Common Use Cases

Use this page after the first-run guide. It does not repeat every setup step; it shows which parts of ImpactAccounting.ai to use for common jobs.

Evaluate an organization or initiative

Use this when you need a decision-ready view of current societal value for due diligence, reporting, benchmarking, or impact strategy.

Explore scenarios and time projections

Use this when you want to understand how results change under future states or alternative assumptions.

Start from: Model → Scenarios

1. Open the completed project and go to Model.
2. Open the Scenarios panel.
3. Review the AI-generated scenarios if the Prediction stage has run. These scenarios are editable; they are not locked templates.
4. Add or edit rows for the variables or pathway factors you want to test, such as revenue, users, production volume, energy mix, attribution, baseline, or drop-off.
5. Add columns for time steps or alternative states, then enter absolute values or percentage changes.
6. Use the scenario chart to compare baseline and projected values. Click a scenario bar to show that state in the main valuation timeline.
7. Return to the pathway view to see which pathways explain the change.

Scenarios are useful for investment cases, sensitivity checks, pre/post intervention comparisons, and operational planning. Keep scenario names specific, such as "Base 2028 expansion" or "Low adoption case", so exported data remains easy to interpret.

Download a workspace for advanced work

Use this when you need analysis outside the web app: advanced visualization, custom reporting, portfolio aggregation, deeper QA, or work with Claude, Codex, notebooks, BI tools, or internal data pipelines.

Start from: workspace menu → Download workspace export

1. Open the workspace menu in the sidebar.
2. Select Download workspace export.
3. Unzip the file locally.
4. Start with README.md, then inspect projects.jsonl, materiality_summary.jsonl, pathways.jsonl, variables.jsonl, sources.jsonl, and the per-project workbook.xlsx files.
5. For scenario analysis, use scenario_results.jsonl and the per-scenario folders under each project.
6. For custom scripts, use the included schemas and helper tools as the contract for reading the data.

The export is designed for both humans and AI coding assistants. It includes machine-readable project summaries, pathway-level rows, source links, environmental KPI files, scenario outputs, JSON schemas, methodology notes, and recomputation helpers.

Validate results (beta)

Use this before treating a model as decision-grade.

Start from: Insights → Validation

1. Open Insights and read the data challenges. These identify the assumptions most likely to affect the result.
2. Open the Validation tab from the project navigation.
3. Review the queue by section: sources, activities, financials, variables, pathways, and model checks.
4. Mark items as confirmed when the evidence is acceptable. Flag items when they need correction, better evidence, or follow-up with the organization.
5. Open linked variables, citations, activities, or pathways from the validation rows to inspect details.
6. Use the pathway inspector edit mode or variable editor to correct values, formulas, sources, or assumptions.
7. Revisit Insights and scenarios after material changes so the narrative and decision view match the latest model.

Validation is currently a beta workflow. Treat it as a structured review queue, not as a guarantee that every assumption is correct. The strongest validation still comes from checking high-priority assumptions against primary company data, credible external sources, and expert judgment.

Which workflow should I use?