Summary.
In years past, a familiar formula carried many grant proposals: describe a pressing community need, outline planned activities, and submit to funders whose missions broadly aligned. Persuasive writing did much of the work, and for a long time that approach succeeded.
Proposal review has since shifted. Federal rules now direct agencies to weigh applicant risk and measure grantee performance, and generative AI has made polished prose easy to produce. As a result, reviewers increasingly look past the narrative to the program behind it, including its baseline data, logic model, staffing plan, and evaluation design. A moving story still matters, but it can't stand in for those details.
This article traces five shifts behind that change, then lays out eight criteria reviewers use to judge whether a program is ready to fund and ready to run. A self-assessment at the end shows where your program stands on each.
From narrative-first to design-first.
The narrative-first approach was not a mistake. It reflected how much of philanthropy operated, with relatively light reporting requirements and grant decisions shaped by relationships and storytelling. That environment rewarded an informal formula, one many development teams still follow.
Emotional appeal + broad census need + network affinity = funded award
Five shifts have since weakened each part of that formula.
Shift 1. From individual donor psychology to institutional review.
Individual donors respond to emotional connection. Behavioral economists call this pattern the identifiable victim effect, in which a single, named person in need draws more giving than statistics describing many people (Jenni & Loewenstein, 1997). Adding statistical information can even reduce donations to an identifiable person (Small, Loewenstein, & Slovic, 2007), and compassion declines as the number of people in need grows, a pattern known as compassion fade (Västfjäll et al., 2014).
Because many development teams manage both annual appeals and institutional proposals, techniques from individual fundraising often carry over into grant applications. The proposal describes the hardship, shows the organization's heart, lists planned activities, and makes the request. However, institutional funders make award decisions differently. Program officers and review panels answer to boards, agencies, and their own reporting requirements, so they have to weigh whether a program can deliver what it promises. The story earns attention; the program design earns the award.
Shift 2. From light oversight to measured performance.
For much of philanthropy's history, what an application promised and what happened in the field were only loosely connected. Before digital reporting systems, tracking performance as it happened was impractical, so funders often accepted headcounts and narrative reports as evidence of success. Consequently, programs that looked strong on paper could struggle during implementation, sometimes without anyone noticing until the grant had ended.
Federal rules have since made performance a formal part of the award. Under the Uniform Guidance (2 CFR 200), as revised in 2024, federal agencies must assess the risk an applicant poses before making an award (§ 200.206) and must measure recipients' performance against program goals and objectives (§ 200.301). An award may also be terminated if it no longer advances program goals or agency priorities (§ 200.340). Pass-through entities, such as state and local agencies or other organizations that regrant federal funds, must evaluate each subrecipient's risk of noncompliance and monitor its overall performance so that the subaward's goals and objectives are achieved (§ 200.332). Ultimately, when a subrecipient's program struggles, the organization that passed the funds along has to answer for it as well.
Shift 3. From established relationships to demonstrated viability.
Relationships have long shaped institutional funding, and proposals sometimes documented decisions made largely through informal conversations. Relationships still matter. However, the sector's growing emphasis on trust-based philanthropy, which favors flexible, multiyear support with lighter reporting, has been slow to change practice. In a Center for Effective Philanthropy study, foundation leaders expressed strong support for multiyear general operating support, yet relatively few of their foundations provided much of it (Buteau et al., 2020). Where flexible funding exists, it is scarce and competitive, and funders often reserve that type of support for organizations that have already demonstrated sound programs and governance.
Public funding operates on different terms altogether. Federal grantmaking runs on regulatory compliance rather than trust, so organizations that wait for trust-based exceptions may find themselves outside major public funding streams.
Shift 4. From proposal drafting to program design.
Review panels rarely explain why a proposal was declined, so applicants often have little to go on. Under that kind of uncertainty, organizations commonly follow the most visible traits of successful peers, a pattern sociologists call mimetic isomorphism (DiMaggio & Powell, 1983). In grant seeking, the most visible trait of a winning application is its narrative. As a result, proposals are treated as writing projects, and organizations bring in writers to draft narratives before the program itself is fully developed.
Drafting a proposal and developing a program are different tasks, though. Even the most skilled grant writer can't produce baseline data no one has collected, staffing ratios no one has calculated, or program logic no one has modeled. When those pieces are missing, the writer has to cover gaps in the program with language, which leaves design questions unresolved until after the award.
Shift 5. From eloquent narratives to structured evidence.
Reviewers have always worked under tight constraints, scoring dozens of applications in short review cycles. In that setting, a well-written narrative offered some relief, and polished prose could occasionally carry an application whose program details were still incomplete.
Generative AI has changed that dynamic. Fluent, well-organized text is now easy to produce, so reviewers are likely to see more applications that read equally well. When every narrative sounds polished, writing quality stops distinguishing one proposal from another, and supporting materials (e.g., logic models, baseline data tables, staffing and evaluation plans) carry more weight.
Taken together, these five shifts change what makes a program fundable. Fundability now depends less on how compellingly an organization describes a problem and more on whether its program is designed to deliver, measure, and document results.
The Anatomy of a Truly Fundable Program: 8 Core Criteria.
When reviewers score your proposal, they're assessing whether your program can deliver what it promises. The eight criteria below reflect what they look for. Each one ends with three questions you can use to test your own program before drafting begins.
1. Local baseline data.
Regional statistics show that a need exists, but they say little about your organization's ability to address it. A countywide rate of chronic absenteeism or housing insecurity describes a broad problem, not the specific group your program will serve. Instead, reviewers look for local baseline data that define the population you're positioned to reach and the gap your program fills. Grounding your program in your own data, supplemented by local sources, allows reviewers to see how your program meets your beneficiaries' needs.
What do your baseline measures show about participants before services begin?
Where, specifically, do existing services miss this population?
Does your data show that your specific participant group is falling through those gaps?
2. Coordinated community resources.
A program proposed in isolation raises questions about duplication, partner relationships, and overhead. If another organization already addresses part of the need, positioning your program alongside it, rather than in competition with it, makes better use of both the funder's dollars and the community's existing services. Building local resources into your program design, with clear points of referral and handoff increases your chances of winning an award.
Which community resources are formally part of your service model?
At what point do beneficiaries move to or from partner providers?
How does your design avoid duplicating services or administrative costs?
3. Evidence-based design.
Funders invest in interventions with a reasonable likelihood of success, so they look for designs grounded in published research and tested models. A program with no grounding in prior evidence asks the funder to underwrite an untested experiment. Anchoring your intervention in research and peer models demonstrates how your design accounts for the barriers earlier implementations encountered.
Which published research or peer models support your approach?
If you're adapting an evidence-based model, what have you changed for your target population, and why?
How does your plan address barriers identified in earlier implementations?
4. Clear causal logic.
A calendar of activities isn't a program model. Reviewers look for clear links from inputs to activities, outputs, outcomes, and long-term impact, with explanations for each of those links. When the connection between an activity and an outcome is implicit, reviewers can't tell whether the program has a mechanism for producing the change it promises. Mapping the chains in your logic model and explicitly addressing the assumptions behind each link strengthens your proposal.
If you set the narrative aside, does your logic model still explain how the program produces change?
Have you described how each activity leads to its outcome, or have steps been skipped?
What unstated assumptions is your plan relying on?
5. Operational feasibility.
An ambitious reach target without a realistic staffing plan suggests that implementation will be harder than the proposal implies. Reviewers check staffing ratios, contact hours, and overhead to see whether your team can deliver the program at the proposed scale without exhausting staff partway through the performance period. Modeling staff capacity and administrative workload against projected direct service hours before you set your targets shows reviewers that you've thought through all program details.
Do staff hours cover projected participant contact time plus documentation?
Is the frontline workload sustainable for the full grant period?
Does your budget include the real cost of supervision, compliance, and record-keeping?
6. Sustainability plan.
Funders generally treat a grant as an investment in lasting change rather than indefinite support for a single program. They look for programs that leave something durable behind when the grant ends, whether that's trained staff, community partnerships, data systems, or a service model another funder can carry forward. Identifying the parts of your program designed to continue after award funds are spent strengthens your case for support.
What resources or capacity will remain in the community after the grant ends?
Which program elements will continue, and who will fund and run them?
What partnerships or systems will keep operating after the award period?
7. Measurable outcomes.
Broad goals such as "enhancing participant resilience" or "increasing community awareness" are hard to score because they don't explain exactly what success looks like or how anyone would know it happened. Reviewers look for outcomes defined with clear targets, timeframes, and measurement tools. Naming specific targets for short-, intermediate-, and long-term outcomes, along with the instrument that will measure each of them, improves funder confidence in your program.
Is each outcome defined by a target (a percentage or threshold) and a timeframe?
Which validated instruments or data sources will show whether you met each target?
Could someone outside your organization confirm your results without interpreting them?
8. Practical data collection.
Competitive proposals spell out who collects which data, with which tool, how often, and where the data are stored. That level of detail shows funders that results will be documented and reporting requirements can be met. It also shows that data collection has been planned around service delivery rather than added on top of it. Pairing clear evaluation protocols with collection tools that fit your staff's daily routines not only improves your likelihood of funding, but also reduces the risk of implementation challenges.
Does data collection pull staff away from direct services?
Is collection built into daily routines, or added as separate paperwork?
Do your methods protect participant privacy and trust while meeting reporting requirements?
Design first, then draft.
The shifts described above don't make storytelling obsolete. Instead, they change its job. A strong narrative now explains a program that is already designed, with its baseline data, logic model, staffing plan, and evaluation methods in place. When those pieces exist before drafting begins, your proposal can show reviewers exactly how the program will run, and your team starts the award with a plan it can follow.
In practice, that calls for three changes in how you approach your next funding pursuit.
Design before you draft. Establish baselines, map local resources, test the program logic, confirm staff capacity, and set data collection procedures before narrative drafting begins.
Let writers do what they do best. Grant writers are valuable partners for turning a complete program into a clear, compelling proposal, but they shouldn't have to invent the program under a deadline.
Give funders a plan they can evaluate. Show reviewers demonstrated capacity, clear logic, and a realistic plan for measuring results.
A compelling story can open the door, but a well-designed program is what funders say yes to.
Next steps.
1. Check where your program stands. The Program Fundability Self-Assessment scores your program on each of the eight criteria and shows you where to focus first.
2. Talk it through. If your results raise questions about your program design, I'd be glad to hear what you're working on.
A free 30-minute video call. No pitch.
Sources.
Buteau, Ellie, Satia Marotta, Hannah Martin, Naomi Orensten, and Kate Gehling. "New Attitudes, Old Practices: The Provision of Multiyear General Operating Support." Center for Effective Philanthropy, October 2020.
DiMaggio, Paul J., and Walter W. Powell. "The Iron Cage Revisited: Institutional Isomorphism and Collective Rationality in Organizational Fields." American Sociological Review 48, no. 2 (1983).
eCFR. "2 CFR Part 200: Uniform Administrative Requirements, Cost Principles, and Audit Requirements for Federal Awards." Office of Management and Budget.
Jenni, Karen, and George Loewenstein. "Explaining the Identified Victim Effect." Journal of Risk and Uncertainty 14 (1997).
Small, Deborah A., George Loewenstein, and Paul Slovic. "Sympathy and Callousness: The Impact of Deliberative Thought on Donations to Identifiable and Statistical Victims." Organizational Behavior and Human Decision Processes 102 (2007).
Västfjäll, Daniel, Paul Slovic, Marcus Mayorga, and Ellen Peters. "Compassion Fade: Affect and Charity Are Greatest for a Single Child in Need." PLOS ONE 9, no. 6 (2014).
