AI-Generated Impact Reports: Are They Reliable?

AI‑generated summaries are reliable when grounded in structured programme data and reviewed by humans.

• They save time and reveal patterns across portfolios.
• They still require human sense‑check and context.
• Plinth generates drafts that teams can refine.

Strengths and limitations

Use AI for speed and consistency; keep judgement with people.

• Strengths: rapid synthesis, consistent structure, trend detection.
• Limitations: nuance, attribution and context may need editing.
• Mitigation: show sources and provide editing workflows.

Key takeaway: AI is a first draft, not the final word.

Making outputs dependable

Quality depends on data hygiene and clear prompts/templates.

• Standardise outcomes and indicators across programmes.
• Reuse application data to reduce duplication and errors.
• Capture quotes and case studies to enrich summaries.

Key takeaway: better inputs produce better reports.

Communicating results

Summaries should be honest, proportionate and accessible.

• Highlight achievements and learning, not just numbers.
• Provide short executive summaries for boards.
• Share public‑facing stories where appropriate.

Key takeaway: concise stories build understanding and trust.

FAQs

Can AI fabricate data?

It can if misused. Plinth restricts generation to your stored evidence and shows sources.

Do auditors accept AI‑assisted reports?

Yes when evidence and decisions are traceable and reviewed by humans.

Will AI reduce grantee voice?

No. Use AI to synthesise, then include direct quotes and examples.