Finding Balance in the AI Conversation
The conversation around Artificial Intelligence, or AI, has become hard to avoid. Headlines range from sweeping claims about societal transformation to debates about trust, accuracy, and public perception. For leaders of nonprofits, foundations, and associations, this steady flow of information can feel overwhelming. It often raises more questions than it answers.
Leaders are asking what is real, what is still speculative, and how to steward limited resources responsibly. Those questions are reasonable. They reflect the care mission driven organizations bring to every major technology and investment decision.
At Build Consulting, we see this tension clearly in our work with nonprofits, foundations, and associations. Many organizations understand that AI will shape how work gets done in the coming years. At the same time, they are rightly cautious. A poorly chosen investment can divert attention, strain budgets, and erode trust with staff or constituents. AI readiness for nonprofits is, in my view, an organization-specific evaluation but every organization should be thinking about AI.
Two Competing Realities Leaders Are Balancing
In our work across the social sector, we consistently observe two realities in tension.
- Organizations that do not explore AI may be left behind.
- Organizations that move too quickly, or without adequate planning, risk disappointment, staff fatigue, and financial strain.
Navigating between these realities does not require urgency or fear. However, it does require clarity and a plan Leaders need a way to filter out noise and assess opportunities in a manner that reflects the realities of their organization, the readiness of their organization, governance responsibilities, with a view towards the long-term.
The question becomes not whether to act, but how to begin planning for AI in a measured and intentional way.
The Build Consulting Framework for Nonprofit AI Investment
Build Consulting approaches AI planning through a structured evaluation framework designed for mission driven organizations. The intent is practical. Every potential AI initiative is reviewed through multiple lenses that matter to executive leadership, boards, and staff.
This framework helps move AI conversations out of isolated technology discussions and into broader organizational decision making. It also creates a shared language for leadership, operations, and data teams.
The framework includes five measures.
Measure 1: AI Value/Return on Investment for Nonprofits
For nonprofits, foundations, and associations, the purpose of any technology initiative is to strengthen mission delivery. AI is no exception.
We encourage leaders to define value across three dimensions.
- Mission impact. How might this initiative improve your ability to advance your purpose, such as deepening services, expanding reach, or accelerating research.
- Constituent experience. Will donors, members, or program participants experience clearer communication, more timely support, or greater personalization.
- Financial implications. What measurable financial outcomes could reasonably be expected, including cost reduction or improved revenue performance.
Financial returns matter. Still, in many AI initiatives, mission impact carries the greatest weight. We also see that projects with the highest potential impact can place the greatest demands on an organization. Assessing readiness for nonprofits involves not only understanding the ROI of the technology, but change management ROI as well. not just aspiration, becomes essential as well.
Measure 2: Technical and Data Feasibility
Even a compelling AI idea cannot succeed without the right foundation. Technical feasibility goes beyond the technology itself. It requires an honest look at data and should be underpinned by a data strategy.
Key considerations include the following:
- Data readiness. Does the organization have sufficient data quality, structure, and governance to support AI use. Gaps here often surface broader data platform issues that need attention first.
- Tools and infrastructure. Are the necessary systems already in place, or would the initiative require significant new investment.
- Staff capability. Does the organization have the skills to oversee and sustain the work. If not, what level of training or external support would be required.
When data is fragmented or unreliable, feasibility is limited, regardless of potential value. Addressing those fundamentals is often a meaningful first step.
Measure 3: Mitigating the Risks in AI
AI introduces ethical, legal, and operational risks that require deliberate oversight. For organizations serving vulnerable communities or handling sensitive information, these risks deserve particular care.
A thoughtful risk review typically includes the following.
- Ethical. Guardrails to ensure AI is free of bias, is trained in fairness, and governance is in place to mitigate unintended consequences.
- Data privacy and security. Obligations including compliance with applicable regulations and internal standards.
- Legal and policy. Considerations related to transparency, intellectual property, and accountability.
- Operational reliability and accuracy. including the consequences of system failure or inaccurate outputs.
Risk tolerance will vary by organization. Traditional business applications, applied internally, carry different implications than AI systems that influence service delivery or decision making that impacts your communities. Many leaders find it helpful to create AI specific policy/governance structures that define acceptable risk.
Measure 4: Anticipated Costs
AI is sometimes perceived as low cost, particularly when tools appear embedded in existing platforms. In practice, leaders benefit from understanding the full cost over time.
Total cost of ownership should include both initial and ongoing elements.
- Initial costs. Planning, data preparation, configuration, and potential external guidance.
- Ongoing costs. Licensing, subscriptions, model updates, and integration with other systems.
- Indirect costs. Staff time that is required for adoption support, oversight, quality review, and ethical governance.
Clear visibility into these costs supports sound, multi-year financial planning and helps set realistic expectations.
Measure 5: Change Impacts
Change management is inseparable from AI adoption. New tools almost always bring new workflows, new expectations, and new concerns. Tools are expanding and flexing.
Leaders should consider the scope of change across several areas.
- Process adjustments. The frequency and cadence with which we need to evaluate existing processes in the context of AI is quickening.
- Adoption support. Training and support needed for staff to work confidently with new tools.
- Cultural impact. This includes questions about up-skilling, role evolution, and job security.
- Role definition. Especially where routine tasks may shift and staff capacity can be redirected toward higher value work.
When leaders engage staff early and communicate clearly, AI initiatives are more likely to be understood as supportive rather than disruptive. We are all on a journey of AI exploration and learning together. Bring your team along.
From Discrete Projects to an Adaptive Roadmap
Evaluating potential initiatives across value, feasibility, risk, cost, and change impact provides a strong foundation for an AI roadmap. That roadmap may feel different from traditional technology plans.
AI planning often benefits from an iterative approach.
- Early efforts may focus on learning rather than scale.
- Insights from one initiative can reshape priorities for the next.
- Some ideas will move forward, while others may pause as conditions change.
This flexibility allows organizations to explore AI thoughtfully without chasing trends or standing still.
When guided by a clear framework, AI becomes less about hype and more about informed choice. It supports careful planning, grounded experimentation, and responsible stewardship. In that context, organizations can begin preparing for AI in a way that respects mission, people, and long term trust.
