AI Readiness for Nonprofits: A New Approach

Is your nonprofit ready for AI tools? Are you?

Community IT President and COO Johanny Torrico developed our Mission-Aligned AI Adoption Model for Nonprofits. In this follow-up, she reflects on how her thinking has changed since, and what it really means to use AI well.

Earlier this year, I developed a Mission-Aligned AI Adoption Model for nonprofits. My thinking was that organizations needed a thoughtful approach to AI, one that considered their mission, values, people, and the risks involved.

I still believe that. But after months of working with nonprofit leaders, listening to their concerns, and working through these same questions inside Community IT, my thinking has evolved.

The challenge isn’t really how to adopt AI. It’s how to make good decisions about it.

Are we ever really ready for AI?

We use the term AI readiness quite a bit, including in our own work at Community IT. Lately, I’ve been questioning what it really means.

Ready for what? To experiment with a tool? To automate a process? To change how people work? And how would we know when an organization is “really” ready?

The term suggests that readiness is something we achieve before moving forward. With AI, I don’t think that’s how it works. Some nonprofits are already experimenting. Others are cautious, and some are deliberately choosing not to adopt it. There is no single starting point or path that works for everyone.

Organizations do need safeguards, an understanding of their risks, and some basic knowledge before introducing certain technologies. But readiness is not something we can check off a list and consider complete.

I’ve come to think of AI as an organizational capability, something we develop over time through learning, experience, and the decisions we make along the way.

An AI readiness assessment can be a valuable starting point, helping organizations understand their current capabilities, risks, and opportunities. But it shouldn’t be treated as a pass/fail exercise or a one-time determination that an organization is ready. The greater value is in helping organizations build the judgment to make good choices as technology and their needs evolve.

And sometimes, the right decision is not to use AI at all.

Choosing not to use AI is a decision you keep making

Even organizations that have decided against AI are encountering it. AI features are arriving in tools nonprofits already use, often through routine updates rather than a formal decision: assistants built into Microsoft 365 and Google Workspace, automatic meeting summaries in Zoom and Teams, and new AI features in CRMs and donor databases.

That means every organization, whatever its stance, needs to know where AI is showing up, what data it can see, and what staff are allowed to do with it. A clear AI acceptable use policy is a good place to start.

Just because we can automate something, should we?

AI offers real opportunities to automate work, save time, and help people accomplish more with limited resources. For nonprofits, that potential is meaningful.

But what happens when we automate a process we don’t fully understand?

A process may look straightforward, yet the person performing it often contributes more than the documented steps suggest. They recognize exceptions, make judgment calls, understand the people involved, and notice when something doesn’t seem right. Some of that knowledge has never been written down.

One example we’re exploring internally is using AI to help with ticket triage. At first, this may seem like a straightforward process to automate. But our Service Coordinators bring knowledge that isn’t always captured in a ticket: the client’s environment, the people involved, their expectations, and when a routine issue may have been creating frustration. They make judgment calls based on that understanding and context that an AI tool might miss.

AI may eventually learn to recognize some of these situations, but we need to be deliberate about how we train it, what decisions we allow it to make, and how we review those decisions, particularly in the early stages.

If we automate without understanding those contributions, we could make an inefficient process run faster. Or worse, we could introduce problems that are harder to recognize and untangle, because the person who used to catch them is no longer involved.

We also need to think carefully about how we define success.

If automation saves several hours of work, that’s valuable. But what if the experience for the people we serve doesn’t improve? What if we lose something important in the process?

It matters for staff, too. People notice when their work is being looked at for automation, and how leaders handle those decisions shapes trust. Involving the people who do the work from the start is more respectful, and it’s also the best way to surface what the documentation misses.

I’m not suggesting we avoid automation. There are tremendous opportunities. But before we automate, we should understand what we’re trying to improve, how the work happens, and what needs to remain in human hands.

Efficiency matters. So do judgment, relationships, and the quality of the work.

Questions to ask before you automate anything

These are the questions we’re asking, with our clients and within Community IT:

What problem are we solving?
What difference will this make, and for whom?
What knowledge or judgment does the person doing this work bring that may not be captured in the documented process?
What will it require in time, budget, training, and oversight?
What might we need to stop doing to make room for it?

If you don’t have the capacity to examine every process, start with one. Spend an hour with the person who does that work before you change anything.

AI can amplify what already exists

One thing I’m learning through conversations with nonprofit leaders is that questions about AI often uncover broader organizational challenges. How is information managed? Who makes decisions? Are processes understood consistently? What happens when something doesn’t go as expected?

These questions existed long before AI. But AI can amplify both what works well and what doesn’t.

For nonprofits with limited resources, this creates a challenge. Many don’t have the capacity or budget to examine every organizational process before considering AI.

Examining organizational processes is where technology partners can help.

Through ongoing relationships, we get to know our clients, their people, their systems, and the challenges they face. That doesn’t make us experts in how every organization should operate. But it gives us the opportunity to ask better questions, help clients recognize potential issues, and understand where technology can make a meaningful difference.

We’re asking these questions within Community IT, too. There are many things we could do with AI, but our resources and attention are limited as well. For example, as we evaluate different AI opportunities internally, we’re learning to look beyond whether something can be automated and ask whether the expected improvement justifies the time, investment, and attention required. Sometimes that means being willing to put an interesting idea aside, at least for now.

AI capability is more than knowing how to use the technology. It’s developing the judgment to recognize opportunities, understand tradeoffs, and make deliberate choices. Sometimes the most valuable contribution isn’t implementing a solution. It’s helping an organization better understand the problem.

What I would change about the original model

The foundation of the Mission-Aligned AI Adoption Model still holds: AI decisions should be grounded in an organization’s mission and values. What has changed is my understanding of what it takes to put that principle into practice. If I were revising the model today, I would make three changes.

The stages are a cycle, not a path. The original model noted that its three stages weren’t purely sequential. In practice, the work is even less linear than I expected. Each new tool or use case brings an organization back through the same questions. Repeating these questions with each new use case helps organizations develop stronger decision-making capabilities over time.
“Whether” comes before “how.” The original model focused on adopting AI well. I would now add an explicit first question for every use case: should we use AI here at all?
Success means more than time saved. The original model measured outcomes like time saved and capacity gained. I would add the experience of the people we serve and the quality of the judgment that stays in the process.

What hasn’t changed

At Community IT, our purpose is to help nonprofits accomplish their missions through the effective use of technology. That doesn’t mean recommending technology in every situation. Putting that into practice takes more listening, more curiosity, and sometimes a willingness to question whether technology is the right answer at all.

I expect my perspective will keep evolving as we gain more experience. For now, I’m less interested in whether an organization is ready for AI and more interested in whether it is developing the ability to make thoughtful decisions about it.

If we help a client recognize that a technology solution isn’t needed, or that something else needs attention first, I consider that a successful outcome.

Continue the conversation

Want to hear these questions debated? Join us on November 18 for Should Your Nonprofit Use AI? a panel discussion with Community IT’s Steve Longenecker, Nura Aboki, and Carolyn Woodard on the choices nonprofits are facing about AI, including the choice not to use it. After the webinar has passed you can view the video or read the transcript at that link above.

Related resources:

If your organization is weighing decisions about AI and could use a thinking partner, we’d be glad to talk.

Author:

Johanny Torrico brings over thirty years of experience managing teams and operations to her role as President and Chief Operating Officer at Community IT, where she leads the largest internal team providing services to clients. A calm and organized leader, Johanny is responsible for the service and technical operations for all the teams at Community IT. She also leads staff development and internal business processes, with a focus on staff retention and career mentoring.

As Chief Operating Officer, Johanny played a critical role in leading the dramatic expansion of our service operations. She established new teams, expanded company management and led the successful adoption of a wide range of new technologies. Johanny has a special ability to promote standardization of our services in ways that add value for our clients. Johanny brings decades of experience, professional maturity and tremendous skillsets as a business leader.

During her tenure at Community IT, Johanny has mastered every role she has taken on including network administrator, network engineer, and service manager. She still enjoys providing technical support to our clients, participating in our professional services team, and implementing technical solutions. She draws on her long experience interacting with nonprofit clients in various roles to understand how best to partner with our community.

Prior to joining Community IT, Johanny served as Director of Technology for The National Association of People with AIDS (NAPWA) for nearly four years. Previously, she worked for 11 years at Whole Foods Market, where she was the Facility Leader for their food plant in Landover. Through these management experiences Johanny gained extensive leadership, planning, management, and customer service skills.

Johanny holds a B.S. in Computer Information Systems. She is a VMWare Certified Professional and a Microsoft Certified IT Professional for Office 365.

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As advocates for using technology to work smarter, we’re practicing what we recommend. This article was drafted with the assistance of AI, but the content was reviewed, edited, and finalized by a human editor to ensure accuracy and relevance.

This is one piece of a larger picture. Explore our AI for Nonprofits guide for the full context.

Photo by Mehdi Sepehri on Unsplash