The question this answers
How do we structure our grant program so AI actually works?
What the problem looks like without AI-ready design specifications
You invest in AI tools for your grant program. The vendor promises efficiency gains, fraud detection, reporting insights.
Then you discover your application form collects free-text narratives that AI can’t meaningfully analyse. Your guidelines use undefined terms that mean different things to different applicants. Your acquittal reports ask for unstructured descriptions that can’t be aggregated.
The AI produces noise, not insight. Or worse, it produces confident-sounding outputs from garbage inputs. And because the training data reflects the biases in your existing data, the AI amplifies those biases rather than correcting them.
You promised efficiency. You got more work.
The problem isn’t the AI. The problem is your data was never designed for it.
What I deliver
A practical specification document that sets out how to structure:
- Guidelines: clear definitions, consistent terminology, explicit categories that AI can work with
- Application forms: structured fields, constrained responses, evidence prompts that elicit comparable data
- Progress reports and acquittals: standardised questions, quantifiable outcomes, reportable data
For each element, the specification shows:
- What you have now
- What AI needs to work effectively
- What changes are required
- How to avoid creating biased or low-quality training data
- How to make changes without breaking the applicant experience
It’s not about making your grant program serve the AI. It’s about designing data collection that produces usable insight and avoids amplifying bias.
What good looks like vs what bad looks like
Bad: A free-text question that asks “Describe the expected impact of your project.”
You’ll get 500 different interpretations. AI will summarise them. The summaries will be meaningless. You’ll have no comparable data. And if certain types of applicants write in certain ways, the AI will learn those patterns and reinforce them.
Good:
| Question type | Purpose | AI utility | Bias risk |
|---|---|---|---|
| “Select the primary outcome area your project addresses” (dropdown) | Categorisation | High – enables filtering, grouping, reporting | Low – constrained options |
| “How many people will directly benefit?” (number field) | Quantification | High – enables aggregation, benchmarking | Low – objective measure |
| “Describe how you will achieve this outcome” (text, 200 word limit) | Evidence | Medium – AI can summarise, but human assessment required | Medium – writing style may vary by applicant type |
| “Upload evidence of community support” (file upload) | Verification | Low – AI cannot assess, human review required | Low – evidence-based |
A well-designed form collects structured data where it matters, reserves free text for where judgment is genuinely needed, and avoids creating data that will train AI to replicate existing biases.
Why it matters
AI doesn’t fix bad data. It amplifies it. And it doesn’t correct bias. It learns it.
But there’s a more immediate problem: if your data isn’t structured, AI can’t take the mechanical work off the pile. Every AI output requires human review. Every summary needs checking. Every aggregation needs validating. You’ve added a tool without reducing work.
If you want AI to improve your grant program, you need to design your data collection so AI has something to work with, so the patterns it learns are the ones you want it to learn, and so it can handle mechanical tasks outright without creating a review burden.
This specification gives your team a clear brief for form redesign, guideline revision, and reporting templates. It also future-proofs your grant program: even if you’re not using AI today, you’ll have data that’s ready when you are, and that won’t create problems when you get there.
Other AI‑Augmented Grantmaking Deliverables
Where Can AI Be Used in Grant Programs? → An AI governance architecture that defines where AI strengthens decision quality and where it introduces risk. Every AI touchpoint has a defined purpose, boundary, and accountability structure. The program knows exactly what AI does, what it doesn’t, and who is responsible at each point.
Who decides what’s in an AI-assisted grant program? → A decision architecture mapping human and AI roles across the full lifecycle. Where AI handles mechanical work entirely, where it assists human judgement, and where human oversight is non-negotiable. Designed so AI removes work from the pile rather than adding a review layer.
Is your AI use in your grant program audit-ready? → An explainability and audit architecture built into every AI-influenced step. Every decision that AI touches can be reconstructed, explained, and defended under audit, FOI, parliamentary scrutiny, or public challenge. Accountability is designed in, not documented after the fact.
