Methodology
How Impact Intelligence Lab treats evidence
Practice methodology first: strength, limits, influence, and accountable judgement. CIIS is the infrastructure that helps keep that discipline visible when claims move.
This page describes how Impact Intelligence Lab works with evidence. It is not a CIIS feature list. Evaluation is something IIL does; it is not the whole of what IIL is. For the CIIS workflow step-by-step, see how it works.
Evidence has strength, context and limits
- Separate what evidence supports from what is inferred, suspected, or still unknown.
- Missing voices, disagreement, and conflicting findings stay visible; they are part of the result, not defects to hide.
- Contribution is not automatically causation. Social outcomes sit inside systems; do not claim ownership of change beyond what the evidence supports.
Influence is the bar for Lived Experience
- Did people merely provide information, or could they influence what it meant and what happened next?
- A survey response, focus group, or quote in a report is not proof that people shaped meaning or next steps.
- Preserve people's meaning, context and consent. Distinguish what someone said from our interpretation of it.
Evidence should lead somewhere
- Research, measurement and evaluation should improve understanding, decisions or action, not merely produce outputs.
- Ask what worked, for whom, under what circumstances, what did not, what changed unexpectedly, and what to examine next.
- Measurement should be proportionate to the question and decision at hand. More data is not automatically more intelligence.
Human judgement stays accountable
- Professional judgement stays with your team. CIIS makes the evidence and limits visible; it does not replace that judgement.
- Build capability, not dependency: engagements should leave organisations better able to understand and use their own evidence.
- IIL does not sell automated funding, clinical, or policy decisions.
How CIIS supports this as infrastructure
CIIS is governed impact-intelligence infrastructure used inside IIL engagements and restricted pilots. It helps keep sources, interpretation boundaries, shareability and human authority visible. Capability-by-capability live status is on current status.
Evidence model in CIIS
- Outcome statements link to measures, source evidence, review state, and explicit limitations.
- Shareability labels (ready to share, share with limits, should stay internal, not ready to share) describe what the evidence can support. They are an evidential ceiling, not CIIS approval to send or publish, and not a ranking of the program.
- Reporting lenses map interpretation for different audiences; they do not certify impact or compliance.
Data, AI and deployment (today)
- CSV-first bulk import for spreadsheet-style program data; document and manual-entry workflows are pilot-scoped.
- User-facing AI is not offered today. Humans remain accountable for what is shared.
- Your organisation owns the impact information it enters; exact subprocessors and retention are contract-specific.
- Live capability status is maintained on current status, not asserted here as a full product catalogue.
Naming note
Impact Intelligence Lab (the Lab, this site) is distinct from Impact Intelligence (impactintel.com), a separate consultancy. This page describes the Lab practice and CIIS only.
Discuss your evidence challenge
We'll work with you to see whether evaluation, synthesis, reporting support, or a CIIS pilot is the right next step — before you spend procurement time.
Discuss your evidence challenge