regularly audited and trained only on diverse and representative datasets. Never let AI post without your consent, and always read the results to make sure the statements reflect your values and policies.

Mitigate risks by implementing robust protection measures and ensuring the data is used ethically and with consent. Don’t give AI complete autonomy; keep humans in the loop and empower them to override AI decisions when needed.

AI and automation are powerful tools designed to assist and enhance work by handling repetitive and mundane tasks, however they still need human supervision.

AI for Nonprofits: Risks, Benefits, and Best Practices

Guest Author: Chris Howard, advanced technology services manager, Morefield

 

Artificial intelligence (AI) and large language models (LLMs) are transforming workplaces, including nonprofit organizations, by automating routine tasks, providing data-driven insights, and creating personalized content. However, while AI offers significant rewards, it also brings certain risks that must be managed carefully.

Benefits of Using AI for Nonprofits

• Operational Efficiency: AI can save time on essential but time-consuming tasks such as managing emails, scheduling meetings, and streamlining communications.
• Improved Data Management: AI can tame data management by ingesting raw information and providing summaries, spotting trends, and enhancing reporting and compliance.
• Enhanced Program Delivery: AI can improve program design and delivery through personalized outreach, drafting emails, sending follow-up reminders, analyzing feedback, and creating social media content.

Risks of Using AI for Nonprofits

• Data Privacy and Security: Using free AI tools can compromise sensitive information. It is essential to customize AI systems to maintain control and ensure data privacy.
• Ethical Concerns: AI models must be audited regularly and trained on diverse datasets to avoid bias. AI should post content only with human consent and oversight.
• Potential Misalignment with Values: AI-generated content must reflect the nonprofit's values and policies, requiring careful review and supervision.

Best Practices for Using AI in Nonprofits

• Use Paid Models: Avoid free AI tools and opt for paid models to keep sensitive information secure.
• Implement Robust Protection Measures: Ensure the data is used ethically and with consent, and maintain human oversight to override AI decisions when necessary.
• Regular Audits: Routinely audit AI models to ensure they are trained on diverse datasets and perform ethically.
• Human Supervision: AI should assist and enhance work by handling repetitive tasks, but it should always be supervised by humans to ensure alignment with the organization’s values.