Responsible AI at Blackbaud
A People-First Approach to AI for the Social Impact Sector
Published: June 2026
Table of Contents
- Executive Summary
- Responsible AI in the Social Impact Sector
- Our Approach: People and Principles First, Learning Always
- Governance, Oversight, and Stewardship as an Ongoing Practice
- Being Intentional About the Environmental Impact of AI
- How We’re Influencing Continuous Learning Across the Sector
- Responsible AI as an Enabler of Social Impact
- Additional Resources
- Authors
Authors
Carrie Cobb, Chief Data and AI Officer
Laurene Currie, Responsible AI Research Scientist
Ren Nunes, Sr. Manager, Data & AI Governance
Purpose
This paper outlines how Blackbaud approaches responsible AI in the social impact sector. It’s designed to give a clear, practical view of how we think about, build, and evolve AI in a way that reflects the realities of trust-based work. It sets context for how our principles, research, and governance connect and evolve over time as new insight, risks, and expectations emerge.
AI is changing how social impact organizations work and make decisions. In a sector built on trust, stewardship of data, and accountability to communities, this technology creates a real opportunity to increase mission impact—but only if introduced thoughtfully.
At Blackbaud, responsible AI is an ongoing practice. We approach it through a culture of learning that’s grounded in evidence about real people’s work, guided by clear principles, and supported by human accountability and oversight as systems evolve.
This paper explains, in practical terms, how Blackbaud approaches responsible AI, including:
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Why we believe responsible AI is important in social impact contexts
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How we start with people through ongoing research and sector insight
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The principles that guide our decisions across the AI lifecycle
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How we approach governance, oversight, and stewardship and being adaptable as the landscape changes
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How we are being intentional about the environmental impact of AI as part of our responsibility
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How we contribute to shared learning across the sector
A note on transparency: This paper (published June 2026) reflects our current posture and the direction we are building toward. AI, regulation, research insight, and best practices evolve quickly. Our intent is to communicate clearly and honestly, update as we learn, and remain answerable to evidence and people.
Blackbaud’s approach to responsible AI is shaped by two commitments:
- A learning posture grounded in our data, primary research, and real-world evidence, so we can adapt as technology, expectations, and risks evolve
- A clear set of principles to guide decisions and trade-offs
We aim to build AI-enabled services that support social impact work in ways that are understandable, appropriately governed, and worthy of trust over time.
What People-First Means in Practice: Responsible AI Research at Blackbaud
When we say research fuels responsible AI at Blackbaud, we mean that decisions should be informed by evidence about how real people in the social impact sector think, work, and decide.
Starting With People Before Technology
A common pattern in AI innovation is to start with the capability of the model and work backwards. Our intent is to start with the people who will live with the outcomes:
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The Major Gifts Officer building relationships
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The Finance Director who needs to trust what the data is telling them
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The Program Coordinator making sense of reporting
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The donor who feels a lack of connection to the cause
This grounding is important because social impact work is deeply human and relationship-based, and we believe AI must earn its place in that environment.
Responsible AI research starts with people and their real-world context. It focuses on understanding genuine needs so that innovation decisions are grounded in evidence and support trust as something built over time.”
Laurene Currie
Responsible AI Research Scientist
How Our Research and Data Inform Responsible AI Decisions
Our responsible AI research is ongoing and multi-method, combining the breadth of data across the Blackbaud ecosystem with direct engagement and real-world observation. It includes:
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Using our proprietary data to identify trends, patterns, and signals around need and improvements
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In-depth conversations with social impact professionals and the people they serve (e.g. donors, teachers, students, parents) to understand goals, pressures, and real decision contexts before discussing AI at all
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Early concept testing with real users to observe interpretation, trust signals, hesitation, and points of confusion before major commitments are made
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Longitudinal sector research to track how confidence and expectations shift over time as people gain experience with AI
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Engagement with industry frameworks and emerging regulation to benchmark thinking and keep risk conversations current
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Cross-functional synthesis so findings directly inform product, design, engineering, and governance decisions
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Developing and deploying our own trust evaluation measurement frameworks, based on a robust data set and expertise to track experiences around credibility and trust, quality and engagement with AI-enabled services
Each year, more than $100 billion is raised, granted, or invested through Blackbaud solutions. This scale of real-world activity, combined with deep sector data and behavioral insight, helps us ground our understanding of AI in how it’s experienced in real workflows and decision-making contexts.
Through this work, we continue to observe themes across roles and organizations that shape how people understand, use, and build trust in AI-enabled services.
These patterns and insights shape how we approach AI design in practice, including when and how we explain AI involvement and how we can provide meaningful, context appropriate user control.
Alongside our data and research insight, our responsible AI principles provide a key reference for our teams to guide intentional decision making in innovation.
Our Six Responsible AI Principles
The following principles act as a shared compass across our teams and across the AI lifecycle. They are designed to guide decisions about what we build, how we build it, and how we respond when new risks or information emerges.
Alongside these principles, we translate this into what you can expect us to pay attention to in practice.
Mission First
AI should serve the mission, not the other way around.
We measure AI decisions against a practical question: does this meaningfully advance the work of social impact organizations and the people, communities, and causes they serve?
In practice: We aim to prioritize AI use cases where the value is clear for social impact work to avoid novelty-driven deployments.
Fairness, Inclusion, and Accessibility
AI must work equitably for everyone it touches.
We treat fairness and accessibility as ongoing work: examining outcomes, exploring diverse needs, and designing intentionally.
In practice: We aim to include diverse groups, roles and organization contexts in research and testing, and treat fairness as iterative work.
Accountability and Human Oversight
AI should remain answerable to the people who use it.
We explore context to appropriately preserve human judgment and agency.
In practice: We design for experiences where people can guide, review, adjust, and override AI-supported outputs where appropriate, especially in trust-critical workflows. What is defined as ‘appropriate’ in any given context needs to be informed through our ongoing research insight.
Transparency and Explainability
People deserve to know when AI is involved and how it works.
We prioritize plain-language explanations delivered at the moments they matter, to support informed judgment.
In practice: Through our research, we explore how people understand systems across different contexts and aim to include diverse perspectives.
Safety, Security, and Privacy
The data and relationships entrusted to us require protection.
Trust depends on systems behaving in a way that protects what organizations share with us. We approach this as a foundational responsibility across AI design and deployment.
In practice: We treat data stewardship and security posture as foundational to AI work, informed by established security and risk frameworks and internal review.
Sustainability and Stewardship
We’re building for the long term, and that includes the environment.
Responsible AI is ongoing stewardship: making thoughtful choices about resources, governance, and improvement over time, with the long-term health of the sector in mind. It also means being intentional about the environmental footprint of the AI we build, because AI has a real cost in terms of energy, water, and carbon.
In practice:
- We plan for AI as a long-lived capability that needs monitoring, iteration, and clear ownership as risks and expectations change.
- We strive for AI services to be proportionate to value: including defaulting to the simplest viable approach, minimizing unnecessary model calls, with an ongoing commitment to learning and monitoring impact.
- We work alongside cloud and infrastructure partners committed to renewable energy and continue to measure our overall footprint as part of how we steward the long-term health of the sector.
These principles provide a foundation to build on our responsible AI approach. Applying them across teams, systems, and decisions requires defining human accountability and ongoing oversight practices.
We view responsible AI as an ongoing practice with clear accountability, structured governance, and oversight across the AI lifecycle. This approach is anchored in human judgment and informed by context and risk as both AI systems and expectations evolve.
This section outlines how that approach is implemented through governance frameworks, accountability structures, and continuous oversight and learning.
Human Accountability as a Three-Tier Model
Responsible AI at Blackbaud is structured as a three-tier model of human accountability: oversight through the AI Council, centralized AI governance to set standards and guardrails aligned to our six principles, and federated execution across the lifecycle to operationalize them in practice.
In practice, this looks like:
- An AI Council made up of executive leaders from across Blackbaud’s core functions, providing oversight through shared cross-functional ownership. The council helps focus AI decisions around our goals and values, with coordinated guidance across security, legal, governance, and responsible AI practices.
- A centralized AI governance function that establishes review mechanisms, standards, and guardrails aligned to our six principles and industry best practice, then federates them across the company so teams can apply them consistently in context.
- A federated execution model across the AI lifecycle, where research, product, engineering, platform, and control functions operationalize those standards in practice through design, development, review, deployment, and ongoing monitoring.
These three tiers are connected through ongoing feedback loops and cross-functional engagement so what teams learn in practice, and from research, can inform oversight, refine standards and guardrails, and strengthen execution over time. This helps keep our approach aligned and continuously improving as technology, risks, and expectations evolve.
The practical intent is to keep AI decisions understandable, reviewable, and accountable, so teams can respond when new information emerges or expectations change.
We design AI to support people’s judgement, not replace it. That means clear accountability from the start and oversight that grows with the system.”
Ren Nunes
Sr. Manager, Data & AI Governance
Ongoing Oversight, Stewardship, and Continuous Learning
Once AI-enabled capabilities are deployed and in use, oversight, review, and the ability to intervene remain part of responsible stewardship.
As AI is applied in real contexts, new signals emerge from:
- How people interpret and use AI-supported insights
- Where confusion or misuse risk shows up
- How system performance shifts over time
- How regulatory expectations and best practices evolve
Our intent is to pay attention to these signals and respond thoughtfully when additional clarity, safeguards, or changes are needed. Our methods to turn this intent into practice include working on in-product feedback loops and measuring the AI experience with a focus on credibility and trust, quality and engagement.
AI has a real cost in terms of energy, water, and carbon, so part of responsible AI by design is being intentional about how and where it’s used. We see this as part of our wider commitment to sustainability and stewardship. Building for the long-term means accounting for what AI costs the environment, alongside what it delivers in value.
Design Principles that Support Keeping AI Proportionate
At Blackbaud, that translates into a few core design principles that help shape how teams approach innovation:
- Grounding AI in real outcomes. Starting from a clearly defined problem and the value AI might add to it, rather than introducing AI for its own sake.
- Defaulting to the simplest viable approach. Not every step needs a large model. Where simpler, more efficient methods do the job well, we explore these first.
- Introducing AI only where it adds meaningful value. We weigh the benefit of an AI-enabled capability against its cost, including its compute and energy cost.
- Reducing unnecessary computation. We are deliberate about minimizing model calls and avoiding repeated or wasteful use.
The AI-enabled experiences we’re building are a reflection of these principles in practice. Where we can deliver the same outcome with less computation, we strive to do so.
Action at an Organizational Level
Alongside how we design individual capabilities, we’re also working more broadly as an organization:
- Partnering with cloud providers committed to renewable energy
- Investing in carbon offset programs
- Continuing to measure and reduce our overall footprint
It’s an evolving space. Model efficiency, infrastructure choices, and measurement practices are all changing quickly, and our commitment is to keep learning and adjusting. The goal is to build AI in a way that is thoughtful, efficient, and sustainable over time, answerable to the same standard of evidence and intentionality as the rest of our responsible AI work.
With a focus on continuous learning within Blackbaud, we believe responsible AI leadership should also include contributing to shared learning across the sector.
In 2025, Blackbaud convened the AI Coalition for Social Impact to support cross-sector dialogue, shared learning, and practical education that helps remove barriers to responsible AI adoption. A key initiative of the Coalition is the AI for Social Impact Certification Program, which provides free, on-demand courses to help all social impact professionals build practical AI skills and strengthen responsible use.
Members of our leadership contribute across nonprofit advocacy, governance and policy, philanthropy research, fundraising innovation, and cross industry AI discussions through groups, including:
- The Advisory Council for Fundraising AI
- The National Artificial Intelligence Association
- The Association for the Advancement of Artificial Intelligence
- The Nonprofit Alliance
- Independent Sector
By engaging in these forums, we can continue to help shape how responsible AI standards are discussed, framed, and advanced across social impact.
Learn more about how we’re shaping the future of responsible AI across the sector.
Responsible AI is about earning the right to scale innovation.”
Carrie Cobb
Chief Data and AI Officer
- Blackbaud AI Terms: Review contract provisions that apply to AI-enabled capabilities across our solutions.
- Blackbaud Trust Center: Security, privacy, compliance documentation, and governance resources.
Carrie Cobb
Chief Data and AI Officer at Blackbaud
Carrie Cobb is an accomplished innovator and change-agent capable of translating multifaceted data and analytics products into successful business initiatives and consumer experiences. She holds over 25 years of experience in developing data-driven analytic solutions, with the goal of leveraging advanced analytic methodologies and artificial intelligence to create easy to execute strategies for the social impact community. As the Chief Data and AI Officer at Blackbaud, Carrie leads the Data Intelligence Center of Excellence, and is responsible for Blackbaud’s AI and data strategy, vertical-focused product innovation, data science and engineering disciplines, and data governance and ethics. She chairs the Advisory Council on Methodology for the Giving USA Foundation, serves on the Governmental Affairs Committee for the Nonprofit Alliance, and is a member of the Advisory Council for Fundraising.AI. Carrie has been honored as one of DataIQ’s 100 Most Influential People in Data in both 2024 and 2025, and was named to CDO Magazine’s Global Data Power Women List 2023. Most recently, she was recognized by the Stevie Awards in the Women in AI Leadership category of the 2026 American Business Awards, and was a nominee in the 2026 Responsible AI RAISE Awards for Outstanding Individual in Responsible AI. She holds a Chief Data Officer Certificate from Carnegie Mellon University.
Dr. Laurene Currie
Responsible AI Research Scientist at Blackbaud
Laurene leads research at the center of experience and ethical AI innovation. For two decades she’s researched human behavior, human-avatar interaction and consumer, nonprofit and higher education technology experiences, and driven initiatives that shape how organizations can adopt AI responsibly. She has a PhD in Usability Engineering, where she focused on people’s experiences of interaction, collaboration and learning via avatars in financial services contexts and a Master of Arts in Sociology. As a Responsible AI Research Scientist at Blackbaud and Research Advisor, AI Innovation for the Blackbaud Institute, her work includes developing AI experience frameworks, principles for agentic and generative AI to foster trust and clarity and conducting in-depth studies on customer and donor sentiment towards AI-enabled services. Laurene champions human-centered AI practices that prioritize transparency, inclusivity, and positive social impact.
Ren Nunes
Senior Manager, Data and AI Governance at Blackbaud
Ren Nunes is a Responsible AI and governance leader focused on designing enterprise systems that enable AI to scale with trust, accountability, and speed. He specializes in rethinking traditional governance models, building approaches that embed risk management directly into how systems are designed and operated.
His work makes AI decision-making clearer, more consistent, and easier to act on at scale, enabling organizations to accelerate adoption while strengthening transparency and regulatory alignment. He is particularly focused on connecting governance, risk, and behavior change to ensure AI can be used confidently across the enterprise.
Ren currently leads Responsible AI, AI literacy, and Data & AI governance capabilities at Blackbaud and brings nearly two decades of experience in customer trust, compliance, privacy, and governance.
Ren holds a Bachelor of Science in Management from Clemson University, a Master of Business Administration from the University of Wisconsin, and is a Certified Information Privacy Manager (CIPM) through the International Association of Privacy Professionals.