From Curiosity to Culture: A Framework for Communications and Fundraising Collaboration: PLAN™

By Bryan Casler

Like many of you, I have been grappling with how to make sense of artificial intelligence (AI). Not in the abstract, not in some future-scenario workshop, but right now in our day-to-day lives and work.

We keep hearing the same thing from peers at nonprofits and the agencies that support them — a sense of uncertainty, a bit of fear and one big question: How do we adopt this responsibly, and where do we even start?

Some teams have experimented with ChatGPT and used AI to write email subject lines or draft campaign copy, but deeper understanding and adoption is lagging. Others are hesitant, waiting for a formal policy or a perfect use case to emerge. But here’s the truth: No policy will make your team comfortable with AI. No deck will demystify it. We’re on the frontier now. As leaders, we’re not waiting for a map, we’re drawing it.

Why This Matters Now

AI is powerful, potentially dangerous, and fundamentally transformative. To harness it effectively, we have to understand it. Not abstractly, but intimately. The team that waits for permission to explore how AI can be applied to their work — or those who are using it in the dark — will get burned. The sooner your team starts to use it out in the open, the sooner your organization will understand the opportunities, the risks and the ways to shape it to your organization’s values. No one becomes AI literate by reading a policy. They learn by doing.

From Curiosity to Integration

At 4Site Studios, we didn’t mandate AI adoption to build that culture. Instead, we created space for experimentation. More than a year ago, I offered to personally reimburse any staff member up to $100/month for them to use any AI tools. No questions asked and no requirements. Over time, we began sharing our best tips, hard lessons learned and questions with which we were still grappling. This is the reason our sarcastically named “#ai-underlords” Slack channel has become our most active.

Together, we discovered which models gave the best results, as well as which prompt engineering was helpful and which was AI theater. We developed a collective AI literacy not from taking courses, but from achieving many small victories and frustrating failures, together and in the open.

One nonprofit that focuses on ending the drug addiction crisis in America offers memorial pages that anyone can create in memory of a loved one. Unfortunately, some of the submissions are not in good faith. Due to the nature of the subject matter, we can’t flag submissions for moderation based on keyword analysis alone, but with AI, we can perform “sentiment analysis” to help flag submissions that likely straddle or have crossed the line.

Our inclusive and collaborative culture allowed for and promoted curiosity, and that’s where the real transformation started. We are now able to effectively utilize AI in our daily workflows and even in our client strategies and web development. It’s allowing us to build solutions and find efficiencies that were previously impossible.

Culture First, Policy Second

Let me be clear: I’m not against acceptable use policies or AI governance. In fact, as organizations scale their use of AI, policies will be essential. In July, we formalized our own. A good AI policy encourages experimentation by clearly showing what’s allowed, not just what’s off-limits.

But I also believe that when it comes to navigating this urgent threat and the bountiful opportunity of AI: Culture precedes policy. If your team doesn’t feel comfortable using AI (or sharing that they’re using AI), a policy won’t change that. If there’s fear, shame or a desire for perfectionism around these tools, adoption will be superficial and inadequate.

My team was able to build AI literacy because we were allowed to be vulnerable with each other and fail. No one was penalized for experimenting. No one was shamed for sharing imperfect outputs (though, there have been good-natured ribbings, like when a colleague’s email begins with the AI-tell, “I hope this email finds you well ...” or has five em-dashes). We have all been learning to ride this bike together and have the collective scraped knees and elbows to prove it.

If you’re a leader thinking about AI, ask yourself this: Have I created a space where my team can play? Do they know that experimentation is safe, and curiosity is encouraged? If not, that’s the first policy you need to implement, and it might need to start with you taking the first step.

The Four-Stage Model for Authentic AI Adoption:

At 4Site Studios, our team has adopted this framework:

1. Onboarding. Get to know the tools. Think of AI as a curious intern — eager, quick and sometimes confidently wrong. Use it for basic drafting, summarizing or brainstorming.
2. Probation. Start giving it more responsibility. Try it on tasks that matter and push beyond what you think it might be capable of. Put it into your creative process, your code base or your research. See where it shines and where it stumbles.
3. Co-pilot mode. You’re now collaborating. You know its strengths and weaknesses. You work with it the way you’d work with a brilliant colleague who’s fast, occasionally wrong, but incredibly helpful when you can guide them.
4. Integration. This is where the brain rewiring happens. You stop asking, “Should I use AI for this?” and instead assume it will be part of your process. It becomes second nature, like copy/paste, spell check or going to a search engine to find what you’re looking for.

At this final stage, AI isn’t just a productivity booster. It becomes a creative enabler, helping your team stretch into new capabilities, new offerings and new ways of thinking.

The Leadership Opportunity

If you’re wondering what to do next, here’s my advice: Start using AI yourself, and do it in the open. Share your prompts. Talk about your failures. Show your team that it’s not about perfection. It’s about curiosity and exploration. Then invite them to do the same.

If the nonprofits in our sector are going to take advantage of the promise of AI and mitigate its dangers, nonprofit leaders will need to model curiosity, transparency and responsibility. That’s how you normalize adoption. That’s how you build trust. And that’s how you build real organizational literacy. Not because someone asked for it, but because everyone started living it.

So yes, build your policy, but build your culture first. Model the behavior. Use AI in the open. Encourage play. Make curiosity the standard. That’s how trust grows, and how real literacy takes root.

Bryan Casler is the vice president of digital strategy for 4Site Studios. He helps social-impact organizations harness their digital platforms to boost fundraising and drive advocacy. Increasingly, he leverages artificial intelligence to optimize supporter engagement.

From TSA to Seurat: Understanding the Critical Role of Context in Data

By SB Birnie

On several separate occasions, my bag was pulled off the conveyor belt going through airport security — once for my cuff links, once for a tiny tub of Play-Doh and once for a small umbrella in a case. What do you think the Transportation Security Administration (TSA) workers saw on their monitors? What was their context for searching my bag?

The clasp of the cuff links was thought to be a knife. The actual context: I was on my way to a fancy gala. Cuff links are fancy. There you go!

The tub of Play-Doh? C-4 explosives, apparently. The actual context: It was my nephew’s birthday. He’s a kid. He likes Play-Doh. Happy birthday (or not, since I had to throw it out)!

The collapsible umbrella? I actually have no idea about that. But I bet you already guessed the context: It was raining at my destination. Mystery solved!

The point of this story: Without thinking critically about context, you won’t know the full picture of what’s happening. This is especially important when it comes to data.

Without the right context, there’s a good chance you won’t make smart decisions for your organization’s future.

Data Equals What and People Equal Why

We know that data, visualizations or AI-generated reporting can tell you what happened, but it takes a human to investigate the why.

Was there an increase in direct marketing investment to cause that spike in revenue? Or maybe acquisition doubled or your organization launched digital advertising? Was this an industry-wide trend? Were there a couple of major gifts in there skewing the numbers?

Looking at several layers of data — like donors and revenue per donor rather than just revenue alone — and factoring in outside context will paint a clearer picture of what happened and why. Tell the full, honest story with your data.

Say No to Junky Charts and Chartjunk

A good chart visualizes data in a way that’s easy to understand.

This is how you go from a bad chart to a good chart. Excel’s recommended charts start the axis at 32% instead of 0%. Don’t do this! Start at 0% for a truer comparison.

It also includes gridlines and more, called “chartjunk.” This is a concept, coined by Edward Tufte, that refers to unnecessary, distracting elements that make you stare at a chart for too long trying to understand it.

Get rid of anything you don’t need to comprehend the data — gridlines, redundant data labels, extra text, etc. Ensure your data labels are consistent. And don’t overcomplicate your visuals.

Be Like Cameron and the Seurat Painting

Remember that part from “Ferris Bueller’s Day Off” where one of the film’s main characters, Cameron, stands in front of a George Seurat painting, “A Sunday Afternoon on the Island of La Grande Jatte”?

He stares intently at the painting, and the camera zooms in closer and closer, revealing the people in the painting are made up of teeny tiny dots. Cameron’s having a moment. It’s a great scene.

Like Seurat’s pointillist masterpiece, data viewed up close may appear chaotic or indecipherable. But when you step back — and apply the right context — the full picture emerges.

If you aren’t an art person, think of your data like Lego bricks. At first, it’s a pile of scattered pieces. Sort them to start understanding what you have. Arrange them by shape or size. Stack them. Start building and end up with a house and garden scene.

Know Your Audience

Tailoring your visualization to the audience ensures your insights are not only accurate, but actionable. A data table may work for your fellow nerds or technical stakeholders, while a simple bar chart or big numbers might be more effective in a boardroom.

Be mindful of the story you’re trying to tell. Are you focusing more on channel composition or channel trends? Arrange your data and present it in a way that truthfully supports your story. It’s all about knowing your audience and the context in which it will be shared.

Don’t be the “distracted boyfriend.” You know the meme — the guy turns his head to check out another woman. Think of the tried-and-true visuals, bars and lines as your soulmate. Don’t let treemaps or bubble charts catch your eye. They can be difficult for people to understand, and you may spend more time explaining how to interpret them than focusing on results.

Do the Squint Test

A quick way to evaluate your visual clarity is the squint test. If you can’t easily distinguish elements while squinting, your viewers likely can’t either — especially those with visual impairments.

Make sure your fonts are large enough and your color choices work for all audiences. Use free tools like a Contrast Checker, Microsoft’s Accessibility Checker or a Color Blindness Simulator to ensure what you’re trying to convey is easily understood. Context won’t matter much if people can’t see the visual in the first place.

Effective data storytelling hinges on context. Dig deeper to uncover not just what happened, but why. Design your visuals to communicate clearly, serve your audience and drive smarter decisions. And yes — always give yourself extra time at TSA.

SB Birnie, who goes by either SB or Birnie, is a queer vegan and self-identified nerd who has been in nonprofit fundraising and analytics for more than 20 years. They got their start as the membership intern at the Human Rights Campaign; then managed and grew the nonprofit’s sustainer and canvass programs; and ultimately helped form the analytics department before heading to ROI Solutions where they did everything from programming and product management to database conversions and integrations. Now, as the vice president of analytics at Avalon Consulting, Birnie brings together that experience to provide in-depth analytics, forecasting and more to nonprofits.

The Power of Collaboration

By Kathy Swayze, CFRE

In an increasingly interconnected world, collaboration between like-minded organizations is a strategic necessity. DMAW invests time and energy in building collaborations that help create a richer experience for our members. Collaboration expands access to training and resources, helps amplify our voices in the advocacy arena and stimulates innovation.

“There are so many different things that contribute to nonprofit success,” Donna Tschiffely, executive director of DMAW, said. "Working with other associations allows us to meet different needs while exceling at our core mission."

Here are some of the successful collaborations that DMAW has invested in over recent years.

The Nonprofit Alliance (TNPA)

DMAW was at the table when TNPA formed. There were critical needs in the sector that were not being met, and we worked closely to ensure that TNPA's offerings would be complementary to DMAW’s programs. Today, TNPA’s core focus areas include advocacy and leadership development, while DMAW leads in the areas of direct response strategy, campaign tactics and implementation, and fundraising and marketing staff training.

In 2023, DMAW and TNPA launched the Nonprofit Fundraisers Symposium, the inaugural conference in Washington, D.C. Limited to 150 nonprofit leaders with 10-plus years of experience and 150 professionals serving the nonprofit sector, it’s a rare chance to dig deeper on critical issues. Participants form powerful partnerships, gain insights on cutting-edge fundraising strategies and help drive progress for the entire sector.

Association of Fundraising Professionals, Washington DC Metro Area Chapter (AFPDC)

Since 2006, DMAW has partnered with AFPDC to convene the three-day Bridge to Integrated Marketing and Fundraising Conference. The conference has grown from 600 attendees in its first year to nearly 3,000 at its peak. Each year, conference and committee co-chairs include one leader each from AFPDC and DMAW to help curate 90-plus breakout sessions.

In recent years, the Bridge Conference enhanced its appeal by working with its partners to add pre-conferences and additional tracks during the main conference:

• The Faith & Fundraising Forum, which welcomes all faith-based fundraisers, is produced in partnership with TNPA Christian Development Council.
• BridgeTECH, co-presented by NonProfit PRO, explores how nonprofits can maximize technology to power their missions.
• The professional development track during the Bridge Conference is hosted by TNPA.

Other Collaborations

DMAW also regularly collaborates with the Direct Marketing Fundraisers Association (DMFA) and the Alliance of Nonprofit Mailers (ANM) on timely, targeted efforts — ranging from urgent briefings on postal policy to webinar programs.

What Collaboration Means

Through these powerful collaborations, DMAW gives its members the tools they need to address challenges and build success. If any single organization tried to address all the diverse and complex needs of our sector, it would quickly become unwieldy and ineffective, lacking the agility to thrive in a world where change is fast and constant.

We know collaboration is important, but what makes it successful?

“It’s important that leaders check their egos at the door,” Tschiffely said. “You, of course, want to make decisions that are best for your organization. But you also must be flexible and lean into what’s best for the entire sector.”

DMAW is deeply committed to collaboration. By looking beyond our organization and embracing strategic partnerships, we create a more enriching experience for all DMAW members. We regularly explore new partnerships and welcome your input. If you have ideas for potential collaborations or other ways DMAW can help you succeed in your jobs, reach out to executivedirector@dmaw.org.

Kathy Swayze, CFRE, is president and creative director of Impact Communications, a fundraising agency focused on great storytelling, mid-level fundraising and planned gift marketing. Contact Kathy at kswayze@impactdc.com.

Rethinking Work-Life Balance in Nonprofit Fundraising and Direct Response

By Brenna Holmes

If you’ve been in this field long enough, you’ve probably asked yourself the same question more than once: How much can I really handle before I snap?

Not in a hypothetical, late-night-philosophy kind of way. More like a “my to-do list has its own to-do list, and I just opened a Slack message that made me cry a little” kind of way.

In nonprofit fundraising and direct response, this isn’t just about burnout. It’s about sustainability. Yours. Your team’s. Your mission’s.

Because we’re not in this work to coast. We’re here to create progress, drive impact and solve real problems for causes that matter. But doing that in a landscape where urgency is constant, budgets are tight and expectations are high? It’s not just demanding, it can be destabilizing if we’re not careful.

The Weight of the Mission

When your work is mission-driven, it’s easy to blur the lines between passion and pressure. You care, deeply. That care fuels extraordinary things, from high-converting campaigns to lasting donor relationships. But it can also keep you locked in a cycle of over-functioning, especially in organizations where understaffed teams and overcommitted calendars are considered normal.

Direct response professionals are particularly vulnerable. We operate on accelerated timelines, across a half-dozen channels, managing messages, metrics, and momentum simultaneously. We’re constantly toggling among strategy, execution, reporting and pivoting.

The pace doesn’t slow unless we decide to slow it. And the longer we wait, the harder it becomes to distinguish urgency from noise.

The Problem With ‘Balance’

Work-life balance is a nice concept, but for many in our field, it feels more like a myth than a model. That’s not because we’re bad at boundaries. It’s because the way our work is structured often makes it impossible to succeed without overextending ourselves.

When people talk about work-life balance, they often mean time management. But the real issue is energy management — mental, emotional and creative. You can optimize your calendar all day, but if your workflows are inefficient, your expectations are misaligned or your team is flying without a net, no color-coded time block will save you.

Systems, Not Superheroes

Instead of expecting individuals to be superhuman, we should build smarter systems around them. That means designing roles and teams with intention. It means clarifying priorities, setting boundaries that honor both the work and the people doing it, and normalizing the pause. Asking “What’s essential right now?” before charging ahead.

Leaders play a critical role here. Not just by modeling boundaries and sanity, but by ensuring that their teams are actually resourced to meet the goals they’re being asked to achieve. It’s not heroic to shoulder an unsustainable workload. It’s a signal that something in the system needs to shift.

Culture Is the Real Capacity Builder

No amount of time off can fix a culture that expects 24/7 availability. No Slack status can override the unspoken norm that being busy is a badge of honor.

If we want to keep high-performing fundraisers and changemakers in the sector — and help them thrive — we have to make room for restoration, clarity and human-scale work. That starts with how we define success, and who gets to speak up when the load is too heavy.

Organizations that lead with purpose should also be willing to structure their teams and processes with purpose. That includes things like:

• Making strategic choices about what gets done, when and by whom.
• Saying “no” to shiny object syndrome — even when it’s donor-driven.
• Centering people in process design, not just outcomes.
• Equipping teams to work with intention, not just intensity.

Progress Is Possible

There’s no perfect formula for balance. But there is a path forward — and it starts with acknowledging that we can be mission-driven without being martyr-driven.

You are not the only one feeling stretched. You are not the only one questioning whether the pace at which you’re working is sustainable. And you are definitely not the only one who deserves better.

We can do this differently. We can build structures that support brilliance without burnout. That’s the kind of change worth working for.

Digital Exclusive

Part 2: Workload Sanity Check-In: How Are You Really Doing?

Take a minute. Answer honestly.

1. How often do you feel truly “done” with your workday?

   1. Rarely
   2. Occasionally
   3. Almost every day

2. When you take time off, do you fully disconnect?

   • No, I still check email or Slack
   • Sometimes, but I feel guilty
   • Yes, I trust my team and systems to handle things

3. How often do you reprioritize based on strategic goals (not just what’s loudest)?

   • Almost never
   • Sometimes, when I remember
   • Regularly—it’s part of how I lead or work

4. Do you feel supported by your organization when your workload feels unmanageable?

   • Not at all
   • In theory, but not in practice
   • Yes, there are structures and people I can lean on

5. How do you feel at the start of most weeks?

   • Dread
   • Cautious optimism
   • Clear and focused

Mostly A’s? You’re likely overcapacity. Time for a hard look at what’s on your plate, and who can help you carry it.

Mostly B’s? You’re surviving, but with small changes in structure or communication, you could be thriving.

Mostly C’s? You’ve likely built systems and boundaries that support your success. Keep modeling what’s possible.

Brenna Holmes is a strategic adviser and executive coach helping purpose-driven teams design smarter systems, healthier cultures and sustainable growth. Through her consulting practice, she empowers nonprofit and social impact leaders to reclaim their time, align their teams and lead with clarity. Learn more at brennaholmes.com.

Breaking Data Silos: Small Steps that Work

By Anna Rhodes and Ariel Kaplan

Have your fundraisers been trying to create a donor-first fundraising program? Have they been pushing for better channel integration in direct response campaigns and better reporting on the impact of various channels on donors’ giving behavior?

We data folks want to support the fundraisers’ business strategy. A major obstacle is data silos: different CRMs for online and offline or channel-specific production processes.

Another challenge is chronic resource constraints. When one person is wearing 14 hats, it’s only natural for them to focus on their own tasks. Collaboration across verticals becomes a luxury, not a standard. The person keying gifts into Salesforce isn’t talking to the person pulling reports in Excel, who isn’t talking to the person building audiences for digital ads. Not because they don’t want to — but because the structure doesn’t support it.

We have faced all of these challenges and found a number of ways to make meaningful progress toward an integrated data environment that supports the direct marketers’ business goals — all without new systems or high-priced consultants.

Think Big, Start Small

Thinking big has several advantages. When you have a documented vision (aka data strategy), you can more easily get buy-in from leadership for your work, and you have a prioritization guide.

In our case, Anna, as the analyst, led discovery sessions with the online and offline direct response fundraising leaders to understand their long-term goals for a donor-first fundraising program and the data products they believed would help them. This information allowed us to clearly communicate with one another about data needs. Then, Anna was able to create visuals for the fundraisers so that they could see how our data work tied to their wish list.

We already had full-time jobs on two different data teams before we started selling this idea of a common data strategy, cross-team collaboration and data architecture transformation. So we could have become overwhelmed by our grand data strategy to offer advanced attribution reporting, our own predictive models or even the more modest goal of offering integrated campaign reporting that made it easy for fundraisers to see the relative impact of each channel. This is where starting small comes in.

We started with building a flattened contributions table that brought together data from Salesforce, EveryAction and other platforms. It wasn’t glamorous, but it gave us a shared foundation. That one build supported dozens of future projects and reports — many of which had nothing to do with attribution reporting directly, but everything to do with making it possible down the line.

Build Relationships of Trust

As we mentioned, we were on different data teams with very different responsibilities. We didn’t know each other. In order for us and the colleagues who joined us on our integrated data quest to be successful, we had to develop a high level of trust in each other quickly. We did not have time to use a high collaboration approach on every project. A few small steps made a huge difference.

The biggest item was simply creating a recurring weekly meeting with a shared agenda. This meant that we were showing our commitment to this work every week, even when the only time we could spare was that one hour. These regular conversations also helped us de-silo our language so we could communicate clearly with each other. We were soon learning each others’ work and developing a common language as we sought advice on our projects.

We chose to believe in each others’ expertise from the start while proving ourselves to each other through the reliability of our work. For example, when Ariel presented a draft table to the group and he had already thoroughly validated it against existing data sets, it built trust, and the team was happy to use a high autonomy approach for future builds.

Tools for Incremental Change

In addition to the weekly meetings, building trust, relying on each others’ expertise and developing a shared language, a small step that had a big impact was our development of a documentation template.

Our template included sections for purpose, status (in production, in development), an entity relationship diagram link, functional requirements, data sources, data definitions and the proposed design of the data product. During development, these templates helped us record group decisions and ensure clear communication between analysts and engineers. Once the data product was in production, the existence of clear, consistent documentation meant that we all felt comfortable using the data product. So we weren’t waiting on each other to help with our various script rewrites. We were able to take the new shared data product and immediately incorporate it into our own work.

Of course, every day will continue to be filled with the thousand urgent items and all the work you have been supporting for years. However, we are hopeful that, like us, you can make small steps toward your grand data dreams.

After working as a direct marketing fundraiser for 10 years at the Seattle PBS Station, Anna Rhodes moved to Washington, D.C., to pursue a career in data and analytics. She spent years creating BI dashboards, performing full-program analyses and developing a data warehousing plan at an agency serving 20-plus nonprofits. For the past six years, she's been at Planned Parenthood Federation of America building campaign services and analytics offerings. She currently serves as the nonprofit's director of data and analytics.

Ariel Kaplan, the associate director of data architecture at Planned Parenthood Federation of America, has been a nonprofit data professional for nearly 15 years. He is proud of the many hats he has worn — from sorting mail and batching gifts to designing gift-entry processes and the many different data roles that led him to become a data architect.