Every time I open the Gmail app on my phone, artificial intelligence (AI) is at work. When a friend emails me to find out if I’d like to have lunch with him next week, Gmail offers reactive AI responses like, “Yes,” “No,” or “Sure!”  Gmail’s AI is helping me respond to email quickly, based on the words in the email. And, that’s awesome.

What if the next time I opened the Gmail app, however, there was an email written for me (instead of to me) suggesting that because it’s been a while since I’ve seen my friend, I should invite him to lunch next week, and it looks like there’s an opening in my calendar on a certain time and day. Imagine if AI took that one step further and used past emails to craft the first draft of a future email, in my voice, on my behalf inviting my friend to lunch at a specific time and a specific location, both convenient for my schedule. Wouldn’t that be magical? We think so, and the true magic is in the idea of proactive versus reactive artificial intelligence. This is going to change the world.

To compare proactive versus reactive artificial intelligence, I think about what it’s like for me to have an annual checkup with my doctor. She always tests me for so-called “bad” cholesterol, because my family has a history of problems there. I’m glad my doctor is vigilant, but her approach is essentially reactive. She is only going to tell me if something is wrong and then, hopefully, she’ll tell me what to do about it. But what if the damage has already been done?

Wouldn’t it be better if she consistently reminded me of this concern and educated me about what I can do now to keep the good cholesterol high and the bad numbers low? Maybe this proactive approach would also allow her to be proactive about other areas of diagnosis and treatment.

Proactive AI is like the doctor I would like to have: looking ahead, prescribing the future, and letting us know how we can shape it.

In fact, a proactive system redefines what AI means: instead of “artificial intelligence,” we think of AI as “actionable intelligence,” which is really what we want as we strive to drive action from data. What would this mean for fundraisers who no longer have to choose which donor to be in touch with or copy and paste email templates? What could this mean for sales professionals who constantly struggle to personalize their communication with their most important leads?

Consider this definition of actionable intelligence: Actionable intelligence is information that can be acted upon, with the further implication that actions should be taken.

Expanding on the definition just slightly, we can say that actionable intelligence not only presents information that can and should be acted upon, but it also tells you what those actions should be, and might even perform some of them for you.

The problem isn’t that the human brain can’t set priorities and make decisions; we do it all the time, every day. The challenge is the amount of data we now have available to us and how long it takes to sift through those terabytes and create relevance from it. Proactive AI can do that for us 24/7, never getting tired and never asking for a coffee break. Its feelings aren’t hurt when we reject or modify its findings. Instead, its machine learning algorithms adjust, so the information gets better and better. It’s your brain on … well, you get the picture.

As a fundraiser, this expanded definition of AI opens new doors of opportunity. Isn’t your day all about priorities and doesn’t your success depend on making the right decisions in response to those priorities?

About the Author

Adam Martel is CEO of Abila Product Partner, Gravyty. Through its technology, Gravyty helps nonprofits retain donors, onboard new fundraisers faster, and increase donations.