How Real-World Experience Shapes Responsible AI at Blackbaud

AI innovation can often start with technology, then works backward to the people it’s meant to serve.

But in the social impact sector where the work is deeply human, that approach can’t hold.

At Blackbaud, we believe in a people-first approach to AI innovation, where technology needs to earn its place in this relationship-driven environment.

This requires a clear understanding of how AI services are experienced in practice, how they might support relationships between individuals and organizations, and what it means for the social impact sector to use these systems. 

This article explores how we develop that understanding through responsible AI research and how it informs our approach in practice.

What responsible AI research looks like at Blackbaud

Responsible AI research science at Blackbaud centers on people, how they live, work, make decisions and where AI technology could, or shouldn’t, play a role in helping them advance their mission.

The discipline explores the societal and ethical implications of AI over time, with an emphasis on defining what trustworthy AI needs to mean in this context: systems that support human judgement, respect the communities they serve, and align with the sector’s values of accountability, care, and long-term stewardship.

Practically, our research includes direct engagement with social impact professionals and supporters, industry research, signals from Blackbaud’s proprietary data, behavioral and attitudinal insight, and cross-functional synthesis.

What we’re learning from the sector

Across our research, differences in how people approach AI often reflect the broader context they bring with them.

Some people experience AI as a familiar part of digital interaction, where usefulness and speed take priority. In these cases, keeping the experience focused on achieving real value, alongside clear disclosure, can build trust.

Others may focus more on how AI fits into their workflow. Here, value is shaped by whether it simplifies a task without becoming intrusive. The ability to adjust, opt in, review decisions, or step back can influence how comfortable the experience feels.

There are also perspectives that place greater emphasis on awareness and reassurance. In these cases, clearer signals around use of AI, how data is involved, and where to go for support can become even more important.

Taken together, these perspectives show a shared need for experiences that are understandable, proportionate to the task, and give clear space for human judgement.

Our research also highlights that these experiences rarely exist in isolation. People engage with AI across connected tools and workflows, and how systems interact can shape trust just as much as any single interaction.

This is where responsible AI moves from individual moments to broader decisions. What we learn about how people experience AI directly informs how we define and apply our principles, shaping how systems connect, how information moves, and how accountability can be maintained.

Connecting Blackbaud’s responsible AI principles to real experience

Responsible AI at Blackbaud is built on six core commitments: mission first, transparency and explainability, fairness, inclusion and accessibility, safety, security and privacy, accountability and human oversight, and sustainability and stewardship.

Stating these commitments is one step and understanding how they hold up in practice is another. Our responsible AI research plays a central role in closing that gap. It connects principles to real experience, helping our teams at Blackbaud ground decisions in how AI is understood, used, and evaluated in context.

Mission first

What it means in practice
A mission-first approach starts with the goals and realities of social impact organizations. This is about creating experiences that meaningfully advance the work of these organizations and the people, communities, and causes they serve.

How research informs this work
Our research begins with the context people are already operating in. In this sector, resource constraints, accountability, and relationship-driven work influence how AI is perceived and used. Research helps surface where AI may support confidence and where it may introduce hesitation, keeping decisions grounded in real-world context.

Transparency and explainability

What it means in practice
People need to understand what a system is doing and why, in order to decide how to act on it.

How research informs this work
Explanations, when delivered at the right moment, can support decisions about when to rely on or question outputs. Our research informs how AI capabilities are communicated, which in turn influences how people can calibrate trust and use systems appropriately.

Fairness, inclusion, and accessibility

What it means in practice
AI-enabled services should reflect a range of perspectives and be usable across different levels of familiarity and confidence.

How research informs this work
People bring different expectations and levels of understanding. Our research draws on these perspectives so that design decisions can be based on the range of people who may encounter the service. Clear, supportive experiences can help people engage more confidently and understand next steps.

Safety, security, and privacy

What it means in practice
People need to feel confident that their data is handled responsibly and that the experience is reliable and controlled.

How research informs this work
Every interaction can influence perceptions of trust and safety. In the social impact sector, this is closely tied to responsibility for donor and community data. Our research highlights the importance of clear communication, access to human support, and careful handling of uncertainty.

Accountability and human oversight

What it means in practice
People remain responsible for decisions, with AI supporting, and not replacing, human judgement.

How research informs this work
Experiences need to support review, adjustment, and understanding of outputs. Our research can also inform where additional governance, safeguards, or oversight may be needed as systems evolve.

Sustainability and stewardship

What it means in practice
Responsible AI is a commitment that needs ongoing attention to stay aligned with real needs over time.

How research informs this work
Our research supports continued listening and adaptation as expectations and technologies change. Beginning with meaningful problems helps avoid unnecessary use of AI and keeps the focus on supporting missions in achieving real value. Ongoing behavioral and attitudinal measurement of AI experiences provides signals on where systems remain aligned and where adjustment may be necessary.

Continuously building towards trustworthy AI

In the social impact sector, responsible AI requires continual learning and understanding of how technology is experienced in practice.

At Blackbaud, responsible AI research helps us build that understanding over time. It supports us in grounding decisions in real-world context, shaped by what we’re learning, and applying insights to how we design, deliver, and govern AI.

This helps us to create experiences that support the people, relationships, and decisions at the heart of this work.

This ongoing cycle of understanding and application defines our approach to responsible AI, guiding services that can earn trust and hold their place in the sector.