A nonprofit impact report is only as credible as the line from each number back to the people it counts. Most of the work is building that line once, so it does not have to be rebuilt every year.
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Impact reporting is how an organisation shows funders, trustees and the public what changed because of its work: outputs such as sessions delivered, and outcomes such as people housed, skills gained or wellbeing improved. AI helps by gathering that evidence from scattered records into one model and drafting the narrative, with every figure linked to where it came from.
The difficulty is rarely the writing. Outcomes sit in case notes written as prose, in survey exports with changing questions, in attendance sheets and in a finance system coded by budget line. Language models are good at reading the prose and mapping it to an agreed outcome framework, whether that is your own theory of change, IRIS+ or a funder’s template. They are not good at deciding what counts as an outcome, so that decision is made once, by your team, and written down.
case notes · surveys · attendance
against your outcome framework
per programme and period
every figure linked to source
before it reaches a funder
queued for a person to code
Impact reporting is the regular account an organisation gives of the difference its work makes: the activities delivered, the outcomes for the people or places served, and how it knows. For a charity it usually feeds funder reports, the annual review and the trustees’ report. Good impact reports explain the method behind each figure, not just the figure, and say honestly where evidence is thin.
The need you set out to address, what you delivered, the outcomes you can evidence and how you measured them, stories that illustrate rather than replace the data, what did not work, and what you will change. Funders increasingly look for the method and the limitations as much as the headline figures, so a report that traces each number to its source is stronger than a glossy one.
No. Measurement depends on choices only your organisation can make: which outcomes matter, what counts as evidence, what baseline you compare with. AI makes the collection and assembly far less laborious once those choices are made, by reading case notes and survey answers against your framework and drafting the report. A programme lead still checks the coding and signs off the figures.
What we built, what stage it is at, and a link to the live thing wherever there is one.
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Bring last year’s impact report and the records behind it. We will map where each figure came from.
30-minute technical call · no deck