The Oxford English Dictionary defines artificial intelligence as the “theory and development of computer systems able to perform tasks normally requiring human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages.”
According to the Brookings Institute, nonprofit organizations are showing a surging interest in tapping into the capabilities of not only artificial intelligence (AI), but also machine learning (ML) and data analytics. For a host of reasons, the time is now for nonprofits to leverage the potential and promise of AI-driven software. Contributing to this growing acceptance is more than two decades of astonishingly deep and comprehensive data sets collected by nonprofits.
With this wealth of data, Peter Drucker’s maxim, “What gets measured, gets managed,” has never been more apropos for nonprofits as 2020 nears.
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If nonprofit demand is poised to ignite a revolution in the adoption and use of AI in back-office applications, the uprising will begin with the following three “skirmishes” – immediate and current impacts of AI on nonprofit administration:
Routine Administrative Tasks
AI-based Chatbots, which we’ve all encountered, automate conversations for commonly asked questions through text messaging or telephone. A chatbot is a type of software that produces intelligent, automated responses to common questions in order to hold a “conversation” with a user. It stands to follow then, that AI algorithms can enable efficient and effective communications with both internal and external audiences.
Chatbots can help with customer service and routine requests, such as how to contribute money, address a budget question, or learn about upcoming programs. They can manage first-line support queries and subsequently direct those queries to human personnel as needed. Chatbots can even schedule appointments.
In addition, AI can automate repetitive tasks, reducing the risk of human inputting errors, accelerating accurate data collection and ensuring an organization’s donor outreach is seamless and timely.
Schedule and reschedule meetings, send out briefings, set reminders – AI is primed to handle these types of routine obligations and applications already exist to manage these tasks. A message to schedule, postpone, or cancel a meeting is sent to an office bot, via SMS or other software-enabled tool, and the bot first scans a person’s calendar before scheduling the meeting. Then, it automatically sends alerts to involved parties. AI completes the task, saving time, labor and flaws of human involvement.
Afterward, it can automatically send meeting minutes to all involved parties, arrange introductions among individuals, and even book travel. That’s pretty handy and supremely efficient.
AI-driven solutions can help organizations advertise, screen, and hire prospective staff members.
Once managers have decided what qualities a candidate should possess for consideration, AI can match applicants with recruiters. Bots have already been designed to pre-screen resumes, check for a candidate’s relevant experience and skills and identify applicants who are best suited for roles and organizations.
These duties, traditionally performed by humans, take concentrated amounts of time and effort. In the past, candidates have also been selected – or rejected – based on human subjectivity. But consider: AI can assess a candidate even more effectively than a human counterpart – and a bot is not saddled with emotional “noise.”
Bots have also provided recruiters with sets of interview questions based upon the recruited role. AI-software has even been used to on-board new hires with a chatbot answering “newbie” questions. These tasks can be transferred to the nonprofit realm, cutting costs, accelerating processes, streamlining workflows and lowering costs
Those aforementioned data sets aggregated from donors and supporters include previous individual donation amounts and patterns, event attendance records and wealth amount. If it can be measured, can it be managed?
The cognitive insight intrinsic to AI parses the data for fundraisers. Management of that data comes by means of optimizing donor acquisition efforts, proposing logical and subsequent engagement steps and levels, and bulk generating “personalized” messages.
Management also can include fostering stronger relationships with an organization’s constituents, while disclosing opportunities to generate more revenue. AI nurtures donor journeys by personalizing that path with tailored, personal messages based on actual, real-time donor behavior.
It’s no surprise that AI can perform the repetitive and routine tasks that each gift officer attends to daily. Such routine duties include running reports, analyzing data to select the most important donors, conducting background research, and suggesting purposeful, personalized messages at the right time that encourage donors to contribute to an organization.
The upshot? More and better qualified leads coming in to the organization that should increase donations.
It can also predict the who and when of the next big donor based on earlier engagements. A caveat to this delicate management of the tool must be mentioned: AI systems should be designed so that their goals, behaviors, decisions and recommendations emulate human values and mores.
AI bots can scan datasets of donors and identify top contributors each day. This swift, automatic identification lends itself to extended, tailored communication that ultimately can double or triple the amount of daily outreach.
AI: Value > Cost
In conjunction with machine learning and data analytics, AI-driven solutions can help generate revenue, control costs, manage administrative operations, and automate routine tasks within a nonprofit. The value of this automation and operational efficiency exceeds the cost of acquisition.
Ultimately, AI will help organizations improve their decision-making process by deriving precise and actionable insights from copious amounts of aggregated data. It will boost constituent engagement through customized and intelligent processes and services and allow an organization to grow on a greater scale, through the automation of labor-intensive tasks that have long been performed manually. A prime example of an AI-driven approach to nonprofit analytics is Blackbaud’s Intelligence for Good.
Once AI has been embraced and integrated into the software to help nonprofits, Drucker’s maxim will need to be updated: “What AI measures, AI manages” – with a little human input and to an organization’s gain.
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